Mapping Poverty, Inequality and the New Middle Class ...€¦ · Mapping Poverty, Inequality and...

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Mapping Poverty, Inequality and the New Middle Class Progress in Brazil

(With special reference to Rio 2016 & 2014 World Cup cities)

Coordination: Marcelo Cortes Neri

7th October 2009 - Version 1.0

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The published articles are entirely the sole responsibility of its author. His opinions here

expressed do not reflect necessarily the viewpoint of the Getulio Vargas Foundation.

Mapping Poverty, Inequality and the New Middle Class Progress in Brazil (with special reference to Rio 2016 the 2014 World Cup cities) / Marcelo Côrtes Neri (Coord.). - Rio de Janeiro:

FGV/IBRE, CPS, 2009. [130] p.

1. Brazil 2. Inequality 3. Poverty 4. Social Mobility 5. New Middle Class 6. Rio de Janeiro 7. Olympic Games I. Neri, M.C

© Marcelo Neri 2009

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Centro de Políticas Sociais

Instituto Brasileiro de Economia

Fundação Getulio Vargas

Coordination:

Marcelo Côrtes Neri

Luisa Carvalhaes Coutinho de Melo

CPS staff:

Samanta dos Reis Sacramento

André Luiz Neri

Ana Beatriz Urbano Andari

Lucas Moreira

Ana Lucia Salomão Calçada

Art by: Marlus Pires

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Table of Contents

1. Preface

2. Introduction

3. Rio 2016: a shock of progress?

4. General Brazilian background

5. Income distribution

6. Reasons for change: inequality; mean income

7. Brazilian income

8. Social draw

9. Mapping Brazilian Social Progress (income-based measures)

10. Objective

11. The Geography of Poverty

12. Real poverty decrease

13. Local contribution to poverty decrease

14. Rise of class ABC

15. Emergence of class ABC

16. Inequality reduction

17. Source of income

18. Capitals of income

19. Class ABC – what happened since September 15th 2008?

20. Appendix I – Maps of Social Evolution

21. Appendix II – The Principal component analysis

22. Annex I – Rankings

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List of Tables

1) Economic classes

1.1) % Class ABC

1.2) % Class E

1.3) % Class D

1.4) % Class C

1.5) % Class AB

2) Household Per Capita Income

3) Inequality index

4) Sources of Household Per capita income (average income)

4.1 ) Income from work

4.2 ) Private transfers

4.3 ) Public transfers – Family grant

4.4 ) social security benefits up to 1 minimum wage 1 SM

4.5 ) social security benefits above 1 minimum wage 1 SM

5) Participation of different incomes in total income

5.1) Income from work

5.2 ) Private transfers

5.3) Public transfers – Family grant

5.4 ) social security benefits up to 1 minimum wage 1 SM

5.5 ) social security benefits above 1 minimum wage 1 SM

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Preface

The choice of Brazil and Rio de Janeiro, in particular, as host to the 2016 Olympic

Games is justified by different reasons mentioned in the speeches of the Brazilian

authorities in Copenhagen, namely: the opportunity given to the country and the South-

American continent in general to join Olympic nations, the fact that Brazil will host the

2014 World Cup works as an intermediate objective that will enable the achievement of

a greater challenge, that of organizing the Olympic games, and finally an perhaps more

importantly, the marked improvement in Brazilian social and economic indicators in the

last years, including the post-crisis period. More than the level of Brazilian

development, its emerging power status would be the reason of our bid’s success. In this

sense RIO 2016 is above all, a bet on Brazil future.

Looking into the future, the Olympic challenge would be as a relevant incentive in the

margin for Brazil and Rio de Janeiro city to focus on the achievement of tangible and

more permanent improvements - the Olympic legacy - in the quality of life of its

population. Similarly, the 2014 World Cup would place similar objectives to the

attainment of social indicators for each one of its respective hosting cities.

The results of various researches by the Center for Social Policies at the Getulio Vargas

Foundation (CPS/IBRE/FGV see www.fgv.br/cpc/fc) including results based on the

recent released 2008 PNAD microdata which shows that, between 2003 and 2008,

nearly 20 million people left their poverty status in Brazil and more than 30 million

people (equivalent to half of the French population) have risen to the new middle class

(ABC classes) in five years, of which 6,7 million last year alone. This was at mentioned

by President Lula in Copenhagen. In the current decade, the high income inequality that

characterizes the Brazilian case has suffered successive decreases. For instance, while

the 10% poorest Brazilians experienced real per capita increases of 72%, the 10%

richest grew 11% in the period.

The objective of this research is to describe the evolution of these income based social

statistics across Brazil including municipalities, metropolis, states and regions. Where

has Brazilian income grown more? That is the question. Was it in the Northeastern

hinterlands or in São Paulo periphery? And the New Middle Class in Campo

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Grande, has it already? What does explain the class changes in each place: growth

or income redistribution? Why has inequality fallen so little within the poor

Brazilian Northeast states while their mean income grew? Was it the impact of

minimum wage? And the geography of poverty, did it change? And if it did, why?

What is the capital of the Family Grant (Bolsa Familia)? And how about the

pensioners’? Who is the champion of income and jobs generation?

After providing the big Picture of Brazilian social indicators, the research explores

the whereabouts of the level and changes in social indicators based on household

per capita income. As we did last year, we have extended our national-level

research to its regional (regions and states) and local specificities. Opening data for

the capitals of the 27 states and the peripheries of the largest metropolises is an

innovation of this new research. It will enable an evaluation of the mayors’

performance until their mandate’s last year, as well as changes brought about

during the governors’ mid-term in each state, just as we did last year.

Initially, the present research focuses on the social and economic performance of the 27

capital cities in Brazil, including the 2016 Olympic city and the 12 cities of the 2014

World Cup. We analyze the performance of the social indicators among the four last

Olympic Games, More than a sports and social curiosity, there is an exact juxtaposition

of the Olympic cycles with the mayors’ administrations. We present a recent portrait of

the social indicators in these places for 2008, the last year of the mayors’

administration. The objective is to give transparency to the evolution of these indicators,

holding municipal authorities accountable for the previous social performance. The

similarity of the major sports competition in the world that the largest Brazilian cities

will host in the next decade, the competition for indicators among these capitals may

encourage the State and society to fight for a better society departing from tangible

indicators.

Besides local and national results, the research also analyzes international data, based on

microdata from 128 countries. The Brazilian specific features in relation to their future:

among 128 countries, we have the highest individual happiness perspectives (that is we

are number 1 ) but the perception of the country’s overall happiness in the future falls

behind (we are 43rd).

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Introduction

Macroeconomic analyses are aggregated in the very definition of the field of

study founded by J. M. Keynes amidst the great depression in the 1930s. That is,

looking to the group of people in a given society, not mattering their respective housing

region, economic class, income sources composition, or other individual attributes (sex,

age, etc.). It works as if it was a hybrid representative agent (half man, half woman)

middle-aged, belonging to the middle class possibly from Minas Gerais (as political

scientists say it, Minas Gerais summarizes the average Brazilian population’s diversity).

In various situations, macroeconomic fiction proves adequate so that we do not get lost

in unnecessary details, but in other situations, the devil lives exactly in the omitted

details. In particular, in a country of continental dimensions, with huge inequality and,

which becomes internationally known for the dissemination of old and new income

policies such as Brazil, the aggregate analysis hides more than it reveals.

Both in the period of expansion of the Brazilian income until September 2008,

and in the period afterwards, there lacks a clear vision about at least three points: i) Who

changed? Changes in the economy; who loses and who gains in terms of income classes

(E, D, C and AB). The new Brazilian middle class became a crucial macroeconomic

asset to compensate for the decrease in exports of our products as a result of the global

retraction. The demand injection now is the key point today, but we are looking at the

economy in a very aggregate fashion. To the extent that each segment has distinct

expenditure trends, there are macroeconomic implications depending on who advances

and who retreats in each segment. Without looking at the detail of the emerging and

plunging groups, be it in the discovery of market niches, be it in terms of widening the

social networks to losers. Ii) what has changed? Which income segment grew more or

less, before or as a result of the crisis and the actions against it: work, Family Grant,

pension or none of the above. This analysis of the close determinants of income will

help us discover the reasons for change. It is not only about knowing what generates the

new demand, but where the offer finds (or loses) this new (or old) demand, which leads

us to the last and maybe more important dimension in this study, iii) where has it

changed? In which regions, be they macro-regions, states, capitals, types of cities, did

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the domestic economy advance and where did it lapse? The present study addresses

these three types of questions, using the last spatial questions as an axis to orient the

society in the light of PNAD and PME microdata that were recently launched and see

the geography of recent changes in income.

Our questions:

A recently launched research by the Center for Social Policies at the Getulio

Vargas Foundation (CPS/IBRE/FGV) showed that 32 million people, or half of France,

rose to class ABC between 2003 and 2008, of which 6,7 million last year alone. Where

has Brazilian income grown more? That is the question. Was it in the Northeastern

hinterlands or in São Paulo’s periphery? And the new middle class in Campo Grande –

has it already proved its value? What explains class changes in each place: growth or

income distribution? Why has inequality fallen so little in some Brazilian states?

Impacts from minimum wage or a labor boom? And the geography of poverty, has it

changed? Why? What is the capital of Bolsa Familia? And the capital of pensioners?

What is the Champion of income and job generation?

The present research explores the “whereabouts” of the level and changes of

social indicators based on household per capita income. Just as we did last year, we

extended in this second stage, our traditional national-level research to the regional

(regions and states) and local contexts. Opening data for the capital cities of the 27

states and peripheries of the largest metropolises is an innovation of the present

research. It will enable us to assess the mayors’ performances until the last year of their

terms, just as changes brought about up until half the governors’ terms in each state, just

we did last year.

Besides drawing a map of the levels and variations of social indicators based on

income until the end of 2008 based on PNAD, we have identified the evolution from pre

to post-crisis periods of the income of different classes of Brazilian workers in the

largest Brazilian metropolises, opening data for respective capitals and peripheries (i.e.,

the group of cities that are not capital). And we found out that Brazilian peripheries,

much like peripheral countries, are thriving despite the crisis.

The research maps within the Brazilian territory the evolution of social

indicators based on household per capita income traditionally generated by CPS, such as

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poverty, inequality and economic classes (AB, C, D, and E) summing up what happened

to families from different economic classes in different locations.

We analyzed the impact of different income sources. For instance, what was the

relative importance of work earnings, social security benefits or Family Grant benefits

in explaining the sources of changes in poverty, inequality and class sizes in each

region, state, metropolis and capital city.

Your answers:

The research website: www.fgv.br/cps/atlas through interactive databanks will enable

users to carry out a cross-reference analysis of information according to their particular

interests in more detailed geographic levels than usual (e.g. regions, states, etc.) such as

identifying state capitals and large urban centers’ peripheries.

Where social indicators have grown more, was it in Rio de Janeiro recently elected the

host to the 2016 Olympic Games or Sao Paulo? What does explain such changes in each

place? And the geography of poverty, has it changed? If so, why? Who is the champion

in the level and in improvements in social indicators among capitals in Brazil? Without

extending this research further, it provides the ranking of the level and changes of social

indicators in the 27 capitals.

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Rio 2016: a shock of progress? “Rio2016 represents a bridge between the means and the end, from the “illegal, so

what?” to the “legal, what now?” overcoming obstacles between a shock of order and a

shock of progress”

Rio de Janeiro was a mistake. Let me, a true Carioca, try and explain. When the

Portuguese arrived on the Brazilian shore in January, supposedly, they mistook the

Guanabara Bay for a river estuary, and named it January River (Rio de Janeiro). Had

this mistake not occurred, the Baia de Janeiro (January Bay) would be here. The Baia de

Todos os Santos (All Saints Bay) inspired Bahia, the great rivers in the south and north

of Brazil inspired the names of Rio Grande states. Here, the initial lapse was

perpetuated in the name of the state, the capital city and Greater Rio, the metropolis, are

composing what we call here three Rios.

The mistake at the beginning of Rio’s history goes upstream: PDBG (Guanabara Bay

Clean-up Program), financed by the Japanese Development Bank (our former Olympic

rival). Besides money, there was society mobilization that gathered force in the Rio 92

Conference. Our research with the Trata Brasil Institute shows that the expansion of the

general sewerage system in Rio came to a halt. Baia Azul, Salvador’s equivalent to the

PDBG, financed by the IADB, doubled the access to basic sanitation infrastructure

between the 1998 and 2002 World Cups. If money and mobilization are needed, so is

good management.

In the case of Three Rios, bad management, apart from an being an internal problem of

their respective public authorities, is also present in their co-action. Octavio Amorim

argues that when successive mayors and governors in Rio aimed to run for the

presidency of Brazil, they stopped the flow of federal resources to the three Rios.

Alignment between the three levels of government, which was clear in Copenhagen, is

the exception – not the rule. The relationship between the State and society has also

taken a step back here. While the country lived, in the past ten years, a labor/business

formalization process, the Three Rios moved in the opposite direction, at least for the

first part of this period. Between 1997 and 2003, all indicators regarding the formal

status of small businesses, which characterize the local productive fabric, have fallen to

half, just as did the level of workers’ social security adhesion in general. In this period,

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the well-known local street-wisdom, the true local sport, had as its capital the

neighbourhood of Lapa, and as its symbol a typical loser, not so much Disney’s

character José Carioca. A hit with the public and the critics, O Globo newspaper’s series

entitled “Ilegal, so what?”, illustrates the Carioca pledge against chaos, which reached

the current governor and mayor and was implemented as actions called a “shock of

order” in slums, streets and illegal constructions. The term “shock of management”,

borrowed from the successful initiatives in the states of Minas Gerais and Espírito Santo

– success both for its administrative trademark and the good performance of social,

economic and electoral indicators, thus re-electing governors with 80% of local votes.

This showed that a tangible target-oriented management may lead to a sea of results.

The three Rios launched their respective shocks of order, moved on to shocks of

management, but we are still in their means, not their ends; we have met the necessary,

but not sufficient, conditions. There is where the success of Rio’s Olympic bid comes

in, representing a bridge to link the “illegal, so what?” to the “legal, what next?” steps;

to make this crossing between the two margins overcoming existing barriers between

shocks of order and management, on one side, and the shock of progress, on the other

side. We may now perhaps take advantage of Brasilia’s 50-year anniversary in 2010 to

leave behind any nostalgic feelings about Rio’s status as the former Capital of the

Republic, who remains attached to its past.

Our sailors Amyr Klink and Torben Grael are living examples of the capacity of three

Rio’s inhabitants to navigate towards new goals, as impossible as they may appear to

be. The election of Christ, The Redeemer in 2007, a 75-year old pied as one of the new

seven wonders, reflects this capacity. I was in Machu Picchu and I saw the Peruvian’s

surprise about it, after all it was not a competition about our irrefutable natural

endowments, but about human work. Peruvians may not know that our great human

work at stake is the cohesion of Cariocas with the rest of the country around the

presented objectives. Each Carnaval shows our renewed capacity to overcome

challenges.

Now, as many may be mistaken for a long time and be deluded by speeches about a

future as bright as unlikely, CPS launches a research to monitor social indicators of the

27 Brazilian capitals. Our methodological innovation lies in opening microdata for the

capital cities in PNAD, in order to compare mayors and their work. We begin by taking

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advantage of the coincidence between mayors’ terms and Olympic cycles, comparing

the performance of different mayors from the 1996 Atlanta Olympic Games to the 2008

Beijing Olympic Games. For instance, who was the champion in lifting people out of

poverty: Cesar Maia II (Rio´s mayor between Sidney 2000 and Athens 2004) or Cesar

Maia III? Where was the new middle class leap, quoted by Lula in Copenhagen, higher?

In the small Campo Grande or in the Greater Sao Paulo area? What has changed in these

cities? Why? Unemployment decrease, salary increase or none of the above? Obviously,

different contexts must be considered, so as looking at different relative speeds among

places, as in a healthy race for the best indicators in different periods of time. Beyond

2008 PNAD, we bring up data from the last twelve months when not only was the

economic crisis occurring, but new mayors were taking over. More than a fixed route,

the site WWW.fgv.br/cps/2016 is a navigation tool, allowing for a comparison of the

capitals’ performance, including the 12 hosting cities of the 2014 World Cup.

Winning the 2016 Olympics bid is just the start of the race, with hurdles, for the

tangible results which is just beginning, and as any competition, it should be watched by

the public. The Olympic Games, besides being of global interest, should produce a local

legacy of the people, by the people and for the people in Brazil in general and of three

Rios, in particular.

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General Brazilian Background

The French associate the blue, red and white of their flag to the ideals of their

revolution: freedom, equality and fraternity. What attributes would you choose to relate

to our green and yellow flag colors? I would say diversity and inequality. Inequality is a

Brazilian feature that has remained unscathed through the centuries. Yellow from the

extracted gold, initially by native Indians, then by Africans - the last in the Western

hemisphere to be freed. We live in a rich and unequal country. Our diversity is in Brazil

and in each Brazilian, having been compared to a boiling mixing pot of ethnicities,

creeds and behaviours. In our pseudo-democracy, everyone has the same colour, as I say

it, various shades of green1. Green is the colour of the diversity in the mixed races – it is

a secondary colour that results from the combination of yellow and blue, and would thus

capture the Brazilian diversity.

Diversity and inequality are our marks, which stop us from seeing the palette of

Brazilian colours. At each PNAD, Brazilian society has the opportunity to look at the

colours and faces as one stares at the mirror; to know the hindrances and advances of

the year that stays behind. PNAD data reflect the answers of people about themselves,

true self-portraits. PNAD’s expansion factors help to gauge the absolute size or the

relative position of each social group among themselves and in relation to Brazil,

keeping the original scale. Simultaneously to publicizing its rich collection of tables and

analyses, IBGE releases the research’s microdata with an annual sample of more than

380 thousand individual answers to a good questionnaire with over 100 questions asked

with exactly the same structure every year in the last two decades. Beyond its

transparency, the flexibility and precision of the large microdata sample help to portray

the relations between many facets of Brazilian life: school, work, pensions, etc.; girls,

blacks etc. For instance, as the school impacted on work or how work impacted on the

income of families, etc.

1 Only that in Brazil the darker shades of green usually live in slums and can only get through the rear entrance of buildings inhabited by lighter shades of green. In France, diversity is a concern of a different nature, it is not uncommon to meet French people who say “No! Vive la France, I want to remain apart, to keep my culture”. Brazilian diversity’s green is within each one and not stuck in groups or primary colours.

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Without entering virtually infinite possibilities to cross microdata, the question

that remains in each PNAD is what we can conclude from the tables. Researchers,

managers, journalists and ordinary citizens – all of us – are drowned in numbers!

The objective of this study is to build a brief profile of the Brazilian conditions

from a range of PNAD information. The literature on social well-being seeks to

synthesize the various aspects of reality for different people. The chapter on social

indicators based on income translates data on salary, journey, occupation,

unemployment, pensions, access to social programs, etc. into fewer numbers, each one

with the capacity to portray a peculiar aspect of life in society, such as well-being level,

inequality, poverty rate, economic class composition. A first effort is to condense

information in order to transform it into practical knowledge about how much the

Brazilians’ wealth has grown or decreased. We recognize that the exercise is a

simplification of reality whereby the richness of information and PNAD’s colourful

possibilities become black and white paintings. Roughly, it is worse than attaching

values to certain artworks, as here we are talking about the lives of people – in the case,

Brazilians.

Evolution Panorama: Social Measures based on Per Capita income

Given its national and annual nature, PNAD enables the monitoring of the evolution of various social indicators based on income. The release of PNAD data is the moment when society faces a sort of mirror, sees its face and assess its own advances and obstacles. The panorama available in the research website presents a time evolution of different indicators such as poverty, economic classes, income, inequality and education (among other indicators), since the beginning of the 1990s. There follows the group of variables available for analysis.

Each one of these indicators may be analyzed for the overall population or by open sub-groups: i) sociodemographic features such as sex, age, years of schooling, race, position in the household; ii) characteristics of the producer, including job position, social security tax, education and access to digital assets; iii) characteristics of the consumer such as access to consumer goods and services; iv) spatial features such as housing, area (metropolitan, non-metropolitan urban and rural), states and, innovatively, capitals and peripheries:

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http://www.fgv.br/ibrecps/RET4/CPC_evolucao_temporal/index-eng.htm

Income distribution

We present next the accumulated income gain per decile of population, starting

with the poorest groups between 2001 and 2008. The growth rate decreases as we move

from the first (72,2%) to the last decile (11,1%). Although the growth recovery period

only began after the end of the 2003 recession, inequality had fallen beforehand.

72,45%

55,50%50,76%

45,39%40,52% 37,46%

31,65%25,74%

19,18%11,37%

10 20 30 40 50 60 70 80 90 100

Accumulated Variation of Average Income by tenth of income - Brazil (2008/2001)

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The redistributive movement in terms of the Gini index is translated in the graph

below. Gini is the most popular inequality measure applied to the most disseminated

concept of well-being in the literature, which is household per capita income, including

null incomes.

Gini Index

Source: CPS/FGV based on PNAD/IBGE microdata

Gini-measured inequality drops to 0,5486 in 2008, a decrease of -1,15%

reaching a similar speed to that observed in the decade of inequality reduction:

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Poverty, Growth and Inequality Scenarios

The proportion of poor people in Brazil, according to our estimates on the 2008

poverty line is 16,02%. Initially, in a neutral scenario in terms of distribution, if national

per capita income grows 2,6% per year in the next seven years, which would correspond

to 4% a year of total income growth for the overall population growth, poverty would

drop to 12,43%, a decrease of 22,38%. This reduction would be even higher if the

mentioned accumulated expansion of 20% was combined with a decrease in the Gini

index, as it was similarly observed in the last seven years. This would equate the income

distribution of Rio Grande do Sul in 2007. In this case, poverty would drop 41,22%,

leading to a poverty rate below the two-digit mark: 9,45% of the Brazilian population

would be considered extremely poor. However, even with a null per capita income

growth, if inequality fell to Rio Grande do Sul levels, gains would still be reasonable –

for the poor, of course – poverty rate would drop 18,58% in the period, reaching

13,04% of the population. This decrease in poverty is enough to overcome the pace of

the first Millennium Development Goal of 50% decrease in 25 years, dropping 52% in

this period. This note illustrates the prospective and retrospective role played by the

reduction in income inequality within the Brazilian context.

Reasons for Change: Inequality

How do we reduce inequality? Once more, the present decade can show us the

way by applying the decomposition methodology of the Gini variation2 to the period

2001-2008. Income from work explains 66,86% of the inequality reduction, then come

the social programs, in particular the Family Grant and its precedent School Grant,

which explain 17% of the inequality reduction, while the social security benefits explain

15,72% of the income de-concentration; remaining incomes account for less than 1%.

It is desirable that the analysis considers not only the impacts of different income

sources, particularly transfers from the Brazilian state, on inequality displacements, and

the public resources as well.

2 Hoffman 2006 and Soares 2006 apply this methodology to Brazilian data at the beginning of the decade. Kakwani, Neri and Son 2005 and Barros et all. 2006 apply other methodologies to the same data.

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Reasons for Change: Mean Income

If something changed, the second struggle is to find out: why has it changed?

How has it changed? These last questions suggest the two complementary lines of

answers explored here, knowingly: the first one looks at the approximate determinants

of the income distribution and the primary components of people’s income, the role of

pensions, social programs and work (and its components) in the various synthetic

measures.

Decomposition of Income into different sources PNAD

Year Income from all sources

Income from all

jobs

Other private incomes

Public Transfers -

BF*

Social Security

minimum - MW *

Social Security above > MW *

2008 – R$ 592,12 450,29 12,86 12,73 28,05 88,2 2008 – %

Composition 100% 76,05% 2,17% 2,15% 4,74% 14,90%

Annual growth rate

2003-08 5,26% 5,13% 2,62% 20,99% 6,64% 4,44%

Growth 2007-08

5,49% 4,5% 15,13% 30,83% 1,63% 7,68%

Source: CPS/FGV based on PNAD/ IBGE microdata.

Between 2003 and 2008, the average per capita income of Brazilians increased

5,26% in real terms (i.e., population growth and inflation have been discounted) going

from 458 to 592 reais per month. Income sources that increased more were social

programs (20,99%) influenced by the expansion of the Family Grant, created in 2003.

Next, came the share of pension income attached to the minimum wage (6,64%). The

effects of minimum wage readjustments, as it increased more than 45% in the period,

put a pressure on the basic value of benefits and the number of elderly, as a result of the

population’s aging process. Income from social security above the minimum amount

grows less than the general income growth. It is worth pointing out that income from

work has had an average increase of 5,13% per year, which grants a sustainable support

for the living conditions beyond the official income transfers. Income from work

corresponds to 76% of the average income perceived by the Brazilians and 75% of the

income gain observed has come from there.

In the last year, the growth of per capita income from work and pensions bound

with the minimum wage is a little lower, and social programs reach 30,8%. In any case,

in both periods – although there has been a strong increase in income from social

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programs and pensions tied to the minimum wage – the share of income from work

remains close to the impressive growth in income in this phase of boom.

Brazilian Income

The main feature of the approach used here is its level of disaggregation into

four groups of income.

Class AB: according to the last PNAD, the share of people in class AB (household

income above 4807 reais) grew 7% in the last year, which corresponds to 1.5 million

entering the upper income group. In the last 5 years, 6 million people have ascended to

this class that, in 2008, reaches 19,4 million people.

Class C: The same class that reached 37,56% of the Brazilian population in 2003,

reached 49,22% in 2008, or 91 million people with an income between 1115 and 4807

reais monthly, the dominating class in terms of population size. This accumulated

growth of 31% in 5 years means in population terms 25,9 million Brazilian people who

had not been and became class C in the last 5 years (5,3 million last year alone).

Class D: the proportion of people in class D is 24, 35% in 2008 reaching 45,3 million

Brazilians with an income between 768 reais up to the lower limits of class C. In terms

of trends, there has been a reduction of 0,9 million in one year or 3%, and 1.5 million if

we consider the last five years.

Class E: With a reduction of 12,27% in the last year, or the exit of 3,8 million people

out of the lowest income group up to 768 reais monthly, the poverty level in our

methodology. This movement corroborates a trend since the end of the 2003 recession,

when poverty fell 43%, that is, around 19,4 million people have crossed the poverty

line. As a result, 29,9 million poor people (16,02% of the population3) who would be

instead 50 million had poverty not fallen in the last years.

3 With an income below 137 reais monthly (greater São Paulo area prices or 145 reais at national average prices pondered by the population of each class).

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Composition of Income per Economic class

In the period from 2003 to 2008, we noticed that the share of income associated

with social programs, such as the Family Grant, doubled. This corresponds to the poor

groups by the national average line of CPS – after the increases announced by the

government and the new entry criterion for the Family Grant, the share of these

programs in the respective incomes increased from 4,9% to 16,3%.

The analysis of the participation of different income types per economic class

may be useful to assess the prospective impacts of different public policy tools on

income distribution, such as for example the measures adopted with the external crisis

context in September 2008, namely:

Increases in the Family Grant and other programs not related to the social

security tend to benefit predominantly class E that has 16,25% of its earnings from this

type of income.

It is interesting to separate income from social security benefits as individual

earnings up to one minimum wage and benefits above this minimum, because

distinguishing among such increases was stressed in 1998. The major beneficiary of the

increase in the social security minimum (basic) benefit is class D, with 12,66% of

income tied to it. Finally, the increase in pensions above this minimum value benefits

above all class AB as 18,94% of its earnings derive from this source. This measure is

being discussed today.


22

Social Draw

Nine months after the crisis started, there is an already clearer vision about its

effects in the Brazilian people’s income in the six major metropolitan areas in the

country. Income inequality underwent a serious deterioration in January, when part of

last years’ gains were lost, but it has now come back closer to its pre-crisis levels. Even

class AB, that earns more than 4800 reais per month and who had lost more with the

crisis (2,7% in January alone), is today only 0,5% below one year ago’s levels (14,97%

of the population is in class AB with 55% of the country’s income). Class C is already

in a positive situation with a 2,5% gain in 12 months (53,2% it is the dominating class

in terms of population size). If this draw may be considered a good result in times of

crisis, on the other hand, it hides a sudden halt of the previous improvements in the

indicators: from July 2003 to July 2008, class AB grew 35,7%, class C increased 23,1%

and income inequality dropped as it had never dropped before in the Brazilian statistical

series. Looking on the bright side, 2008 PNAD must provide, despite the current crisis,

a more or less faithful portrait of the living conditions in 2009.

Variation of Economic Classes Pre versus Post-Crisis

23,1%

-15,5%

-37,0%

-0,5%

2,5%

-4,1% -3,3%

Classe AB Classe C Classe D Classe E

jul03 a jul08 jul08 a jul09

Source: CPS/FGV based on PME/IBGE microdata

23

Strategy to widen the scope of economic classes

Our strategy is, at each update of our traditional series based on household per

capita income such as poverty, inequality, social welfare and now income classes, to

include a new dimension to the analysis of the various economic classes:

entrepreneurship (www.fgv.br/cps/crediamigo2), microcredit

(www.fgv.br/cps/crediamigo3), micro-insurance (www.fgv.br/cps/ms).

(www.fgv.br/cps/crediamigo2), exploring in each research a new perspective. In the

current research we will explore a multidimensional view from the rich data offered by

PNAD. Still exploring the rich microdata of the PNAD/IBGE we applied a model of

sequential variable selection according to the level of statistical significance related to

household per capita income, always based on the household/family as the basic unit.

We look to the producer and consumer. In the case of the consumer, a range of

information on access to consumer goods, housing and public services are provided by

PNAD. In the case of the producer, the focus is on the inclusion in the labor market that

reflects human physical and social capitals, not only the education level of the

household’s person of reference and his/her spouse, but also the investment on the

future of their kids open by age groups and types of school.

What are the main stocks associated with income flows?

i. Technical aspects

We present initially in this subsection a discussion on the the series of estimating

models Apresentamos inicialmente nesta subseção uma discussão sobre uma série de

modelos de estimação dos determinantes das classes econômicas.

Multivariate Analysis – Methodology The bivariate analysis captures the role played by each attribute considered

isolatedly in the demand for insurance. That is, we desconsider possible and probable interrelations of the explanatory variables. For example, in the calculation of insurance by state within the Federation, we don’t consider the fact that Sao Paulo is a richer place than most states, thus should have greater access to insurance. The multivariate analysis used further ahead seeks to consider these interrelations through a regression of the many explanatory variables taken together.

Aiming to provide a better controlled experiment than the bivariate analysis, the objective is to capture the pattern of partial correlations between the variables, interest and explanatory. In other words, we have captured the relations between the two variables, keeping the remaining variables constant. This analysis is very useful to identify the repressed or potential demand as we compared them, for instance, which are

24

the chances of a person with more education having higher income, if he/she has the same characteristics as the comparison group.

Choice Models for Explanatory Variables

We being exploring the wide range of information relating to the possession and

use of assets based on PNAD, using a model of variable selection according to the level

of statistical significance to explain the household per capita income. It is worth noting

that both in the field of traditional social indicators (i.e. poverty and welfare4) just as in

the definition of economic classes (i.e. E, D, C and AB) the family/househodl is the

basic unit of analysis under the hypothesis of its members’ solidarity who, on the whole,

share the earnings much like the “all for one and one for all” of Dumas’ Three

Musketeers.

Another point is the use of income as a unit of reference to integrate different

information on access and use of productive and consumption assets. In our view,

whether people like it or not, income is the most used variable in economics and, if we

want to increase the dimension of the analysis, it is interesting to use what has already

been done in practice. Here it is important to note that we speak of the sum of different

income sources reported by people to PNAD and not the aggregate vision of production

implied in the GDP5.

Afterwards, based on the selection of variables, we tracked variables referring to

the producer and consumer available on PNAD. The exercise is part of a learning

process to decide what matters in the definition of classes and how much each of the

estimated components are worth. In order to determine which of them have higher

explanatory power and which will be more relevant, by applying a variables’ sequenced

choice procedure that uses a mincerian income equation model.

The list of selected variables for each model (from a test F) is provided next, in

increasing order of importance, in a self-explanatory list of 31 groups of variables; the

eliminated variables are not reported in the table:

ORDEM DE ENTRADA NO MODELO 1 Number of per capita toilettes

4 Welfrae as inequality measure derived from the social welfare measure used. 5 the Stiglitz-Sem report, launched on sept 15 de setembro, defends the use of information from household surveys and less information based on GDP (per capita PPP) that prevails in the analysis. This is described in the introduction.

25

2 Telephone

3 Spouse’s education

4 Type of family

5 Head pays social security tax

6 Washing machine

7 Number of per capita bedrooms

8 Head’s education

9 Spouse’s job status

10 Children in school(7to 14)

11 Children in school (0 to 6 y.o.)

12 Head’s job status

13 Computer

14 Fridge

15 Children in school (15 to 17)

16 Type of household (owned, financed, rent)

17 Head member of union

18 Freezer

19 Per capita number of rooms

20 Sewerage system

21 Radio

22 Number of toilettes

23 Number of members

24 Television

25 Waste collection

26 Age when head began to work

27 Number of rooms

28 Income from work participation

29 Number of bedrooms

26

Initially, it is worth noting that we purposefully omitted sociodemographic and

spatial variables from the explanation of per capita income so that we could infer

afterwards which is the equivalent income of people with different features. We should

also mention that the variable number of toilettes, followed by access to mobile

telephones well before completed years of schooling of the reference person comes in

8th place (3rd place in the case of the spouse education) that typically has the highest

explanatory power in empirical researches on income inequality and poverty.

Obviously, we are not attempting to establish a causal relation between different

variables of stock and income flow, even more because this is a two-way relation. In our

interpretation, we will identify variable that are more dependent on income that income-

generating. The exercise helps to gauge the structure of the model that assign equivalent

income and its counterparts in terms of consumption potential and income-generating

capacity. The table is self-explanatory.

27

Income simulator: Total, consumer and producer Tool used to simulate total income of the population through a combination of

individual attributes tied to the consumer and producer. For that, you must select

characteristics in the form below and click on simulate.

The graphs show the total household income, in the following order:

- Total: expenditure potential and income generation

- Consumer perspective: expenditure potential

- Producer’s perspective: income generation

One of the bars represents the current scenario, according to the selected attributes;

the other is the previous scenario as simulated before.

http://www.fgv.br/ibrecps/cpc/SIM_PNAD_0208_RENDATOT/renda.htm

28

In the table above, we are not considering the magnitude of each category’s

coefficient, but the power of the categories taken together to explain income. Wen

looking at the magnitude of extreme coefficients in each variable, the equivalent income

of a person who lives in a household with one bathroom for each person, if we double it

(two bathrooms for four people instead of one bathroom) the income increases 27,5%

in relation to the previous scenario; while a person with a fixed landline and a mobile at

home, ceteris paribus, and a person with none of these communication technologies at

home, it is 38,5% higher. The spouse variable is more significant than the reference

person, as it refers not only to the impact of education, but to the composition of family

income which is more or less diversified by virtue of potential income earners. The

same does not happen to the education variable of the reference person, because each

household has at least one reference person. A household with a spouse with at least

incomplete university degree (12 or more years of schooling) has a 28% higher income

than a spouse with an ignored educational level (regression basis, zero coefficient),

which in turn has an income 14% higher than those without spouse.

Following the order of statistical relevance of the variable selection model, we

have a variable on the type of family composition where a family consisting of a couple

with all children under 14 has a per capita income around 30% lower than a family with

a couple and no kids. The fifth variable with higher predictive power is that which

captures the nature of the public or private social insurance of the household head or

none of the above, that is, inactive or unemployed household heads, where the reference

person pays both types of insurance with a family/household per capita income around

30% higher than those inactive or unemployed. The remaining variables may be

analyzed through the same prism or through the simulator described in the box further

down.

In the annex, we present a conceptual explanation and a practical application of

a nother methodology to define class based on principal components, and not based on

people’s income. In essence, the principal component analysis reduces the variables for

a group of artificial variables, which is accomplished through turning redundant

variables into new variables that may be used in subsequent analysis as forecasting

variables in a multiple regression – or in another type of regression. Technically, a

principal component may be identified as a linear combination of optimally weighed

variables.

29

The first component extracted from a principal component analysis would be

responsible for a maximum amount of total variance of the observable variables. The

second component extracted would account for a maximum variance of the group of

data that does not derive from the first component. In ideal conditions, this means that

the second component would be correlated with some of the observable variables that

do not show strong relations with the first component.

The final practical result would have three components: the first and most

significant could be interpreted as the consumer’s side – consumption of consumer

goods’ variables (washing machine, fridge and freezer), and the absolute size of

household captured by the number of rooms and toilettes. The vector captures two items

that we also linked to the production as education of the household head and his/her

spouse and the possession of goods tied to ICT such as fixed landline and mobile

phones and computer connected to the internet. The second vector with more relevance

may be defined as that consisting of some variables of quality on the household’s

housing quality captured based on the per capita number of room, bedrooms and

toilettes, the number of members in the household and the presence of teenagers as well

as variables of education quality of the children between 7 to 14 years old and 15 to 17

years old, as well as the type of family structure (family headed by a mother with

children of up to 14 years old). Finally, the third factor may be interpreted as effective

generation of income consisting of the participation of income from work in the total

income, the type of social security/insurance (public and private, etc.) and job status

(private employee, unemployed self-employed, etc) of the household head.

Marginal contribution of the stocks to the inequality of flows

We explored then the contribution of each variable of stock on the variance ofthe

household per capita income inequality. We calculated the marginal contribution of

each variable on the total R2 of the regression taking them one by one out of the

complete regression and calculating the relative difference such as its contribution to the

margin for the income inequality:

30

Marginal Contribution of Income Inequality

Without the respective variable

R2 dif R

2 dif R2/R

2 orig%

All variables (R2 original) 0,6924 -

Telephone 0,6813 0,0111 1,60

Spouse’s job position 0,6825 0,0099 1,43

Children’s school attendance(7toa 14 years old) 0,6860 0,0064 0,92

Washing machine 0,6868 0,0056 0,81

Education of the head of the household 0,6870 0,0054 0,78

Type of family 0,6871 0,0053 0,77

Head’s job position 0,6874 0,0050 0,72

Computer 0,6876 0,0048 0,69


Type of household (owned, financed and rent) 0,6888 0,0036 0,52


Fridge 0,6892 0,0032 0,46

freezer 0,6896 0,0028 0,40

Spouse’s education 0,6897 0,0027 0,39

Head pays social security tax 0,6898 0,0026 0,38

Head belongs to union 0,6916 0,0008 0,12

per capita number of toilettes 0,6919 0,0005 0,07

per capita number of bedrooms 0,6920 0,0004 0,06

per capita number of rooms 0,6921 0,0003 0,04

Sewerage system 0,6921 0,0003 0,04

radio 0,6921 0,0003 0,04

Number of members 0,6922 0,0002 0,03

Television set 0,6922 0,0002 0,03

Age when head started working 0,6923 0,0001 0,01

number of rooms 0,6923 0,0001 0,01

number of toilettes 0,6923 0,0001 0,01

Waste collection 0,6923 0,0001 0,01

number of bedrooms 0,6924 0,0000 0,00

Participation of work income 0,6924 0,0000 0,00

31

Mincerian Equation The mincerian equation of salary is the basis of a vast empirical literature of labour

economics. The salary model by Jacob Mincer (1974) is the framework used to estimate the returns on education, among other variables that determine the salary. Mincer conceived an equation for earnings that would be dependent on explanatory factors related to the academic level and experience, besides possibly other attributes, like sex, for instance

This equation is the basis of the labor economics particularly in what concerns the effects of education. Its estimate has already encouraged hundreds of studies, that tried to include different educational costs, such as taxes, fees, opportunity costs, learning material, just as the uncertainty and expectation of agents present in the decisions, the technological progress, non-linearity in school, etc. identifying the costs of education and work earnings enable a calculation of the internal rate of return on education, which is the discount rate that must be compared to the market’s interest rates to determine the optimal quantity of investment in human capital. The MIncer equation is also used to analyze the relation between growth and educational level in a given society, besides inequality determinants.

The typical econometric model of regression of the mincerian equation is:

ln w = β0 + β1 educ + β2 exp + β3 exp² + γ′ x + є where

w is the salary earned by the individual; educ is its educational level, measured by years of schooling; exp is its experience, whose Proxy is the individual’s age. x is a vector of the observable characteristics of the individual, such as race, gender

and region; є is a stochastic mistake

. This is a model of regression in the log-level format, that is, the dependent variable

- salary – is in a logarhytm format and the independent variable, more relevant – education – is on level. Therefore, the β1 coefficient measures how much an extra year of scholing causes a proportional variation in the individual’s salary. For instance, if β1 is estimated in 0,18, this means that each extra year of study will be related on average with na increase in salary of 18%.

Mathematically, we have: Deriving , we found that ( ∂ ln w / ∂ educ ) = β1 On the other hand, by virtue of chain, we have:

(∂ ln w / ∂ educ) = (∂ w / ∂ educ) (1 / w) = (∂ w / ∂ educ) / w) Logo, β1 = (∂ w / ∂ educ) / w, corresponding thus to the percentage variation of the salary from each unit increase per year of study.

32

Decomposition of Inequality

How do we reduce inequality? Once more, the present decade may show us the

way, by applying to the period 2001-2008 the methodology of decomposition of the gini

variations. According to the last column of the table below shows, the work income

explains 66,86% of the inequality reduction expected between 2001 and 2008, next

come the social programs with emphasis on the Family Grant and its predecessor

School Grant, that explain 17% of the inequality reduction while social security benefits

explain 15,72% of the deconcentration of income, and the remaining income account for

a residue under 1%. The remaining columns of the tables compare 2008 to the other

years. The following tables decompose the nature of the effects per type of income

separating the effects of the contribution of each source to the total income, by the

change in the relative mass of benefits from the effects of inequality of each source,

based on the Total Income Gini.

Percentage effect of each share of earnings in the change of the GIni index on the distribution of the household per capita income in Brazil

Percentage effect

Share 2007 a 2008

2006 a 2008

2005 a 2008

2004 a 2008

2003 a 2008

2002 a 2008

2001 a 2008

Work earnings 116,15 89,30 64,07 65,84 66,39 66,86 66,86 Social security -1,06 21,67 25,49 27,68 17,56 13,80 15,72 Family Grant -1,03 -10,01 11,43 6,99 16,83 18,47 17,00 Private transfer -14,21 -0,82 -0,92 -0,40 -0,71 0,93 0,50 Bonus 0,23 -0,11 -0,04 -0,08 -0,07 -0,04 -0,05 Total 100,07 100,03 100,03 100,04 100,00 100,02 100,02 Delta GINI -0,0064 -0,0137 -0,0196 -0,0225 -0,0344 -0,0400 -0,0471

Composition effect

Share 2007 a 2008

2006 a 2008

2005 a 2008

2004 a 2008

2003 a 2008

2002 a 2008

2001 a 2008

Work earnings 0,41 -0,06 0,00 0,00 0,00 -0,02 -0,04 Social security -0,20 -0,06 0,18 0,07 0,10 -0,18 -0,21 Family Grant 26,45 -0,72 6,62 9,44 9,29 4,71 5,28 Private transfer -4,10 3,05 2,58 1,95 1,16 1,75 1,32 Bonus 0,10 -0,10 -0,09 -0,05 0,06 -0,06 0,01 Total 22,65 2,11 9,29 11,42 10,61 6,21 6,36 Delta GINI

Efeito concentração

Share 2007 a 2008

2006 a 2008

2005 a 2008

2004 a 2008

2003 a 2008

2002 a 2008

2001 a 2008

Work earnings 115,74 89,36 64,07 65,84 66,39 66,88 66,90 Social security -0,85 21,73 25,31 27,61 17,46 13,98 15,93 Family Grant -27,48 -9,30 4,81 -2,45 7,54 13,75 11,72 Private transfer -10,12 -3,87 -3,50 -2,35 -1,87 -0,82 -0,82 Bonus 0,13 0,00 0,05 -0,03 -0,13 0,01 -0,07 Total 77,42 97,92 90,74 88,62 89,39 93,81 93,66 Delta GINI

33

It is interesting that the analysis considers not only the impacts of different

income sources, particularly the transfers from the Brazilian government, on the

inequality movements, but also its costs to the public accounts.

Costs of Poverty Eradication.

Another useful measure in the design of public policy is the income gap (P1).

That is, how much income lacks, on average, to the poor so that they can meet their

basic needs in the market. Using as base our income insufficiency line, the average

deficit expressed in monetary terms of each poor Brazilian would be R$ 56,29 monthly

at average prices in Brazil. In 2007, the same statistics was R$ 57,44.Captured by the

decrease in the index known as P2 (falls from 5,08 to 4,33) we observe a relief in the

seriousness of poverty (in a counterpoint to what occurred last year). Data on income

increase in the first decile and the decrease in the proportion of people with very low

income measured in the poverty line indicate that the poorest of the poor gained in the

period.

Going back to the calculation of the eradication costs in 2008, as just part of the

Brazilian population is below the line, data shows that it would be necessary R$ 9,01 on

average, per person versus R$ 9,65 in 2007), to relief poverty totally in Brazil, a total

cost of R$ 1,7 billion reais per month and R$ 20,2 per year. Information reveals how

much it would cost to complete the income of each Brazilian up to the line of R$ 132

nationally or 145 reais at SP prices, that is, the lowest value of sufficient transfers to lift

each poor person to the floor of their basic needs. This exercise should not be read as a

defense of specific policies, but as a reference to the social opportunity cost of adopting

unfocused policies. Data is useful to indicate the target of policies and organize their

sources of finance.

Cost of Poverty Eradication

Minimum transfers to eradicate poverty

34

R$ person R$ total month R$ total year R$ non

poor R$ poor

Brazil 2008 9,01 1.680.719.363 20.168.632.359 10,73 56,29

Cost of Poverty Eradication

Wealth transfers per non-poor person

0.5 % a.m. 1 % a.m. 2 % a.m.

Brazil 2008 2147 1073 537

Lorenz Curve - Brazil 2008

Household per capita income inequality


Participation in Total Income 2008 – Brazil

35

Level by income groups 2008 – Brazil


36

II Mapping Brazilian Social Progress (income based measures)

Objective

The objective of this study is to provide a short account of the different income

earned by Brazilians, seeking to sum up the various aspects of different people’s reality.

The chapter of indicators based on income in the social welfare literature translated the

data of salary, working day, occupation, unemployment, pensions and benefits, access

to social programs, etc. into few numbers, each with the capacity to depict a peculiar

aspect of life in society, such as welfare level, inequality, poverty rates, and economic

class composition. A first effort is to condense information to turn it into practical

knowledge, such as how much income in Brazil has grown or diminished in different

places. The second effort is, once measurement is defined for the whole, to tread the

inverse way opening per capita income for large income types in order to understand the

close determinants of poverty. In all cases, the core of the research will focus on the

spatial opening of income information.

The Geography of Poverty

Between 2003 and 2008, there was a 43,03% reduction in poverty,

corresponding to 19,3 million people leaving poverty defined as income below 137 reais

in household per capita income. Illustratively, we will initially work with a more local

opening. Among the 27 capitals of the Brazilian states and the peripheries of the six

major metropolises, between 2003 and 2008, the city of Palmas stands out as the highest

rate of poverty reduction (-80,9%) and, among the lowest reduction rates are the city of

Rio de Janeiro (-34,8%) and Recife´s periphery (-36,4%). In terms of the series’ levels

in 2008, the lowest poverty rates are found in Florianópolis (2,36% of the population)

and Curitiba (3,92% and the highest are in Maceio (25,6%) and once more Recife’s

periphery (26,4%). In the table below we present the five top and the worst five cities in

terms of poverty rates in 2008, as well as their position in previous years and in

variation rankings. The complete rankings with all 36 places may be found in annex 1.2

37

% Classe E% % % Var (%) Var (%)

rank 2008 2008 rank 2007 rank 2003 rank 2007/2008 rank 2003/2008

1 Periferia de Recife - PE 26.38 2 26.75 5 41.47 10 -1.38% 2 -36.39%2 Maceió - AL 25.60 7 21.46 4 41.70 5 19.29% 6 -38.61%3 Periferia de Salvador - BA 25.22 5 22.01 1 47.69 6 14.58% 12 -47.12%4 Periferia de Fortaleza - CE 24.63 1 27.07 2 46.69 17 -9.01% 13 -47.25%5 Recife - PE 20.75 3 22.60 6 35.85 15 -8.19% 7 -42.12%

1 Florianópolis - SC 2.36 36 1.68 36 6.49 3 40.48% 33 -63.64%2 Curitiba - PR 3.92 34 3.20 35 10.50 4 22.50% 31 -62.67%3 Goiânia - GO 4.50 32 6.19 32 13.49 28 -27.30% 34 -66.64%4 Vitória - ES 5.45 35 2.77 33 11.99 1 96.75% 25 -54.55%5 Palmas - TO 5.68 21 13.51 17 29.78 36 -57.96% 36 -80.93%

Source: CPS/FGV based on PNAD microdata

(acesse anexo 1.2 ou http://www.fgv.br/ibrecps/RET4/CPC_evolucao_espacial/)

Capital cities had 11,28% of their population living in poverty in 2008 versus

12,37% in the peripheries; and in 2003, right after the so-called metropolitan crisis, they

were very close to one another with slightly higher poverty rates in capitals, 22,47%,

versus 22,06% in the peripheries.

.

% Classe E

% % % Var (%) Var (%) 2008 2007 2003 2007/2008 2003/2008 Total 16.02 18.26 28.12 -12.27% -43.03% Capital 11.28 13.77 22.47 -18.08% -49.80% Periferia das metrópoles (não capital) 12.37 13.87 22.06 -10.81% -43.93%

Urban area - non-metropolitan 14.02 16.09 25.45 -12.87% -44.91% Rural area 34.82 37.30 51.45 -6.65% -32.32%



Next, we open data for states, and the poorest are Alagoas (38,76%) followed by

Maranhão (33,75%). Just as in the case of capitals ranking, the southern states present

the lowest poverty rates, amongst which Santa Catarina has the lowest one with 4,53%,

followed by Parana.

38

% Classe E% % % Var (%) Var (%)


1 Alagoas 38.76 2 37.93 1 57.66 3 2.19% 1 -32.78%2 Maranhão 33.75 1 38.30 2 55.68 14 -11.88% 8 -39.39%3 Piauí 32.38 3 37.05 3 52.01 16 -12.60% 5 -37.74%4 Paraíba 29.20 4 33.19 4 47.28 15 -12.02% 6 -38.24%5 Sergipe 26.56 6 28.59 6 41.58 8 -7.10% 2 -36.12%

1 Santa Catarina 4.53 27 3.67 27 8.29 2 23.43% 13 -45.36%2 Paraná 6.13 26 4.50 26 14.08 1 36.22% 25 -56.46%3 São Paulo 8.79 22 10.86 23 17.65 23 -19.06% 18 -50.20%4 Rio Grande do Sul 9.01 23 10.03 25 14.24 12 -10.17% 4 -36.73%5 Minas Gerais 9.27 25 9.76 22 17.67 6 -5.02% 16 -47.54%



Complementing the poverty analysis, we now assess what happened to the 5

Brazilian macro-regions (excluding the rural area of the northeast). We found the

highest poverty rates in the Northeastern region, 30,69% in 2008. Even though it does

not present the highest decreases in the poverty rate, it is important to look at the

absolute levels to capture the size of its reduction, as in 2003 49,81% of its population

lived in poverty.

% Classe E

% % % Var (%) Var (%) 2008 2007 2003 2007/2008 2003/2008 North 19.07 22.37 35.92 -14.75% -46.91% Northeast 30.69 34.20 49.81 -10.26% -38.39% Southeast 9.68 11.60 18.40 -16.55% -47.39% South 7.29 8.03 13.77 -9.22% -47.06% Center 10.49 11.78 23.22 -10.95% -54.82%



As year I A.C. (after the crisis) was completed on September 15th, when the

crisis occurred in the stock markets abroad, what can we say about its effect on the

income of the Brazilian poor groups? We follow up here, with data up to July 2009, our

monitoring of the evolution of the population composition in terms of different

economic classes. PME enables a look into these types of areas in the post-crisis period

(PME surveys the work earnings within the six major metropolises only). Comparing

July 2008 with July 2009, labor poverty decreased more in the periphery of Belo

39

Horizonte (-26,13%) and increased more in São Paulo capital (11,1%). Next, we present

the poverty variation in this last period, which will be detailed further.

Variation in Metropolitan Poverty (Post-Crisis) – July 2008 to July 2009

Fonte: CPS/FGV a partir dos microdados da PME/IBGE

Real Poverty Decreases (and Class E)

The CPS has been the first to release many results. The group that founded CPS

was the first to show in February 1996 the improvement in social indicators after the

Real Plan. In 1999, the group showed the increase in poverty in view of external crises.

In 2004, CPS not only showed social deterioration in Lula’s term first year (2003), as

well as the decrease in poverty in 2002 as the Cardoso administration ended. No other

institution dared to launch a research on this theme. Access it on the website or through

this link trajetória do CPS no estudo da pobreza

Looking at the large aspects of the poverty series since 1992, when the new

PNAD questionnaire was implemented, we have two important changes in levels.

Firstly, in 1993-1995, the proportion of people below the poverty line goes from 35,3%

to 28,8% of the Brazilian population. In 2003, poverty still reached 28,2% of the

population having grown during Lula’s first year in government, as we announced first

in 2004. In 2003, a new period of fall begins, reaching 22.7%. This amounts to a

cumulative decrease of 19,18% between 2003 and 2005, comparable to the decrease of

40

18,47% between 1993 and 1995. The existing parallel in the poverty reduction between

the two episodes ten years apart may be seen in the graph below.

Proportion of Poor in the Population (% in class E)

Source: CPS/FGV based on PNAD microdata/IBGE

In 2006, inspired by this leap changes in the pattern of poverty, we launched a

research with a provocative title “Second Real”. Since then, poverty has kept its

descending trend, falling almost 30% since 2005. Following the metrics imposed by the

immediate effects of the Real Plan on poverty that we had the pleasure to detect first 13

years ago, today there would be three reais of poverty reduction, for the 2003 to 2008

period when poverty fell 43%. Poverty reduction in the southeastern and northeastern

regions is in the graph below:

41

Poverty Rates (% Class E) Northeastern and southeastern regions


Below we present the map of different states between these two periods of

marked poverty reduction between 1993 to 1995 and 2003 to 2008, expressed in annual

rates of poverty reduction to enable a comparison between them. In Brazil, between

1993 and 1995, poverty decreased 9,6% per year, while the annual average of the last

five years was 10,6%. In regional terms, except for some states such as Amazonas,

Acre, Roraima and Rio de Janeiro, the annual pace of poverty reduction now tends to be

stronger in other states. In the case of Rio de Janeiro State, the fact that the Real boom

through exchange rate valuation may have benefited the non-transaction sector may

explain the phenomenon, given the relative importance of the service sector in the state

(vide Neri 1996)

42

Annual Rate of Poverty Reduction (Class E) – 2003/2 008

T a x a A n u a lA t é 5 %d e 5 % a 7 , 5 %d e 7 , 5 % a 1 0 %d e 1 0 % a 1 2 , 5 %d e 1 2 , 5 % a 1 5 %M a i s d e 1 5 %

T a x a a n u a l d e r e d u ç ã o d a p o b r e z a [ c l a s s e E ] - 2 0 0 3 a 2 0 0 8

Annual Rate of Poverty Reduction (Class E) – 1993/1 995

T a x a A n u a l A t é 5 %d e 5 % a 7 , 5 %d e 7 , 5 % a 1 0 %d e 1 0 % a 1 2 , 5 %d e 1 2 , 5 % a 1 5 %M a i s d e 1 5 %

43

Local Contribution to Poverty Reduction (Size of class E)

In order to complement the analysis of the relative annual changes, we present

the contribution of each local area to the rise in classes ABC in the recent period. Next,

we present the contribution of each geographic unit to the reduction of poverty in 2007

to 2009 and 2003 to 2008. In both periods, the Northeast stands out with 44,28% and

44,70% of poverty reduction observed during the respective time periods. From 2003 to

2008, eight million people crossed the poverty line in the northeast.

Contribution of Spatial Units to Poverty Reduction 2007-08 and 2003-08

In numbers of people and proportion of the total poverty decrease Contribution Population

Population Contribution %

Category 2007-2008 2003-2008 2007-2008 2003-2008

Total 3800837 19454189 100.00% 100.00%


Category 2007-2008 2003-2008 2007-2008 2003-2008

North 330147 1441725 8.69% 7.41%

Northeast 1683090 8696888 44.28% 44.70%

Southeast 1445943 6233898 38.04% 32.04%

South 184478 1606360 4.85% 8.26%

Center 151100 1476818 3.98% 7.59%

Population Contribution

Category 2007-2008 2003-2008 2007-2008 2003-2008

Capital 1055055 4504513 27.76% 23.15%

Periphery of metropolises (non capital) 338641 2041645 8.91% 10.49%

Urban area – non metropolitan 1582543 8547525 41.64% 43.94%

Rural area 823512 4372630 21.67% 22.48%


Category 2007-2008 2003-2008 2007-2008 2003-2008

Rondônia 20751 91142 0.55% 0.47%

Acre 13366 46853 0.35% 0.24%

Amazonas 84760 299776 2.23% 1.54%

Roraima 3347 28700 0.09% 0.15%

Pará 76648 636971 2.02% 3.27%

Amapá 51267 108341 1.35% 0.56%

Tocantins 80932 230188 2.13% 1.18%

Maranhão 263454 1178375 6.93% 6.06%

Piauí 134136 531964 3.53% 2.73%

Ceará 389980 1324724 10.26% 6.81%

Rio Grande do Norte 127081 512316 3.34% 2.63%

Paraíba 139379 598443 3.67% 3.08%

Pernambuco 170441 1346262 4.48% 6.92%

Alagoas -39036 492719 -1.03% 2.53%

Sergipe 34211 249570 0.90% 1.28%

Bahia 466707 2466365 12.28% 12.68%

Minas Gerais 333060 2175137 8.76% 11.18%

44

Espírito Santo 48847 456818 1.29% 2.35%

Rio de Janeiro 542839 876573 14.28% 4.51%

São Paulo 523260 2723409 13.77% 14.00%

Paraná 55320 782092 1.46% 4.02%

Santa Catarina -55370 195439 -1.46% 1.00%

Rio Grande do Sul 184219 630806 4.85% 3.24%

Mato Grosso do Sul 2571 214971 0.07% 1.11%

Mato Grosso 58607 386690 1.54% 1.99%

Goiás 83564 653518 2.20% 3.36%

Distrito Federal 7088 222025 0.19% 1.14%


Category 2007-2008 2003-2008 2007-2008 2003-2008

RO Capital 15334 33981 0.40% 0.17%

AC Capital 6288 24646 0.17% 0.13%

AM Capital 142513 268852 3.75% 1.38%

RR Capital -1071 19494 -0.03% 0.10%

PA Capital 9089 215985 0.24% 1.11%

PA Periphery 22591 82464 0.59% 0.42%

AP Capital 36105 60286 0.95% 0.31%

TO Capital 14103 35241 0.37% 0.18%

MA Capital 19158 230825 0.50% 1.19%

PI Capital 27579 151733 0.73% 0.78%

CE Capital 30969 305433 0.81% 1.57%

CE Periphery 29453 186656 0.77% 0.96%

RN Capital 41617 128217 1.09% 0.66%

PB Capital -1497 101129 -0.04% 0.52%

PE Capital 24086 193137 0.63% 0.99%

PE Periphery 1576 287817 0.04% 1.48%

AL Capital -40099 146914 -1.05% 0.76%

SE Capital 14146 66478 0.37% 0.34%

BA Capital 174468 423684 4.59% 2.18%

BA Periphery -28139 139357 -0.74% 0.72%

MG Capital 16850 159592 0.44% 0.82%

MG Periphery 1297 195800 0.03% 1.01%

ES Capital -6206 20455 -0.16% 0.11%

RJ Capital 383180 326372 10.08% 1.68%

RJ Periphery 72638 304940 1.91% 1.57%

SP Capital 106645 970601 2.81% 4.99%

SP Periphery 277450 607870 7.30% 3.12%

PR Capital -14834 102116 -0.39% 0.52%

PR Periphery -39454 115266 -1.04% 0.59%

SC Capital -3164 15901 -0.08% 0.08%

RS Capital 36887 65207 0.97% 0.34%

RS Periphery 1687 121773 0.04% 0.63%

MS Capital -1159 69715 -0.03% 0.36%

MT Capital -1320 41990 -0.03% 0.22%

GO Capital 18948 103044 0.50% 0.53%

DF Capital 7088 222025 0.19% 1.14%


45

Economic classes

Incidentally, our classification of economic classes (classes E, D, C and AB)

derives from our definition of poverty conceived as class E. So the basis of the income

pyramid was already addressed in the topic above. An interesting synthesis of the other

extreme of the distribution may be found in the sum of classes ABC.

The next table presents the absolute and relative sizes of the population in

various economic classes. It is interesting to note that within PNAD, class C is not only

prevalent in terms of population size encompassing almost of the Brazilian population

(49,22% or 91 million people) in 2008, and that if the increase of 2,5% in the post-crisis

period seen on PME is confirmed by next year’s PNAD, not only the average voter, that

is the one who decides the election would now already be a part of class C – but also

class C alone would decide the election in the first round. Besides possibly crossing the

threshold of 50%, class C according to our estimates, would become the dominant class

in terms of income mass in 2008, with 45,75% of income versus 44,05% of class AB.

Therefore, together classes ABC would hold 89,8% of the income accounted for in

PNAD.

2008

Population Total (people) % Pop

Income Mass Reais % Mass

TOTAL 186.440.290 100,00% 110.395.816.985 100,00%

Class E 29.860.927 16,02% 2.228.819.591 2,02%

Class D 45.399.117 24,35% 9.030.338.362 8,18%

Class C 91.762.175 49,22% 50.506.818.742 45,75%

Class AB 19.418.071 10,42% 48.629.840.290 44,05% Source: CPS/FGV based on PNAD microdata/IBGE

Local classes

The leader in average income (R$ 1249 by month) and in participation of classes

ABC is the city of Florianópolis (92,6% of its population). Curitiba with 86,49% is the

second capital with the largest number of people in class ABC (86,5%), followed by

Vitoria (80%). Rio has 75,3% in classes ABC (7th among the 27 capitals) and each

carioca earns 1015 reais (6th largest). In the opposite extreme, we find the peripheries of

Fortaleza (40,08%), Salvador (42,42%) and Recife (42,89%).

46

% Classe ABC% % % Var (%) Var (%)


1 Florianópolis - SC 92.61 1 90.38 1 81.82 26 2.47% 34 13.19%2 Curitiba - PR 86.49 2 84.97 4 71.43 29 1.79% 28 21.08%3 Vitória - ES 80.07 3 82.18 2 73.57 36 -2.57% 36 8.84%4 Porto Alegre - RS 80.05 6 75.29 3 72.08 17 6.32% 35 11.06%5 Belo Horizonte - MG 78.61 5 75.60 7 62.00 22 3.98% 21 26.79%

1 Periferia de Fortaleza - CE 40.08 36 34.56 36 19.45 3 15.97% 1 106.07%2 Periferia de Salvador - BA 42.42 34 42.72 35 24.12 34 -0.70% 2 75.87%3 Periferia de Recife - PE 42.89 35 39.29 34 28.91 12 9.16% 9 48.36%4 Periferia de Belém - PA 45.28 33 42.99 33 29.87 19 5.33% 7 51.59%5 Maceió - AL 47.61 32 44.62 32 32.45 16 6.70% 11 46.72%



When we assess the total figures of capitals and peripheries together with the

remaining sizes of cities, as expected, we found the lowest number of people in class

ABC in rural areas (34,96% versus 68,48% in capitals). In terms of advancements in

last year, both capitals and metropolitan peripheries present growth slightly superior to

the country’s level. Just urban areas outside metropolises presented lower growth than

the national level.

% Classe ABC

% % % Var (%) Var (%) 2008 2007 2003 2007/2008 2003/2008 Total 59.64 56.63 45.16 5.32% 32.06% Capital 68.48 64.82 54.62 5.65% 25.38% Metropolis periphery (non capital) 63.97 60.66 49.47 5.46% 29.31% Urban area - non-metropolitan 61.07 58.47 46.31 4.45% 31.87% Rural area 34.96 32.83 21.98 6.49% 59.05%



Just like its capital city Florianopolis, Santa Catarina State also leads the ABC ranking

with 82,32% of population in this income group. The remaining southern states

complete the first positions in the rankings: Paraná with 79,85% and Rio Grande do Sul

with 73,29%. Meanwhile, in the opposite extreme, we found only northeastern states,

Maranhão (32,22%), Alagoas (32,25%) and Piauí (36,99%).

47

% Classe ABC% % % Var (%) Var (%)


1 Santa Catarina 82.32 1 82.65 1 71.58 27 -0.40% 27 15.00%2 Paraná 79.85 2 79.28 3 62.83 26 0.72% 21 27.09%3 Rio Grande do Sul 73.29 4 70.90 2 63.27 22 3.37% 25 15.84%4 São Paulo 73.00 5 69.78 5 59.64 20 4.61% 24 22.40%5 Distrito Federal 72.42 3 71.22 6 58.54 25 1.68% 23 23.71%

1 Maranhão 32.22 27 30.00 27 18.84 11 7.40% 1 71.02%2 Alagoas 32.25 26 30.42 26 18.90 15 6.02% 2 70.63%3 Piauí 36.99 24 34.58 25 21.76 14 6.97% 3 69.99%4 Paraíba 39.09 25 33.38 24 26.01 1 17.11% 9 50.29%5 Sergipe 43.05 23 40.77 22 30.85 16 5.59% 13 39.55%



The macro-regions reflect well how the recently analyzed states are disposed.

The southern region with 75,52% of the population in class ABC has a rate twice as

high as the northeast with 37,66%.

% Classe ABC

% % % Var (%) Var (%)

2008 2007 2003 2007/2008 2003/2008 North 49.00 45.08 32.32 8.70% 51.61% Northeast 37.66 34.25 23.03 9.96% 63.53% Southeast 69.48 66.60 55.24 4.32% 25.78% South 75.52 74.01 62.17 2.04% 21.47% Center 65.61 61.09 48.13 7.40% 36.32%



The real rise in classes ABC

The analysis of the maps on the rates of variation in the share of classes ABC in the

population shows a distinct distribution of the improvements in poverty (class E)

keeping the same scale for all episodes. The highest annual average increases occurred

right after the Real Plan (1993-95) and not in the period 2003-08.

48

Annual rate of growth in class ABC – 2003/2008


T a x a a n u a l d e c r e s c i m e n t o d a C l a s s e A B C - 2 0 0 3 a 2 0 0 8

Annual rate of growth in class ABC – 1993/95


We present below maps in 2003, 2007 and 2008 of the evolution in the levels of

classes ABC and E (poverty) to give a dimension of change underway in the series.

49

PERCENTAGE OF POPULATION IN CLASSES ABC 2003 2007

2008

L e g e n d aM e n o s d e 3 0 %d e 3 0 % a 5 0 %d e 5 0 % a 6 0 %d e 6 0 % a 7 0 %M a i s d e 7 0 %



50

PERCENTAGE OF POPULATION IN POVERTY (CLASS E)

2003 2007



2008


Emergence of Classes ABC

Similarly to what we did with poverty with a view to complementing the

analysis of relative annual changes, we present the contributions of each place for the

rise in classes ABC in the recent period. Next, we present the contribution of each

geographic unit to the rise in class ABC of a total of 31,98 million people in 2003-2008,

and more than a fifth of this, 6,77 million in 2007-2008. In both periods, the Southeast

stands out with 42,11% and 40,86% of poverty reduction observed in the respective

periods.

51

Contribution of Spatial Units to the rise of classes ABC

2007-08 and 2003-08

In number of people and proportion of the total poverty decrease

Contribution Population

Contribution to the variation ABC Population Contribution

Category 2007-2008 2003-2008 2007-2008 2003-2008

Total 6776164 31983243 100.00% 100.00%


Category 2007-2008 2003-2008 2007-2008 2003-2008

North 629458 2603100 9.29% 8.14%

Northeast 2009374 8501563 29.65% 26.58%

Southeast 2768831 13468102 40.86% 42.11%

South 588939 4455922 8.69% 13.93%

Center 755612 2962114 11.15% 9.26%


Category 2007-2008 2003-2008 2007-2008 2003-2008

Rondônia 45362 223220 0.67% 0.70%

Acre 23731 103090 0.35% 0.32%

Amazonas 182012 664423 2.69% 2.08%

Roraima 27478 63968 0.41% 0.20%

Pará 235436 1151239 3.47% 3.60%

Amapá 31840 127594 0.47% 0.40%

Tocantins 84020 269552 1.24% 0.84%

Maranhão 163102 929945 2.41% 2.91%

Piauí 87853 515311 1.30% 1.61%

Ceará 429220 1489434 6.33% 4.66%

Rio Grande do Norte 165679 641118 2.45% 2.00%

Paraíba 227698 542006 3.36% 1.69%

Pernambuco 360800 1275458 5.32% 3.99%

Alagoas 68178 457428 1.01% 1.43%

Sergipe 56045 287729 0.83% 0.90%

Bahia 451490 2367423 6.66% 7.40%

Minas Gerais 751151 4068882 11.09% 12.72%

Espírito Santo 62287 659320 0.92% 2.06%

Rio de Janeiro 700562 2007993 10.34% 6.28%

São Paulo 1255381 6719177 18.53% 21.01%

Paraná 222739 2056067 3.29% 6.43%

Santa Catarina 50601 936610 0.75% 2.93%

Rio Grande do Sul 316256 1464675 4.67% 4.58%

Mato Grosso do Sul 91238 450585 1.35% 1.41%

Mato Grosso 262398 731910 3.87% 2.29%

Goiás 329938 1270621 4.87% 3.97%

Distrito Federal 71420 507768 1.05% 1.59%


52

Category 2007-2008 2003-2008 2007-2008 2003-2008

Capital 1926152 7450514 28.43% 23.30%

Periphery das metrópoles (não capital) 1012624 4574920 14.94% 14.30%

Urban area - non-metropolitan 3417552 16654350 50.43% 52.07%

Rural area 387315 3311143 5.72% 10.35%


Category 2007-2008 2003-2008 2007-2008 2003-2008

RO Capital -828 72062 -0.01% 0.23%

AC Capital 6243 65865 0.09% 0.21%

AM Capital 111839 480479 1.65% 1.50%

RR Capital 21325 60805 0.31% 0.19%

PA Capital 36329 279948 0.54% 0.88%

PA Periphery 7123 111007 0.11% 0.35%

AP Capital 29250 81916 0.43% 0.26%

TO Capital 23118 59835 0.34% 0.19%

MA Capital 4690 121952 0.07% 0.38%

PI Capital 57396 164462 0.85% 0.51%

CE Capital 161136 482762 2.38% 1.51%

CE Periphery 46702 213374 0.69% 0.67%

RN Capital 56458 210517 0.83% 0.66%

PB Capital 56291 127608 0.83% 0.40%

PE Capital 48297 229498 0.71% 0.72%

PE Periphery 89281 340952 1.32% 1.07%

AL Capital 28126 157969 0.42% 0.49%

SE Capital 64428 124293 0.95% 0.39%

BA Capital 184287 672441 2.72% 2.10%

BA Periphery 3018 163901 0.04% 0.51%

MG Capital 76294 496979 1.13% 1.55%

MG Periphery 125591 626711 1.85% 1.96%

ES Capital -22892 -7243 -0.34% -0.02%

RJ Capital 363803 520609 5.37% 1.63%

RJ Periphery 209361 812222 3.09% 2.54%

SP Capital 244457 1523416 3.61% 4.76%

SP Periphery 508495 1574550 7.50% 4.92%

PR Capital 70177 405693 1.04% 1.27%

PR Periphery -17929 309898 -0.26% 0.97%

SC Capital 27158 61286 0.40% 0.19%

RS Capital 74029 81464 1.09% 0.25%

RS Periphery 39093 421709 0.58% 1.32%

MS Capital 10076 126395 0.15% 0.40%

MT Capital 90364 89595 1.33% 0.28%

GO Capital 35085 252534 0.52% 0.79%

DF Capital 71420 507768 1.05% 1.59%


The decade of inequality reduction

If a historian from the future would name the main changes occurred in our

Brazilian society in the first decade of the third Millennium, it would call it the decade

of reduction of income inequality, or equalization of results. In the same way as the

1990s was the decade of stability, the 1980s as the decade of re-democratization and the

53

1970s the growth decade6. In Brazilian history statistically documented (since 1960),

there has never been a similar inequality reduction to that observed since 2001: we grew

a third of the 1970s growth, but we reduced poverty more in this current decade. The

cumulative decrease in inequality is comparable in magnitude to the famous increase of

inequality in the 1960s, which placed Brazil in the international scenario as the land of

inertial inequality. According to World Bank data, 2005 data already put Brazil as the

10th country in inequality rates in the world – beforehand we were 3rd. That is, the bad

news is that we are still very unequal; the good news is that there is a lot of inequality to

be reduced and, consequently, a lot of income growth to be generated at the bottom of

the income pyramid. It is as if Brazil had discovered – in this century alone – these

reserves of pro poor growth. For instance, India is an evenly poor country with an

inequality index that is half of ours and has as basic alternative to fight poverty just the

income growth of society. Similarly, Belgium, an evenly rich country, does not have in

substantive terms, an additional alternative to improve the population’s welfare but

growth. In the so-called Brazilian Belindia, besides growth – which is a limitless source

of welfare improvement – we can also reduce inequality as a way to relief poverty and

welfare. Obviously, equity has a lower limit, it is finite, as for instance the oil reserves,

but we are very distant from its exhaustion. No other country in the world may reduce

poverty through redistribution of income in such a high scale as Brazil

Besides keeping the incentives for everyone’s income growth, it is necessary to reach

the fundamental causes of inequality, approaching intergenerational differences of

opportunities. We have in the last years only begun to scratch the surface of outcomes

inequality.

The study of inequality measures the transversal distance between people,

projecting upwards in a similar action to measuring the distance among the stars. If the

study of Brazilian inequality were like analyzing the movement of celestial bodies,

PNAD would be the device receiving and diffusing light from Brazilian skies one year

later. PNAD helps star watchers to focus on a reasonably clear atmosphere and observe

the main relative movements inside Brazilian society in the previous year. Here, we

6 Another characteristic of this decade is its generation of formal Jobs, the previous one was the stabilization, and increase of educational level. Optimistically, the next decade will be the revolution in the quality of education, as we have international targets, civil society goals, the Todos pela Educação movement and the federal government, with its IDEB, with fixed goals pointing to the same North.

54

look at the relative movements that happened in the income of different Brazilian

economic classes. Of all changes observed through the recent 2008 PNAD, the most

remarkable one is the reduction of income inequality. Brazilian income inequality,

which remained stagnated between 1970 and 2000, undergoes successive decreases,

year after year, since 2001, comparable in magnitude to the one well-known movement:

the increase of inequality in the 1960s.

O Globo newspaper organized on August 24th 2009 a seminar to celebrate the

40-year anniversary of its Economy section. The seminar had 2 panel discussions. I

argued in one of them that this is the decade of income inequality reduction. The next

will be the decade of revolution in education quality, because we have international

targets, civil society goals, the Todos pela Educação movement and the federal

government targets, already established and pointing in the same North.

Access the presentation about the economy in Brazil in the last 40 years, as well

as the video of this event. Household per capita income inequality measured by the

Gini index decreased in 2007 around 0,0074 points, which 10% superior to the

decrease pace assumed from 2001 to 2006 (0,0067).

Below we present a state map of the cumulative reductions of inequality inside

the states in this decade and the growth of the average income that corresponds to the

Brazilian inequality component between states. It is interesting to note that, according to

Ataliba et all. (2008), despite the average income among States in the northeastern

region increase at higher rates than the others, the inequality rates inside each

Northeastern state does not decrease. Ceará is the exception, which is the only state to

feature in the darker shaded area.

55

Cumulative reduction of the Gini Index – 2001 to 2008

R e d u ç ã o n o G i n iM e n o s d e 2 , 5 %d e 2 , 5 % a 5 %d e 5 % a 7 , 5 %d e 7 , 5 % a 1 0 %M a i s d e 1 0 %

Increase of Average househol Per Capita Income (all sources) – 2001 to 2008

A u m e n t o n a R e n d aM e n o s d e 1 0 %d e 1 0 % a 2 0 %d e 2 0 % a 3 0 %d e 3 0 % a 4 0 %M a i s d e 4 0 %

Sources of incomes and changes

If something changed, the second effort is to know: why has it changed? What

has it changed? These last two questions suggest the two complementary lines of

answers explored here, namely: the first one looks at the close determinants of the

distribution of income and at the primary components of people’s income, the role of

pensions and retirement benefits, social programs and work.

56

The analysis of the participation of different income source per economic class

may be useful to assess the prospective impacts of different public policy instruments

on income distribution, such as for instance, the measures adopted within the context of

the external crisis begun in September 2008, if not:

Increases of the Family Grant benefits and other programs not related to the

social security system tend to benefit predominantly class E, which has 16,25% of its

revenue from this type of income. Class C receives the greatest share of social security

benefits: 20,73%. We may choose to separate income from social security benefits in

terms of individual earnings below one minimum wage and benefits above this level,

because this distinction between adjustments of these two layers has been stressed since

1998. The greatest beneficiary of the re-adjustment of social security benefits is Class D

with 12,66% of income attached to it. Finally, the readjustment of pensions and

retirement benefits above this benefits mostly class AB with 18,94% of its earning

associated with that source. This measure is now in debate.

More than cross-referencing income types and groups, we aim here to cross-

reference spatial information with income information.

The Capitals of Income

Rio is the state capital for pensioners, whose income correspond to 28,8% of the

carioca’s income, the highest proportion among the 27 capital cities. The Capital of the

Family Grant is Macapá with 3,15% of its income from this program. The work capital

is Palmas, with 88,3%¨of income from the daily work.

57

Participation of different income sources in total (%) – 2008

Capitals and Metropolitan peripheries

Renda todos os trabalhos Outras rendas privadas

R$ R$

rank 2008 rank 2008

1 Palmas - TO 88.31 1 Teresina - PI 4.54

2 Macapá - AP 86.74 2 Rio Branco - AC 3.60

3 Boa Vista - RR 86.11 3 Palmas - TO 3.35

4 Periferia de Curitiba - PR 85.16 4 Campo Grande - MS 3.16

5 Manaus - AM 84.80 5 Goiânia - GO 3.10

1 Rio de Janeiro - RJ 67.98 1 Periferia do Rio de Janeiro - RJ 0.77

2 Vitória - ES 69.97 2 São Luís - MA 0.87

3 Recife - PE 71.53 3 Macapá - AP 1.02

4 Teresina - PI 71.67 4 Periferia de Curitiba - PR 1.13

5 Porto Alegre - RS 72.26 5 Aracaju - SE 1.18 Transferências Pública - BF* Piso Previdencia - SM* Previdencia Pós-piso > SM*

R$ R$ R$

rank 2008 rank 2008 rank 2008

1 Periferia de Fortaleza - CE 3.85 1 Periferia de Fortaleza - CE 10.53 1 Rio de Janeiro - RJ 27.22

2 Periferia de Belém - PA 3.34 2 Periferia de Recife - PE 7.07 2 Vitória - ES 25.35

3 Macapá - AP 3.15 3 Periferia de Belo Horizonte - MG 4.82 3 Porto Alegre - RS 22.39

4 Boa Vista - RR 3.11 4 Periferia de Salvador - BA 4.31 4 Periferia do Rio de Janeiro - RJ 21.78

5 Recife - PE 2.90 5 Periferia do Rio de Janeiro - RJ 4.28 5 Recife - PE 19.52

1 Vitória - ES 0.46 1 Brasília - DF 0.85 1 Palmas - TO 5.68

2 Periferia do Rio de Janeiro - RJ 0.66 2 Palmas - TO 1.39 2 Boa Vista - RR 6.06

3 Cuiabá - MT 0.72 3 Florianópolis - SC 1.41 3 Macapá - AP 6.80

4 Aracaju - SE 0.75 4 Curitiba - PR 1.54 4 Periferia de Fortaleza - CE 8.50

5 Rio de Janeiro - RJ 0.86 5 São Paulo - SP 1.56 5 Periferia de Curitiba - PR 8.76 Source: CPS/FGV based on PNAD microdata


Comparing the participation of different income types in each type of town, we

noticed clearly the existence of some particular aspects: work earnings are relatively

more important in the periphery, while social security benefit up to 1 minimum wage is

extremely important in the rural area (16,94% of income sources), followed by other

public transfers, social programs (5,21%). By analyzing the high pensions, they

represent 17,15% of the capitals’ income.


Types of cities

Social security

above MW

Income from all sources

Income all jobs

Other private

incomes

Public

transfers

family grant -

BF*

Social

security

basic

benefit -

SM*

Pós-piso >

SM*

Capital 100 76.80 2.37 1.68 2.00 17.15

Metropolis periphery (não capital) 100 78.28 1.37 1.58 3.87 14.89

Urban area - non-metropolitan 100 76.09 2.38 2.23 5.33 13.97

Rural area 100 67.24 1.22 5.21 16.84 9.49


58

(acesse anexo 5 ou http://www.fgv.br/ibrecps/cpc/PNAD_DECOMP/index.htm )

When analyzing the 27 states in Brazil, Amapá is where work earnings are more

important (88,16%). In terms of public transfers, Alagoas has the highest share of

income from social programs (4,43%) and Rio de Janeiro, 27,9% of family income

come from retirement benefits.


States



Nest, we disaggregate the participation of income sources in the 5 Brazilian

regions. We found the following geography: high importance of work in the Center-

Western region (82%), and urban north (81,43%). Public transfers such as social

programs (3,885%) and lower pensions (9,52%) are more present in the northeast.

Social security benefits above one minimum wage are in the southeast (16,70%) and

south (15,31%).

Public transfers family grant - BF* Basic social security benefit - SM* Social security Benefit over one minimum wage* R$ R$ R$

rank 2008 rank 2008 rank 2008 1 Alagoas 4.43 1 Ceará 10.83 1 Rio de Janeiro 25.352 Pernambuco 4.35 2 Alagoas 10.77 2 Rio Grande do Sul 18.743 Maranhão 4.17 3 Piauí 10.63 3 Piauí 17.574 Paraíba 4.13 4 Maranhão 10.45 4 Distrito Federal 16.435 Ceará 3.97 5 Paraíba 10.36 5 Espírito Santo 16.25

1 Rio de Janeiro 0.79 1 Distrito Federal 0.85 1 Amapá 5.392 Espírito Santo 1.25 2 São Paulo 1.96 2 Tocantins 5.533 Mato Grosso 1.28 3 Amapá 2.22 3 Roraima 5.554 Santa Catarina 1.34 4 Rio de Janeiro 2.52 4 Mato Grosso 6.655 Distrito Federal 1.48 5 Amazonas 3.15 5 Maranhão 7.68

Income from all jobs Other private income

R$ R$ rank 2008 rank 2008

1 Amapá 88.16 1 Tocantins 3.512 Roraima 86.26 2 Piauí 3.333 Mato Grosso 85.69 3 Acre 3.194 Amazonas 83.94 4 Santa Catarina 3.145 Rondônia 83.00 5 Mato Grosso do Sul 3.06

1 Piauí 64.65 1 Roraima 1.082 Paraíba 68.57 2 Amapá 1.173 Rio de Janeiro 69.54 3 Sergipe 1.214 Pernambuco 70.11 4 Amazonas 1.265 Ceará 70.91 5 Maranhão 1.30

59


Macro-regions

Social security

above MW


Income all jobs

Other private

incomes

Public

transfers

family grant -

BF*

Social

security

basic

benefit -

SM*

Pós-piso >

SM*

North 100 82.00 2.04 2.72 4.62 8.61

Northeast 100 71.85 1.99 3.83 9.52 12.82

Southeast 100 75.99 2.07 1.72 3.52 16.70

South 100 76.01 2.54 1.79 4.35 15.31

Center 101 81.43 2.43 1.75 3.27 11.12



In annex I, we find the variations and all regional rankings to the different levels

and participations of income sources.

60

PERCENTAGE PARTICIPATION OF INCOME FROM ALL JOBS IN TOTAL INCOME

2003 2007 2008

L e g e n d aA t é 7 0 %d e 7 0 % a 7 5 %d e 7 5 % a 8 0 %d e 8 0 % a 8 5 %M a i s q u e 8 5 %



61

PERCENTAGE PARTICIPATION OF INCOME FROM THE

FAMILY GRANT IN TOTAL INCOME 2003 2007

2008

L e g e n d aA t é 1 %d e 1 % a 2 %d e 2 % a 3 %d e 3 % a 4 %M a i s d e 4 %



62

Classes ABC – What has happened since the crisis began in September 2008?

July 2009, nine months after the crisis arrived here, there is already a clear vision of its

effects on the income of Brazilian workers in the six major metropolises in the country. A

synthesis may be found in the sum of classes ABC, which increased 1,81% in the period of

crisis and 25,7% in the fortunate phase that preceded the arrival of the crisis in Brazil,

although the crisis had already been present in developed countries since mid-2007. An

innovation of the present research was to open the peripheries (that is, the cities/towns in the

metropolises that are not their capital), where we observe increases in classes ABC of 2,8% in

the last 12 months and 31,15% in the previous period, in levels, thus well superior to the

metropolises’. That is, in the same way as this is the crisis in the center of the capitalist world,

i.e. developed countries, and not in the so-called periphery, inside the metropolises the same

applies: capitals - more connected to the international market through the exports of

manufactured goods and credit – have been hit harder than their peripheries. As an example of

this process is the city of Sao Paulo, the dynamic center of the Brazilian capitalism, which had

a decrease of -0,68% from July 2008 to July 2009, and even the Great Sao Paulo area, which

includes the rich Paulista ABC region, where class ABC had a growth close to zero, 0,67%.

Variation of Class ABC (Post-Crisis) – July 2008 to July 2009

Source: CPS/FGV based on PME microdata

Before commemorating the results, we should note that we still have a lot of

uncertainties and challenges in the near future. By opening the monthly survey on a weekly

63

basis in order to better track the weekly chronology of the crisis, we go as far as the last week

of July 2009.

When comparing this last week in the series with the total monthly result, we notice

that class ABC would take on a downward trend (67,44% or 1,06% below the average of July

2009 as a whole, but still 1,1% higher than July 2008). As if it was not enough, it will be hard

in the next two months to keep the pace of growth of the July-September period last year,

when classes ABC were thriving with a 2.4% growth in the period. That is, we come to a

draw in September, but one year later this result should be seen as even more prosperous than

observed so far.

64

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69

Appendix I: Maps of Social Evolution

PNAD with its sample size allows opening and analyzing the information by spatial

categories, even more so as the rural area of the Northeast was added to the sample since 2004

thus covering the whole Brazilian territory. We detail here this analysis for the levels of

macro-regions, states, metropolitan areas and by separating them into capitals (or

metropolitan nucleus) and periphery (the ensemble of cities/towns within a given metropolis,

excluding the capital). Moreover, we present information by city type, that is, capitals,

peripheries, urban non-metropolitan areas, and rural areas. The most interesting part may be

the analysis of the information on metropolises’ capitals. Next, we present information on

these distinct geographic levels for the social indicators based on income, in the shape of

rankings.

70

Growth in the proportion of People in Classes ABC

C r e s c i m e n t o - 1 9 9 3 a 1 9 9 5M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %

C r e s c i m e n t o - 2 0 0 3 a 2 0 0 8M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %d e 5 0 % a 7 0 %M a i s d e 7 0 %

C r e s c i m e n t o - 2 0 0 7 a 2 0 0 8M e n o s d e 5 %d e 5 % a 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %

C r e s c i m e n t o - 1 9 9 3 a 2 0 0 8A u m e n t o d e a t é 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %d e 5 0 % a 1 0 0 %M a i s d e 1 0 0 %

71

Accumulated Rate of Poverty Eradication– Reduction of Class E

R e d u ç ã o - 1 9 9 3 a 1 9 9 5M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %


R e d u ç ã o - 2 0 0 7 a 2 0 0 8M e n o s d e 5 %d e 5 % a 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %

R e d u ç ã o - 1 9 9 3 a 2 0 0 8M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %d e 5 0 % a 7 0 %M a i s d e 7 0 %

72

C r e s c i m e n t o - 1 9 9 3 a 1 9 9 5M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %


C r e s c i m e n t o - 2 0 0 3 a 2 0 0 8M e n o s d e 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %d e 5 0 % a 7 0 %M a i s d e 7 0 %


C r e s c i m e n t o - 2 0 0 7 a 2 0 0 8M e n o s d e 5 %d e 5 % a 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %

R e d u ç ã o - 2 0 0 7 a 2 0 0 8M e n o s d e 5 %d e 5 % a 1 0 %d e 1 0 % a 2 5 %d e 2 5 % a 3 5 %d e 3 5 % a 5 0 %M a i s d e 5 0 %

73

Variation in the Gini Index – 2007 a 2008 Variation in the Gini Index – 2003 a 2008

V a r i a ç ã o G i n i - 2 0 0 7 a 2 0 0 8R e d u ç ã o d e m a i s d e 1 0 %d e 5 % a 1 0 %d e 2 , 5 % a 5 %d e 0 % a 2 , 5 %A u m e n t o d e 0 % a 2 , 5 %A u m e n t o d e m a i s d e 2 , 5 %

V a r i a ç ã o G i n i 2 0 0 3 a 2 0 0 8R e d u ç ã o d e m a i s d e 1 0 %d e 5 % a 1 0 %d e 2 , 5 % a 5 %d e 0 % a 2 , 5 %A u m e n t o d e a t é d e 3 %

74

Appendix II: Principal Component analysis for the definition of economic classes

Principal component analysis is a methodology that is useful when you have data on a

number of variables and believe that there is some redundancy in those variables –

which means that some of the variables are correlated with one another, possibly

because they are measuring the same dimension. Given this apparent redundancy, it is

likely that, for example, different item in a questionnaire are not really measuring

different constructs; more likely, they may be measuring a single construct that could

reasonably be labeled, in the present case, for instance, “an optimistic view of reality as

a whole”.

It consists in a variable reduction procedure, and involves the development of obtained

measures on a number of observed variables and into a smaller number of artificial

variables - called principal components - that will account for most of the variance in

the observed variables. In essence, what is accomplished by principal component

analysis it the reduction of the observed variables into a smaller set of artificial

variables, what is done collapsing some redundant variables into single new variables

that can be used in subsequent analyses as predictor variables in a multiple regression -

or in any other type of analysis.

Technically, a principal component can be defined as a linear combination of optimally-

weighted observed variables. In performing a principal component analysis, it is

possible to calculate a score for each subject on a given principal component. Each

subject actually measured would have scores on each one of the new components, and

the subject’s actual scores on the original questionnaire items would be optimally

weighted and then summed to compute their scores on a given component.

In reality, the number of components extracted in a principal component analysis is

equal to the number of observed variables being analyzed. This means that an analysis

of a questionnaire with many items would actually result in as many components as the

number of items. However, in most analyses, only the first few no redundant

components account for meaningful amounts of variance, so only these first few

components are retained, interpreted, and used in subsequent analyses. The remaining

components account for only trivial amounts of variance and generally would therefore

not be retained and further analyzed.

75

The first component extracted in a principal component analysis accounts for a maximal

amount of total variance in the observed variables. Under typical conditions, this means

that the first component will be correlated with at least some of the observed variables,

and may be correlated with many. The second component extracted will have two

important characteristics. First, this component will account for a maximal amount of

variance in the data set that was not accounted for by the first component. Again under

typical conditions, this means that the second component will be correlated with some

of the observed variables that did not display strong correlations with the first

component. The second characteristic of the second component is that it will be

uncorrelated with the first component. Literally, a computation of the correlation

between components 1 and 2 would give zero. That is the general rule: the remaining

components that are extracted in the analysis display the same two characteristics: each

component accounts for a maximal amount of variance in the observed variables that

was not accounted for by the preceding components, and is uncorrelated with all of the

preceding components. A principal component analysis proceeds in this fashion, with

each new component accounting for progressively smaller and smaller amounts of

variance - this is why only the first few components are usually retained and interpreted.

When the analysis is complete, the resulting components will display varying degrees of

correlation with the observed variables, but are completely uncorrelated with one

another.

The observed variables are standardized in the course of the analysis, that is, each

variable is transformed so that it has a mean of zero and a variance of one. What we

mean by “total variance” in the data set is simply the sum of the variances of these

observed variables. Since they have been standardized to have a variance of one, each

observed variable contributes one unit of variance to the “total variance” in the data set.

Therefore, the total variance in a principal component analysis will always be equal to

the number of observed variables being analyzed, and the components that are extracted

in the analysis will partition this variance. If there are six components, for instance, the

first component might account for 2.9 units of total variance; perhaps the second

component will account for 2.2 units, and so on, with the analysis continuing in this

way until all of the variance in the data set has been accounted for.

76

Fator1 Fator2 Fator3

RADI Radio 29 1 0 TV TV 35 3 13 LAVA Washing machine 62* -8 0 GEL Fridge 43* -5 12 FREE Freezer 43* -2 -15 Esgoto 35 -10 9 Lixo 40 -7 23 * PARTICIPACAO 21 13 54* COMPNET Computer with internet 67* -7 -8 COMP 23 1 1 NCOMP -71* 5 7 FIXOCEL 68* -3 -6 FIXO -2 -5 -4 CEL Mobile phone -11 2 24 NTEL -57* 5 -13 NBAN 72* -11 -16 NCOMODOS 64* 2 -34 NDORMITORIO 33 55* -33 NBAN_PC 47* -60* -21 NCOMODOS_PC 33 -69* -30 NDORMITORIO_PC 32 -46* -41* MORADORES -6 83* 0 EDUCACHEFE 68* -19 29 CHCONTRIB_PUBPRIV 27 -4 0 CHCONTRIB_PUB 31 -4 47* CHCONTRIB_PRIV 10 -4 -6 CHCONTRIB_DESEMP -6 10 4 CHCONTRIB_INATIVO -11 -9 -44* * CHPOS_DESEMP -6 10 4 CHPOS_PRIV -4 -2 52* CHPOS_LIB -12 8 -17 CHPOS_EMP 27 -2 -4 CHPOS_PUB 28 -1 10 CHPOS_NREM -10 0 -18 NFREQ_0_6 -24 23 37 FREQpub_0_6 -10 12 17 FREQpriv_0_6 19 -1 17 N_0_6 20 -27 -50* NFREQ_7_14 -11 13 -2 FREQpub_7_14 -20 58* 13 FREQpriv_7_14 37 6 7 N_7_14 4 -63* -16 NFREQ_15_17 -12 23 -8 FREQpub_15_17 1 49* -21 FREQpriv_15_17 31 8 -10 N_15_17 -6 -57* 26 CHTRAB_9ANO -16 6 -12 CHTRAB_1014ANO -8 8 15 CHTRAB_1517ANO 12 -2 24 CHTRAB_1819ANO 19 -4 14 CHTRAB_2024ANO 17 -5 5 CHTRAB_2529ANO 8 -2 0 CHTRAB_30ANO 1 1 -2 chSINDICATO 16 -1 5 PROPRIOPG 7 15 -41* PROPRIO 13 -3 13

77

Fator1 Fator2 Fator3

ALUGUEL_AB -17 -8 24 ALUGUEL_AC 17 -9 18 CEDIDO -18 -6 16 DOM_OUT -4 2 2 CASALFILHO -4 -37 -7 CASALFILHO_AB14 -8 -5 63* CASALFILHO_AC14 23 3 -31 CASALFILHO_14 6 53* -6 MAE_AB14 -9 9 4 MAE_AC14 -2 -6 -25 * MAE_14 -5 18 -5 MAE_IG 0 0 -1 FAM_OUT -10 -38 -21 EDUCONJ_SEM -25 13 -21 EDUCONJ_1_3 -18 11 -9 EDUCONJ_4_7 -11 11 13 EDUCONJ_8_11 27 -4 36 EDUCONJ_12 48* -7 2 CONJPOS_DESEMP -2 1 18 CONJPOS_PRIV 7 4 31 CONJPOS_LIB 6 8 4 CONJPOS_EMP 21 0 -1 CONJPOS_PUB 25 3 3 CONJPOS_NREM -20 7 -22 * CONJPOS_NCONJ -14 -20 -29 *

Variance explained by each factor Fator1 Fator2 Fator3 Factor4

6.77902414.47842254.0032932 2.9953943

79

Annex (Tables and Rankings)

1) Economic Classes

1.1 ) % CLASS ABC % Class ABC

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 10.42 9.73 9.40 8.32 7.71 7.60 8.31 8.31 7.09% 37.11%

% Class ABC

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 6.30 6.05 5.55 4.85 4.66 3.91 4.88 4.70 4.13% 61.13% Northeast 4.73 4.22 3.97 3.45 3.25 2.92 3.36 3.25 12.09% 61.99% Southeast 12.87 12.17 12.08 10.54 9.73 10.07 10.95 11.21 5.75% 27.81% South 14.65 13.65 13.04 11.80 10.69 10.19 10.65 10.36 7.33% 43.77% Center 13.47 12.43 10.92 10.27 9.78 9.04 10.13 9.59 8.37% 49.00%

% Class ABC

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 17.98 17.12 16.86 15.38 14.39 14.18 15.64 15.30 5.02% 26.80% Metropolitan peripheries(Non- capital) 9.07 8.25 7.28 6.96 6.16 6.08 7.06 7.23 9.94% 49.18% Non-metropolitan urban area 9.38 8.63 8.52 7.22 6.71 6.59 7.15 7.26 8.69% 42.34% Rural area 2.20 2.43 1.81 1.67 1.39 1.40 1.15 1.43 -9.47% 57.14%


80

Ranking by State % Class ABC % Classe ABC

% % % % % % % % Var (%) Var (%)

rank 2008 2008 rank 2007 rank 2006 rank 2005 rank 2004 rank 2003 rank 2002 rank 2001 rank 2007/2008 rank 2003/2008

1 Santa Catarina 82.32 1 82.65 1 80.91 1 75.99 1 73.42 1 71.58 1 70.16 1 68.70 27 -0.40% 27 15.00%2 Paraná 79.85 2 79.28 2 71.50 2 70.75 2 69.27 3 62.83 2 67.68 3 66.00 26 0.72% 21 27.09%3 Rio Grande do Sul 73.29 4 70.90 3 68.91 3 67.25 3 66.06 2 63.27 3 63.65 2 66.06 22 3.37% 25 15.84%4 São Paulo 73.00 5 69.78 6 67.34 5 64.58 5 60.61 5 59.64 5 63.27 4 62.73 20 4.61% 24 22.40%5 Distrito Federal 72.42 3 71.22 4 68.17 4 64.96 6 59.94 6 58.54 6 62.15 6 60.51 25 1.68% 23 23.71%6 Minas Gerais 71.64 6 68.57 7 65.85 7 61.79 7 57.00 7 53.40 7 58.82 7 55.56 21 4.48% 18 34.16%7 Rio de Janeiro 69.83 7 65.24 5 67.76 6 63.57 4 61.57 4 60.31 4 63.62 5 62.24 13 7.04% 26 15.79%8 Goiás 64.85 9 60.13 9 56.98 10 51.01 10 50.57 9 46.81 11 46.93 11 45.13 9 7.85% 14 38.54%9 Mato Grosso do Sul 63.50 8 60.41 8 58.44 9 51.79 11 49.89 8 47.61 8 49.22 10 46.56 19 5.12% 19 33.38%

10 Mato Grosso 63.04 12 55.10 12 55.54 8 51.93 9 51.60 12 42.63 10 46.96 9 47.36 2 14.41% 11 47.88%11 Espírito Santo 60.41 10 59.10 11 55.76 11 50.39 12 47.22 11 43.67 12 46.00 14 42.11 24 2.22% 15 38.33%12 Rondônia 59.05 11 56.15 10 56.38 12 48.79 8 52.05 10 43.76 9 48.61 12 43.55 18 5.16% 17 34.94%13 Bahia 53.38 15 49.25 14 47.56 15 42.30 14 38.05 17 34.36 14 40.81 16 39.15 8 8.39% 7 55.36%14 Acre 52.23 14 49.64 13 48.13 17 40.24 16 35.96 13 40.95 13 41.40 13 42.33 17 5.22% 20 27.55%15 Amapá 51.42 16 48.02 15 46.68 13 45.32 17 35.08 15 37.41 18 35.74 8 53.86 12 7.08% 16 37.45%16 Roraima 51.28 17 45.11 17 44.66 20 34.84 22 29.51 14 40.59 20 32.81 15 41.43 3 13.68% 22 26.34%17 Pará 51.17 13 49.66 18 44.56 16 40.75 13 38.95 16 34.50 15 38.77 18 36.98 23 3.04% 10 48.32%18 Amazonas 50.22 18 45.07 16 45.30 14 42.54 15 37.77 20 31.38 19 34.85 20 36.66 7 11.43% 6 60.04%19 Tocantins 50.13 20 44.17 21 40.79 21 34.76 18 34.99 21 31.20 23 27.37 21 30.74 4 13.49% 5 60.67%20 Ceará 50.11 19 44.86 19 42.90 18 39.09 19 34.49 18 32.95 17 35.87 19 36.66 6 11.70% 8 52.08%21 Pernambuco 45.95 21 42.75 20 41.82 19 38.13 20 34.30 19 32.50 16 37.74 17 37.49 10 7.49% 12 41.38%22 Rio Grande do Norte 45.62 22 40.81 22 37.80 23 31.71 23 28.73 23 26.93 22 29.62 22 30.35 5 11.79% 4 69.40%23 Sergipe 43.05 23 40.77 23 37.05 22 34.27 21 33.37 22 30.85 21 31.45 23 27.52 16 5.59% 13 39.55%24 Paraíba 39.09 25 33.38 24 33.37 24 29.05 24 26.06 24 26.01 24 25.24 24 23.41 1 17.11% 9 50.29%25 Piauí 36.99 24 34.58 25 29.69 25 24.48 25 21.75 25 21.76 25 23.70 25 22.20 14 6.97% 3 69.99%26 Alagoas 32.25 26 30.42 27 25.50 26 21.71 26 19.37 26 18.90 27 19.12 26 19.90 15 6.02% 2 70.63%27 Maranhão 32.22 27 30.00 26 26.05 27 20.59 27 19.16 27 18.84 26 19.35 27 19.11 11 7.40% 1 71.02%


81

Ranking by Capitals and Metropolitan Peripheries** % Class ABC

% % % % % % % % Var (%) Var (%)


1 Florianópolis - SC 92.61 1 90.38 1 89.83 1 81.86 1 79.51 1 81.82 1 83.31 1 85.25 26 2.47% 34 13.19%2 Curitiba - PR 86.49 2 84.97 2 78.01 2 79.44 2 77.46 4 71.43 3 77.61 3 73.65 29 1.79% 28 21.08%3 Vitória - ES 80.07 3 82.18 3 76.76 6 69.14 3 73.91 2 73.57 2 79.02 5 67.95 36 -2.57% 36 8.84%4 Porto Alegre - RS 80.05 6 75.29 4 74.84 3 74.86 4 72.58 3 72.08 4 71.31 2 74.31 17 6.32% 35 11.06%5 Belo Horizonte - MG 78.61 5 75.60 5 73.82 5 71.30 7 65.63 7 62.00 6 68.55 6 65.70 22 3.98% 21 26.79%6 Goiânia - GO 77.31 4 77.82 7 71.27 7 67.11 6 67.58 6 63.68 7 66.55 8 63.64 33 -0.66% 27 21.40%7 Rio de Janeiro - RJ 75.32 12 67.90 6 73.18 4 71.32 5 70.86 5 66.24 5 70.15 4 68.50 7 10.93% 33 13.71%8 Cuiabá - MT 73.66 14 66.35 8 69.93 11 62.39 8 65.38 13 56.72 10 60.75 10 61.12 6 11.02% 18 29.87%9 São Paulo - SP 73.61 7 72.31 9 68.84 8 66.99 10 62.41 8 61.09 8 65.48 7 64.78 28 1.80% 29 20.49%

10 Brasília - DF 72.42 9 71.22 10 68.17 9 64.96 12 59.94 9 58.54 9 62.15 11 60.51 31 1.68% 25 23.71%11 Periferia de São Paulo - SP 72.21 13 66.50 14 65.35 12 61.44 13 58.33 11 57.64 12 60.21 12 59.95 13 8.59% 22 25.28%12 Campo Grande - MS 71.30 10 68.83 11 67.54 16 56.50 11 60.04 12 56.98 11 60.50 17 54.69 23 3.59% 23 25.13%13 Periferia de Curitiba - PR 70.95 8 71.88 15 63.00 13 59.36 14 57.97 16 51.70 17 54.65 14 56.08 35 -1.29% 15 37.23%14 Palmas - TO 70.87 18 59.95 17 59.87 14 57.64 17 53.82 19 47.47 24 43.51 15 54.97 2 18.22% 8 49.29%15 Periferia de Porto Alegre - RS 69.94 11 68.74 12 65.94 10 63.35 9 62.65 10 58.45 13 59.35 9 61.23 30 1.75% 32 19.66%16 Periferia de Belo Horizonte - MG 65.17 16 61.89 18 58.25 19 52.72 19 48.44 20 45.19 20 48.91 21 45.84 20 5.30% 13 44.21%17 Periferia do Rio de Janeiro - RJ 63.98 15 62.27 16 61.55 17 55.15 18 50.95 14 53.45 15 56.03 16 54.89 25 2.75% 31 19.70%18 Porto Velho - RO 63.80 17 60.95 13 65.67 15 57.39 15 57.73 15 52.00 14 56.67 18 52.12 21 4.68% 26 22.69%19 Aracaju - SE 62.75 20 58.43 21 54.54 18 54.44 16 54.52 17 50.68 18 51.91 24 44.10 14 7.39% 24 23.82%20 Natal - RN 62.13 21 57.99 20 56.33 20 51.33 20 47.07 24 40.81 19 50.74 20 50.91 15 7.14% 5 52.24%21 Manaus - AM 60.40 27 49.66 22 53.49 21 50.39 22 43.40 30 35.39 29 38.57 27 42.53 1 21.63% 3 70.67%22 Rio Branco - AC 59.79 19 59.79 19 57.54 23 46.39 21 43.70 18 49.66 16 54.73 19 51.80 32 0.00% 30 20.40%23 Teresina - PI 59.47 22 53.85 27 48.28 29 38.55 29 37.85 27 37.70 25 43.33 30 37.78 10 10.44% 4 57.75%24 João Pessoa - PB 58.20 25 50.65 23 50.03 25 45.35 23 42.78 22 42.42 21 47.89 23 44.59 4 14.91% 16 37.20%25 Salvador - BA 56.36 24 51.03 24 49.79 26 43.68 27 40.08 28 37.15 23 43.94 28 42.25 9 10.44% 6 51.71%26 Macapá - AP 55.58 26 49.72 25 49.58 22 47.18 28 39.05 21 43.39 28 40.91 13 59.26 5 11.79% 19 28.09%27 Fortaleza - CE 53.97 28 49.04 26 48.29 24 45.46 25 40.40 25 38.31 27 42.09 25 43.79 11 10.05% 14 40.88%28 Belém - PA 53.74 23 52.71 29 47.52 27 43.53 24 42.17 29 36.46 26 43.04 29 41.34 27 1.95% 10 47.39%29 Boa Vista - RR 53.06 29 47.96 28 47.75 30 38.42 31 33.00 23 41.73 31 34.94 22 45.04 8 10.63% 20 27.15%30 Recife - PE 50.40 30 47.78 30 46.97 28 43.16 26 40.30 26 37.81 22 45.10 26 43.19 18 5.48% 17 33.30%31 São Luís - MA 48.51 31 46.89 33 39.44 32 36.04 30 37.04 31 33.31 30 37.32 31 37.03 24 3.45% 12 45.63%32 Maceió - AL 47.61 32 44.62 31 44.10 33 35.49 35 29.13 32 32.45 32 34.85 32 35.17 16 6.70% 11 46.72%33 Periferia de Belém - PA 45.28 33 42.99 35 37.56 35 33.99 32 31.03 33 29.87 35 28.26 35 26.17 19 5.33% 7 51.59%34 Periferia de Recife - PE 42.89 35 39.29 34 38.25 34 34.67 34 29.99 34 28.91 33 32.17 33 33.45 12 9.16% 9 48.36%35 Periferia de Salvador - BA 42.42 34 42.72 32 39.51 31 37.25 33 30.50 35 24.12 34 28.41 34 26.56 34 -0.70% 2 75.87%36 Periferia de Fortaleza - CE 40.08 36 34.56 36 29.98 36 23.63 36 19.62 36 19.45 36 20.35 36 18.91 3 15.97% 1 106.07%

Source: CPS/FGV based on PNAD/IBGE microdata ** Capitals refer to the core of the metropolitan area; peripheries refer to the ensemble of cities/towns within a given metropolis, excluding the capital

82

1.2 ) % CLASS E

% Class E

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 16.02 18.26 19.32 22.80 25.40 28.12 26.66 27.54 -12.27% -43.03%

% Class E

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 19.07 22.37 22.51 26.32 29.48 35.92 34.41 33.21 -14.75% -46.91% Northeast 30.69 34.20 36.54 42.46 46.28 49.81 48.07 49.04 -10.26% -38.39% Southeast 9.68 11.60 11.95 14.16 16.69 18.40 16.71 17.74 -16.55% -47.39% South 7.29 8.03 8.85 11.27 12.18 13.77 14.03 15.36 -9.22% -47.06% Center 10.49 11.78 13.44 17.00 18.27 23.22 21.01 21.76 -10.95% -54.82%

% Class E

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 11.28 13.77 14.12 16.20 19.25 22.47 18.32 19.09 -18.08% -49.80% Metropolitan peripheries(Non- capital) 12.37 13.87 14.96 17.75 21.06 22.06 20.42 21.02 -10.81% -43.93% Non-metropolitan urban area 14.02 16.09 16.92 20.63 22.93 25.45 24.78 25.33 -12.87% -44.91% Rural area 34.82 37.30 40.16 45.20 47.71 51.45 51.73 53.51 -6.65% -32.32%


83

Ranking by State % Class E

% Classe E% % % % % % % % Var (%) Var (%)


1 Alagoas 38.76 2 37.93 1 44.44 1 50.12 1 53.88 1 57.66 1 54.97 1 54.27 3 2.19% 1 -32.78%2 Maranhão 33.75 1 38.30 2 44.23 2 49.00 2 53.15 2 55.68 2 52.99 2 53.53 14 -11.88% 8 -39.39%3 Piauí 32.38 3 37.05 3 40.08 3 46.47 3 48.55 3 52.01 3 51.62 3 51.12 16 -12.60% 5 -37.74%4 Paraíba 29.20 4 33.19 5 30.54 4 39.18 4 45.58 4 47.28 4 45.22 4 49.10 15 -12.02% 6 -38.24%5 Sergipe 26.56 6 28.59 4 30.84 6 35.81 7 36.91 6 41.58 6 39.82 5 43.83 8 -7.10% 2 -36.12%6 Rio Grande do Norte 24.37 5 28.73 6 29.57 7 35.03 6 39.74 5 43.37 5 40.21 6 41.83 19 -15.18% 10 -43.81%7 Pernambuco 24.09 8 25.06 7 28.25 8 32.19 8 36.55 7 39.21 10 34.20 10 33.98 5 -3.87% 7 -38.56%8 Amazonas 20.25 9 23.94 15 18.61 15 21.61 14 29.36 12 36.29 9 35.49 9 35.39 21 -15.41% 11 -44.20%9 Ceará 19.79 11 21.85 10 23.30 11 28.41 10 34.24 11 36.83 11 33.36 8 35.83 10 -9.43% 15 -46.27%

10 Tocantins 18.97 7 25.51 8 24.83 9 30.78 11 33.56 8 39.04 7 39.36 7 38.12 25 -25.64% 19 -51.41%11 Roraima 18.81 14 20.45 9 24.51 5 37.83 5 41.15 14 32.73 8 37.89 15 28.37 9 -8.02% 9 -42.53%12 Bahia 17.88 10 22.25 12 22.54 12 27.91 13 32.17 9 36.93 13 32.13 13 31.69 24 -19.64% 20 -51.58%13 Acre 17.81 13 21.02 13 21.62 10 29.06 12 33.26 15 32.53 12 32.21 12 31.74 20 -15.27% 12 -45.25%14 Pará 16.14 15 17.93 11 23.15 13 26.42 15 27.21 13 33.47 14 30.76 11 32.56 11 -9.98% 21 -51.78%15 Rondônia 12.88 16 15.10 18 14.42 16 20.86 21 16.57 18 23.93 17 23.09 16 25.49 18 -14.70% 14 -46.18%16 Amapá 12.57 12 21.72 14 20.25 14 24.46 9 35.12 10 36.86 15 29.99 23 15.49 27 -42.13% 27 -65.90%17 Espírito Santo 12.53 18 14.07 16 16.10 17 20.37 16 23.10 16 27.34 16 25.37 14 29.56 13 -10.95% 23 -54.17%18 Mato Grosso 11.55 19 13.70 17 15.48 19 17.58 18 18.75 17 26.93 18 22.53 18 22.25 22 -15.69% 26 -57.11%19 Mato Grosso do Sul 10.91 21 11.16 20 12.50 18 17.74 17 19.92 20 21.41 20 19.95 19 20.78 4 -2.24% 17 -49.04%20 Rio de Janeiro 10.65 17 14.71 23 11.49 23 13.07 24 15.50 24 16.72 26 12.08 22 15.59 26 -27.60% 3 -36.30%21 Goiás 10.25 20 11.86 19 13.48 20 17.46 19 17.82 19 23.25 19 22.10 17 23.70 17 -13.58% 24 -55.91%22 Distrito Federal 9.36 24 9.87 21 11.80 21 14.46 20 17.13 21 20.41 21 17.57 20 17.46 7 -5.17% 22 -54.14%23 Minas Gerais 9.27 25 9.76 24 11.24 22 13.20 22 16.47 22 17.67 22 15.87 21 16.50 6 -5.02% 16 -47.54%24 Rio Grande do Sul 9.01 23 10.03 25 10.59 25 11.38 25 12.46 25 14.24 24 13.46 26 12.25 12 -10.17% 4 -36.73%25 São Paulo 8.79 22 10.86 22 11.78 24 13.05 23 16.27 23 17.65 23 15.03 24 15.33 23 -19.06% 18 -50.20%26 Paraná 6.13 26 4.50 26 6.97 26 9.49 26 10.76 26 14.08 25 12.34 25 12.26 1 36.22% 25 -56.46%27 Santa Catarina 4.53 27 3.67 27 4.68 27 6.35 27 8.22 27 8.29 27 8.93 27 9.93 2 23.43% 13 -45.36%


84

Ranking by Capitals and Metropolitan Peripheries % Class E % Classe E

% % % % % % % % Var (%) Var (%)


1 Periferia de Recife - PE 26.38 2 26.75 3 29.97 3 33.52 3 39.59 5 41.47 5 38.10 6 35.90 10 -1.38% 2 -36.39%2 Maceió - AL 25.60 7 21.46 5 25.96 4 32.40 2 39.81 4 41.70 6 37.02 5 36.13 5 19.29% 6 -38.61%3 Periferia de Salvador - BA 25.22 5 22.01 7 25.33 5 32.26 4 37.89 1 47.69 4 39.42 3 38.47 6 14.58% 12 -47.12%4 Periferia de Fortaleza - CE 24.63 1 27.07 2 31.69 1 37.16 1 43.57 2 46.69 1 45.89 1 50.28 17 -9.01% 13 -47.25%5 Recife - PE 20.75 3 22.60 6 25.78 8 30.26 7 32.32 6 35.85 11 29.04 8 31.27 15 -8.19% 7 -42.12%6 Periferia de Belém - PA 18.58 6 21.70 4 26.55 7 30.64 12 29.73 7 35.25 2 40.97 2 41.39 23 -14.38% 14 -47.29%7 São Luís - MA 17.99 9 19.78 1 33.33 6 31.20 6 33.63 3 43.78 3 40.93 4 36.83 18 -9.05% 30 -58.91%8 Fortaleza - CE 17.93 10 19.73 12 19.81 12 24.79 11 30.54 9 32.92 12 28.33 10 30.04 19 -9.12% 9 -45.53%9 Boa Vista - RR 17.53 13 17.93 11 20.65 2 36.06 5 36.98 15 30.65 7 35.68 15 24.29 11 -2.23% 8 -42.81%

10 João Pessoa - PB 17.12 14 16.92 14 19.64 15 23.55 8 31.86 12 31.69 14 26.78 7 32.05 8 1.18% 11 -45.98%11 Salvador - BA 15.88 4 22.31 8 21.77 10 26.71 10 30.63 8 33.99 8 30.29 11 30.02 30 -28.82% 21 -53.28%12 Belém - PA 15.08 16 16.20 9 21.72 13 24.68 15 26.19 10 32.71 15 26.62 12 29.00 14 -6.91% 22 -53.90%13 Manaus - AM 13.73 8 20.16 22 12.10 23 13.56 17 22.83 13 31.46 9 29.68 13 28.59 33 -31.89% 27 -56.36%14 Natal - RN 12.77 12 18.47 19 14.23 16 20.13 13 27.01 16 30.46 21 19.99 19 20.27 32 -30.86% 29 -58.08%15 Rio Branco - AC 12.59 19 14.88 16 17.56 14 23.63 16 25.07 20 24.23 18 21.20 16 23.22 24 -15.39% 15 -48.04%16 Teresina - PI 11.81 17 16.07 10 21.36 9 27.55 14 26.41 11 32.02 13 28.19 9 30.71 27 -26.51% 32 -63.12%17 Porto Velho - RO 11.71 18 15.10 23 12.05 18 17.10 21 17.93 19 24.55 19 20.29 17 22.71 25 -22.45% 20 -52.30%18 Periferia do Rio de Janeiro - RJ 11.15 22 12.88 20 12.39 20 16.02 20 18.87 25 18.00 28 14.50 20 17.88 22 -13.43% 4 -38.06%19 Periferia de Belo Horizonte - MG 11.05 24 11.38 18 14.59 19 16.69 18 20.94 22 20.34 17 21.28 18 20.87 12 -2.90% 10 -45.67%20 Aracaju - SE 10.78 20 14.55 15 19.38 17 18.78 19 19.40 18 25.40 20 20.29 14 27.37 26 -25.91% 28 -57.56%21 Rio de Janeiro - RJ 10.18 15 16.34 27 10.71 30 10.36 29 12.54 29 15.62 33 10.00 29 13.63 34 -37.70% 1 -34.83%22 Macapá - AP 10.10 11 19.72 13 19.73 11 26.20 9 31.83 14 31.17 16 25.01 35 8.25 35 -48.78% 35 -67.60%23 Periferia de Porto Alegre - RS 9.81 27 9.91 25 11.32 26 12.60 27 13.37 30 15.47 25 15.22 27 13.85 9 -1.01% 3 -36.59%24 Brasília - DF 9.36 28 9.87 24 11.80 21 14.46 22 17.13 21 20.41 23 17.57 21 17.46 13 -5.17% 23 -54.14%25 São Paulo - SP 9.25 25 10.35 21 12.36 25 12.93 24 16.00 24 18.46 27 14.61 26 14.83 20 -10.63% 16 -49.89%26 Periferia de Curitiba - PR 9.09 33 6.18 28 9.54 27 11.78 26 14.81 23 18.71 22 17.88 22 16.88 2 47.09% 19 -51.42%27 Periferia de São Paulo - SP 8.18 23 11.52 26 11.03 24 13.20 23 16.62 27 16.55 24 15.60 24 16.02 31 -28.99% 18 -50.57%28 Campo Grande - MS 7.95 31 7.67 33 7.22 22 14.41 25 15.89 26 17.84 29 13.42 23 16.14 7 3.65% 26 -55.44%29 Porto Alegre - RS 7.40 26 10.28 30 9.13 32 9.00 33 10.72 34 11.99 31 10.29 33 9.51 29 -28.02% 5 -38.28%30 Belo Horizonte - MG 7.35 30 8.06 31 7.74 31 9.54 30 11.96 31 14.87 30 10.54 31 11.94 16 -8.81% 17 -50.57%31 Cuiabá - MT 7.26 29 8.35 29 9.36 33 8.94 31 11.87 28 15.93 26 15.13 25 15.34 21 -13.05% 24 -54.43%32 Palmas - TO 5.68 21 13.51 17 16.30 28 11.73 28 13.16 17 29.78 10 29.21 30 12.66 36 -57.96% 36 -80.93%33 Vitória - ES 5.45 35 2.77 34 6.85 29 10.98 32 11.56 33 11.99 35 7.73 28 13.65 1 96.75% 25 -54.55%34 Goiânia - GO 4.50 32 6.19 32 7.55 34 7.78 34 8.58 32 13.49 32 10.24 32 11.24 28 -27.30% 34 -66.64%35 Curitiba - PR 3.92 34 3.20 35 5.00 35 7.74 35 7.82 35 10.50 34 8.12 34 8.71 4 22.50% 31 -62.67%36 Florianópolis - SC 2.36 36 1.68 36 2.35 36 6.74 36 1.96 36 6.49 36 4.33 36 4.52 3 40.48% 33 -63.64%


85

1.3 ) % CLASS D

% Class D

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 24.35 25.11 26.35 27.06 27.16 26.73 26.39 26.08 -3.03% -8.90%

% Class D

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 31.94 32.55 34.49 33.91 33.62 31.76 30.72 31.19 -1.87% 0.57% Northeast 31.65 31.55 31.96 30.56 29.24 27.16 27.52 26.97 0.32% 16.53% Southeast 20.84 21.80 23.38 25.11 26.04 26.35 25.41 25.12 -4.40% -20.91% South 17.20 17.96 19.97 22.08 22.68 24.06 24.18 23.55 -4.23% -28.51% Center 23.90 27.13 27.61 29.15 29.38 28.65 28.95 29.65 -11.91% -16.58%

% Class D

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 20.24 21.41 22.38 23.49 23.57 22.91 22.64 22.75 -5.46% -11.65% Metropolitan peripheries(Non- capital) 23.66 25.47 26.59 28.21 28.16 28.47 27.48 27.27 -7.11% -16.89% Non-metropolitan urban area 24.91 25.45 27.24 27.64 28.25 28.24 27.62 27.42 -2.12% -11.79% Rural area 30.21 29.88 29.76 29.93 28.61 26.56 27.68 26.14 1.10% 13.74%


86

Ranking by State % Class D % Classe D

% % % % % % % % Var (%) Var (%)


1 Amapá 36.01 13 30.26 5 33.07 13 30.22 10 29.80 19 25.74 1 34.27 5 30.65 1 19.00% 1 39.90%2 Maranhão 34.03 6 31.70 15 29.72 11 30.41 19 27.69 20 25.47 16 27.66 18 27.36 3 7.35% 2 33.61%3 Pará 32.69 4 32.41 7 32.28 4 32.83 1 33.84 3 32.03 7 30.46 6 30.46 6 0.86% 11 2.06%4 Paraíba 31.70 2 33.43 2 36.08 6 31.76 18 28.37 15 26.71 10 29.54 17 27.49 16 -5.17% 4 18.68%5 Tocantins 30.90 12 30.31 3 34.38 2 34.47 5 31.46 8 29.76 2 33.26 3 31.14 5 1.95% 10 3.83%6 Piauí 30.64 18 28.37 11 30.23 18 29.05 13 29.70 18 26.23 21 24.69 19 26.68 2 8.00% 5 16.81%7 Sergipe 30.38 10 30.64 8 32.11 14 29.92 12 29.72 14 27.57 12 28.73 10 28.65 9 -0.85% 8 10.19%8 Ceará 30.10 3 33.29 4 33.80 5 32.51 7 31.27 6 30.22 5 30.77 16 27.50 21 -9.58% 14 -0.40%9 Rio Grande do Norte 30.02 11 30.46 6 32.63 3 33.26 4 31.54 9 29.71 8 30.17 15 27.82 10 -1.44% 12 1.04%

10 Pernambuco 29.97 5 32.19 13 29.93 16 29.67 17 29.15 13 28.30 15 28.06 11 28.53 18 -6.90% 9 5.90%11 Acre 29.95 14 29.35 10 30.25 8 30.69 8 30.77 17 26.52 18 26.38 20 25.93 4 2.04% 6 12.93%12 Roraima 29.90 1 34.43 9 30.83 20 27.32 16 29.35 16 26.69 11 29.31 8 30.20 25 -13.16% 7 12.03%13 Amazonas 29.53 9 30.99 1 36.09 1 35.85 2 32.87 2 32.32 9 29.66 13 27.94 15 -4.71% 16 -8.63%14 Alagoas 29.00 7 31.65 12 30.07 19 28.16 20 26.76 21 23.44 19 25.92 21 25.83 20 -8.37% 3 23.72%15 Bahia 28.74 16 28.50 14 29.90 15 29.79 11 29.78 12 28.71 17 27.06 9 29.16 7 0.84% 13 0.10%16 Rondônia 28.08 15 28.75 17 29.20 12 30.35 6 31.38 1 32.32 14 28.30 4 30.97 11 -2.33% 17 -13.12%17 Espírito Santo 27.05 20 26.84 20 28.15 17 29.23 14 29.68 10 28.99 13 28.62 12 28.34 8 0.78% 15 -6.69%18 Mato Grosso do Sul 25.59 17 28.42 18 29.07 10 30.47 9 30.19 4 30.98 4 30.82 1 32.66 22 -9.96% 22 -17.40%19 Mato Grosso 25.41 8 31.20 19 28.98 9 30.49 15 29.66 5 30.44 6 30.51 7 30.39 27 -18.56% 20 -16.52%20 Goiás 24.89 19 28.01 16 29.54 7 31.53 3 31.61 7 29.95 3 30.96 2 31.17 23 -11.14% 21 -16.89%21 Rio de Janeiro 19.52 22 20.05 24 20.75 22 23.36 23 22.94 23 22.97 22 24.30 22 22.17 12 -2.64% 19 -15.02%22 Minas Gerais 19.09 21 21.68 21 22.90 21 25.00 21 26.53 11 28.93 20 25.32 14 27.94 24 -11.95% 25 -34.01%23 Distrito Federal 18.22 25 18.92 26 20.03 25 20.58 24 22.92 26 21.04 26 20.29 23 22.02 13 -3.70% 18 -13.40%24 São Paulo 18.21 23 19.36 23 20.88 23 22.37 22 23.12 24 22.71 24 21.70 24 21.94 17 -5.94% 23 -19.82%25 Rio Grande do Sul 17.70 24 19.06 25 20.50 24 21.36 25 21.48 25 22.49 23 22.90 26 21.69 19 -7.14% 24 -21.30%26 Paraná 14.01 26 16.23 22 21.53 26 19.77 26 19.97 22 23.09 27 19.98 25 21.74 26 -13.68% 27 -39.32%27 Santa Catarina 13.15 27 13.67 27 14.41 27 17.67 27 18.37 27 20.12 25 20.91 27 21.37 14 -3.80% 26 -34.64%


87

Ranking by Capitals and Metropolitan Peripheries % Class D % Classe D

% % % % % % % % Var (%) Var (%)


1 Periferia de Belém - PA 36.14 2 35.30 2 35.89 3 35.36 1 39.24 1 34.88 5 30.77 4 32.45 6 2.38% 12 3.61%2 Periferia de Fortaleza - CE 35.29 1 38.37 1 38.33 1 39.21 2 36.81 3 33.86 2 33.76 7 30.82 18 -8.03% 9 4.22%3 Macapá - AP 34.32 11 30.56 9 30.70 22 26.62 16 29.13 22 25.45 1 34.08 3 32.49 2 12.30% 2 34.85%4 São Luís - MA 33.50 7 33.33 17 27.22 5 32.75 14 29.33 27 22.91 28 21.74 20 26.14 8 0.51% 1 46.22%5 Periferia de Salvador - BA 32.36 3 35.27 3 35.17 12 30.49 7 31.61 13 28.19 3 32.17 1 34.97 20 -8.25% 3 14.79%6 Belém - PA 31.19 10 31.08 8 30.76 8 31.79 6 31.64 5 30.83 6 30.34 10 29.66 9 0.35% 14 1.17%7 Periferia de Recife - PE 30.74 5 33.96 6 31.79 7 31.81 11 30.42 7 29.62 8 29.73 9 30.64 22 -9.48% 10 3.78%8 Boa Vista - RR 29.40 4 34.12 7 31.60 24 25.53 13 30.02 14 27.63 11 29.39 8 30.68 29 -13.83% 6 6.41%9 Recife - PE 28.85 14 29.62 16 27.25 23 26.58 18 27.38 16 26.34 19 25.86 21 25.55 13 -2.60% 5 9.53%

10 Teresina - PI 28.72 13 30.08 10 30.36 4 33.90 3 35.75 6 30.28 13 28.49 6 31.51 16 -4.52% 19 -5.15%11 Fortaleza - CE 28.10 9 31.23 5 31.91 14 29.74 17 29.07 10 28.77 9 29.58 19 26.18 23 -10.02% 16 -2.33%12 Salvador - BA 27.76 17 26.66 14 28.44 15 29.61 15 29.29 9 28.85 20 25.77 16 27.73 5 4.13% 17 -3.78%13 Rio Branco - AC 27.63 19 25.33 22 24.91 13 29.97 8 31.23 17 26.11 25 24.06 24 24.98 3 9.08% 7 5.82%14 Maceió - AL 26.80 6 33.92 12 29.93 6 32.10 9 31.06 20 25.84 14 28.12 14 28.70 33 -20.99% 11 3.72%15 Aracaju - SE 26.46 15 27.02 19 26.09 21 26.77 20 26.09 24 23.92 15 27.80 15 28.52 12 -2.07% 4 10.62%16 Manaus - AM 25.88 12 30.18 4 34.41 2 36.05 4 33.77 4 33.15 4 31.75 12 28.88 31 -14.25% 28 -21.93%17 Natal - RN 25.10 23 23.54 13 29.44 20 28.54 21 25.92 11 28.73 12 29.27 13 28.82 4 6.63% 20 -12.63%18 Periferia do Rio de Janeiro - RJ 24.86 21 24.86 20 26.06 18 28.83 12 30.17 12 28.55 10 29.48 17 27.23 10 0.00% 21 -12.92%19 João Pessoa - PB 24.68 8 32.43 11 30.33 9 31.11 22 25.37 19 25.89 22 25.34 28 23.35 34 -23.90% 18 -4.67%20 Porto Velho - RO 24.50 22 23.95 26 22.29 25 25.51 24 24.35 25 23.45 27 23.04 22 25.17 7 2.30% 8 4.48%21 Periferia de Belo Horizonte - MG 23.78 16 26.73 18 27.16 11 30.59 10 30.62 2 34.47 7 29.81 2 33.29 25 -11.04% 32 -31.01%22 Palmas - TO 23.46 18 26.54 23 23.83 10 30.63 5 33.02 29 22.75 17 27.28 5 32.37 26 -11.61% 13 3.12%23 Campo Grande - MS 20.76 24 23.50 21 25.24 16 29.09 25 24.07 23 25.18 18 26.07 11 29.17 27 -11.66% 24 -17.55%24 Periferia de Porto Alegre - RS 20.26 27 21.35 25 22.74 28 24.04 26 23.98 18 26.08 21 25.42 25 24.92 17 -5.11% 29 -22.32%25 Periferia de Curitiba - PR 19.95 26 21.94 15 27.46 17 28.87 19 27.22 8 29.58 16 27.48 18 27.05 21 -9.07% 33 -32.56%26 Periferia de São Paulo - SP 19.60 25 21.98 24 23.61 26 25.36 23 25.05 21 25.81 23 24.19 26 24.03 24 -10.83% 30 -24.06%27 Cuiabá - MT 19.07 20 25.31 28 20.71 19 28.67 29 22.75 15 27.35 24 24.12 27 23.54 35 -24.65% 31 -30.27%28 Brasília - DF 18.22 28 18.92 29 20.03 29 20.58 28 22.92 30 21.04 30 20.29 30 22.02 14 -3.70% 22 -13.40%29 Goiânia - GO 18.19 31 15.99 27 21.18 27 25.11 27 23.84 28 22.83 26 23.21 23 25.12 1 13.76% 26 -20.32%30 São Paulo - SP 17.15 29 17.33 30 18.81 30 20.08 31 21.59 31 20.45 31 19.91 31 20.39 11 -1.04% 23 -16.14%31 Rio de Janeiro - RJ 14.50 32 15.77 34 16.11 33 18.32 34 16.60 32 18.15 32 19.86 33 17.87 19 -8.05% 25 -20.11%32 Vitória - ES 14.48 33 15.05 33 16.40 31 19.88 36 14.53 35 14.45 35 13.25 32 18.40 15 -3.79% 15 0.21%33 Belo Horizonte - MG 14.04 30 16.35 31 18.45 32 19.15 30 22.41 26 23.12 29 20.90 29 22.36 30 -14.13% 34 -39.27%34 Porto Alegre - RS 12.55 34 14.43 35 16.03 34 16.14 33 16.70 34 15.94 33 18.39 35 16.17 28 -13.03% 27 -21.27%35 Curitiba - PR 9.59 35 11.83 32 16.99 35 12.83 35 14.71 33 18.08 34 14.27 34 17.64 32 -18.93% 35 -46.96%36 Florianópolis - SC 5.03 36 7.94 36 7.82 36 11.40 32 18.54 36 11.69 36 12.36 36 10.22 36 -36.65% 36 -56.97%


88

1.4 ) % CLASS C % Class C

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 49.22 46.90 44.94 41.81 39.73 37.56 38.64 38.07 4.95% 31.04%

% Class C

% % % % % % % % Var (%) Var (%)


% Class C

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 50.50 47.70 46.63 44.94 42.79 40.44 43.40 42.86 5.87% 24.88% Metropolitan peripheries(Non- capital) 54.90 52.41 51.17 47.08 44.62 43.39 45.04 44.48 4.75% 26.53% Non-metropolitan urban area 51.69 49.84 47.31 44.50 42.11 39.72 40.45 39.98 3.71% 30.14% Rural area 32.76 30.40 28.27 23.20 22.28 20.58 19.45 18.91 7.76% 59.18%


89

Ranking by State % class C

% % % % % % % % Var (%) Var (%)


1 Santa Catarina 65.37 1 67.40 1 64.68 1 62.95 1 62.29 1 59.84 1 59.50 1 57.35 27 -3.01% 27 9.24%2 Paraná 61.45 2 60.70 2 57.02 2 55.41 2 55.94 2 51.13 2 53.18 2 52.49 26 1.24% 24 20.18%3 São Paulo 57.58 4 54.29 4 53.03 4 51.06 4 48.82 5 46.77 5 49.11 5 48.18 14 6.06% 21 23.11%4 Rio Grande do Sul 57.46 3 56.42 3 54.11 3 52.45 3 53.10 3 49.72 3 50.11 3 51.55 25 1.84% 25 15.57%5 Minas Gerais 56.73 5 54.11 6 51.88 6 50.03 6 46.45 6 42.41 6 47.23 7 44.09 17 4.84% 17 33.77%6 Goiás 54.87 7 51.31 7 49.34 8 43.96 8 43.69 7 40.81 9 40.09 10 38.67 13 6.94% 16 34.45%7 Rio de Janeiro 53.48 6 51.43 5 52.50 5 50.39 5 48.01 4 47.27 4 49.54 4 48.68 21 3.99% 26 13.14%8 Mato Grosso 52.31 11 47.01 9 48.36 7 44.18 9 43.39 13 35.59 11 38.99 9 39.50 8 11.27% 9 46.98%9 Mato Grosso do Sul 51.73 9 49.28 8 49.10 9 43.70 10 42.75 8 40.35 8 40.77 11 38.23 16 4.97% 19 28.20%

10 Espírito Santo 51.22 8 50.07 10 47.05 10 42.02 11 38.91 12 36.02 12 37.60 14 34.23 24 2.30% 13 42.20%11 Rondônia 50.56 10 48.39 11 46.12 12 41.63 7 44.67 9 38.06 7 41.61 12 37.31 20 4.48% 18 32.84%12 Amapá 46.32 14 41.29 13 41.83 13 38.61 15 31.93 15 32.08 16 31.05 6 47.66 5 12.18% 12 44.39%13 Distrito Federal 45.94 12 43.82 12 43.51 11 41.87 12 38.77 10 37.99 10 39.75 8 39.93 18 4.84% 22 20.93%14 Amazonas 44.21 15 39.87 14 39.73 14 37.78 13 33.25 20 27.17 17 30.16 16 32.23 9 10.89% 5 62.72%15 Roraima 44.04 17 38.65 18 37.58 20 30.36 22 26.03 11 36.58 20 27.66 13 36.11 3 13.95% 23 20.39%16 Acre 43.10 19 37.52 16 38.07 17 33.02 19 28.25 14 34.26 13 32.79 15 33.06 2 14.87% 20 25.80%17 Pará 42.89 13 41.36 17 37.67 15 34.89 14 33.14 16 29.53 15 31.90 18 30.89 22 3.70% 11 45.24%18 Tocantins 42.71 18 38.12 19 36.43 19 30.38 17 30.48 17 28.08 23 23.62 21 26.63 6 12.04% 8 52.10%19 Bahia 42.59 16 39.73 15 38.43 16 34.12 16 31.26 18 27.51 14 32.01 17 31.36 11 7.20% 6 54.82%20 Ceará 41.73 20 37.49 20 35.60 18 32.19 20 27.93 19 27.39 19 29.00 19 29.85 7 11.31% 7 52.35%21 Rio Grande do Norte 39.64 23 35.04 22 32.59 23 26.91 23 25.03 23 23.78 22 25.23 22 26.23 4 13.13% 4 66.69%22 Pernambuco 37.59 21 35.86 21 34.10 22 30.10 21 26.68 21 26.85 18 29.62 20 29.35 19 4.82% 15 40.00%23 Sergipe 37.46 22 35.45 23 32.21 21 30.36 18 28.50 22 26.51 21 27.34 23 23.42 15 5.67% 14 41.31%24 Paraíba 33.32 25 28.11 24 28.63 24 24.95 24 22.19 24 22.94 24 21.66 24 20.35 1 18.53% 10 45.25%25 Piauí 31.93 24 29.85 25 26.10 25 21.53 25 18.70 25 19.04 25 20.67 25 19.42 12 6.97% 3 67.70%26 Maranhão 29.14 26 27.11 26 23.14 26 18.91 26 16.95 26 16.98 26 17.63 27 16.76 10 7.49% 2 71.61%27 Alagoas 27.73 27 26.87 27 21.44 27 18.86 27 16.94 27 15.94 27 16.47 26 17.15 23 3.20% 1 73.96%


90

Ranking by Capitals and Metropolitan Peripheries % Class C % Classe C

% % % % % % % % Var (%) Var (%)


1 Periferia de Curitiba - PR 64.05 1 64.58 1 57.35 2 54.87 4 53.93 6 47.58 8 49.25 4 51.99 32 -0.82% 15 34.62%2 Periferia de São Paulo - SP 60.16 6 55.11 5 55.95 5 51.88 7 49.99 5 48.87 6 49.82 8 49.36 11 9.16% 22 23.10%3 Periferia de Porto Alegre - RS 59.77 2 58.95 3 56.91 3 54.43 3 55.32 3 51.46 3 52.12 2 53.77 29 1.39% 28 16.15%4 Curitiba - PR 59.51 4 57.71 4 56.77 1 55.83 2 57.40 1 53.87 2 56.17 3 52.88 25 3.12% 34 10.47%5 Periferia de Belo Horizonte - MG 58.20 5 55.51 9 53.13 12 48.52 14 44.93 15 41.50 15 44.64 15 41.23 18 4.85% 12 40.24%6 Florianópolis - SC 56.13 8 54.81 8 53.37 7 50.56 1 57.58 2 52.38 1 59.04 1 59.96 28 2.41% 36 7.16%7 Goiânia - GO 55.64 3 57.73 2 57.10 9 50.08 5 51.43 4 49.96 4 50.97 6 50.59 35 -3.62% 33 11.37%8 São Paulo - SP 55.60 9 53.66 12 50.81 8 50.43 12 47.88 11 45.24 10 48.61 11 47.31 24 3.62% 23 22.90%9 Belo Horizonte - MG 55.15 10 52.63 13 50.58 6 51.61 11 47.98 13 43.36 7 49.78 12 47.06 20 4.79% 20 27.19%

10 Periferia do Rio de Janeiro - RJ 54.71 7 54.81 7 54.02 13 48.38 15 44.36 8 47.00 9 49.18 9 47.81 30 -0.18% 27 16.40%11 Cuiabá - MT 54.36 13 50.44 6 54.55 10 48.65 9 48.79 17 40.47 16 43.66 13 44.85 15 7.77% 16 34.32%12 Porto Alegre - RS 52.80 12 51.29 14 48.53 11 48.59 8 48.85 9 46.57 13 46.49 10 47.75 26 2.94% 31 13.38%13 Manaus - AM 52.33 21 43.16 15 47.06 16 44.17 20 37.51 26 30.26 28 32.68 23 36.81 1 21.25% 2 72.93%14 Rio de Janeiro - RJ 52.33 16 48.42 11 51.18 4 52.23 6 51.20 7 47.51 5 49.85 7 49.42 14 8.08% 35 10.15%15 Campo Grande - MS 52.30 14 50.39 10 52.56 14 45.13 10 48.41 10 46.11 14 46.22 16 41.20 23 3.79% 30 13.42%16 Porto Velho - RO 50.88 15 48.98 16 45.92 15 44.56 13 47.87 14 42.92 12 46.68 18 40.80 21 3.88% 26 18.55%17 Vitória - ES 50.56 11 51.73 17 44.91 17 43.47 16 43.28 12 45.16 11 47.95 22 38.13 34 -2.26% 32 11.96%18 Palmas - TO 50.43 19 44.27 20 43.43 18 43.20 19 38.66 18 39.04 26 33.48 14 44.32 6 13.91% 19 29.18%19 Macapá - AP 49.48 24 41.84 21 43.36 22 39.36 22 34.76 19 38.07 23 34.41 5 51.96 2 18.26% 18 29.97%20 Natal - RN 49.36 17 46.28 18 44.83 21 39.88 21 37.22 24 33.29 20 38.97 17 40.81 16 6.66% 7 48.27%21 Aracaju - SE 49.23 18 44.86 23 40.56 19 42.86 17 39.22 21 37.82 18 40.14 27 33.29 9 9.74% 17 30.17%22 Rio Branco - AC 46.60 22 42.76 22 42.30 23 37.19 24 32.88 16 40.69 17 42.46 20 38.76 12 8.98% 29 14.52%23 Teresina - PI 46.50 27 40.13 27 38.65 32 30.23 28 30.03 28 29.73 22 34.44 29 30.78 4 15.87% 4 56.41%24 Brasília - DF 45.94 20 43.82 19 43.51 20 41.87 18 38.77 20 37.99 19 39.75 19 39.93 19 4.84% 24 20.93%25 Boa Vista - RR 45.02 25 41.22 24 40.20 29 33.21 30 29.16 22 37.35 31 28.98 21 38.72 10 9.22% 25 20.54%26 Salvador - BA 44.28 26 40.14 25 39.40 27 34.36 25 32.24 29 29.03 25 33.88 28 33.28 7 10.31% 5 52.53%27 Belém - PA 43.56 23 42.52 26 38.67 25 36.03 23 34.53 27 30.03 24 34.40 26 33.78 27 2.45% 10 45.05%28 João Pessoa - PB 43.55 33 36.94 29 36.40 26 35.29 26 31.86 23 34.43 21 36.48 24 34.95 3 17.89% 21 26.49%29 Fortaleza - CE 43.44 29 39.48 28 38.65 24 36.20 27 31.78 25 31.07 27 32.91 25 34.72 8 10.03% 13 39.81%30 Periferia de Belém - PA 41.37 30 38.81 30 35.30 30 32.13 29 29.70 30 28.36 34 25.75 34 23.72 17 6.60% 9 45.87%31 São Luís - MA 39.36 28 39.76 35 30.83 33 29.94 32 27.50 32 26.72 30 30.63 30 30.32 33 -1.01% 8 47.31%32 Periferia de Recife - PE 37.91 35 34.88 33 33.39 34 29.91 34 26.20 33 26.40 32 28.47 32 28.92 13 8.69% 11 43.60%33 Periferia de Fortaleza - CE 37.28 36 32.61 36 28.30 36 22.46 36 18.24 36 18.11 36 19.25 36 17.72 5 14.32% 1 105.85%34 Recife - PE 37.13 32 37.28 31 35.12 31 30.38 33 27.36 31 27.52 29 31.14 31 29.95 31 -0.40% 14 34.92%35 Maceió - AL 36.83 34 35.46 34 32.77 35 28.04 35 22.17 34 24.52 33 27.36 33 27.58 22 3.86% 6 50.20%36 Periferia de Salvador - BA 36.35 31 38.21 32 34.93 28 33.23 31 27.62 35 21.94 35 24.59 35 23.56 36 -4.87% 3 65.68%


91

1.5 ) % CLASSE AB % Class AB

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 10.42 9.73 9.40 8.32 7.71 7.60 8.31 8.31 7.09% 37.11%

% Class AB

% % % % % % % % Var (%) Var (%)


% Class AB

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 17.98 17.12 16.86 15.38 14.39 14.18 15.64 15.30 5.02% 26.80% Metropolitan peripheries(Non- capital) 9.07 8.25 7.28 6.96 6.16 6.08 7.06 7.23 9.94% 49.18% Non-metropolitan urban area 9.38 8.63 8.52 7.22 6.71 6.59 7.15 7.26 8.69% 42.34% Rural area 2.20 2.43 1.81 1.67 1.39 1.40 1.15 1.43 -9.47% 57.14%


92

Ranking by State % Class AB % Classe AB

% % % % % % % % Var (%) Var (%)


1 Distrito Federal 26.48 1 27.40 1 24.66 1 23.09 1 21.17 1 20.55 1 22.40 1 20.58 25 -3.36% 21 28.86%2 Paraná 18.40 2 18.58 5 14.48 2 15.34 3 13.33 6 11.70 2 14.50 5 13.51 24 -0.97% 11 57.26%3 Santa Catarina 16.95 4 15.25 2 16.23 6 13.04 6 11.13 5 11.74 7 10.66 7 11.35 11 11.15% 17 44.38%4 Rio de Janeiro 16.35 7 13.81 3 15.26 5 13.18 2 13.56 3 13.04 4 14.08 4 13.56 5 18.39% 23 25.38%5 Rio Grande do Sul 15.83 5 14.48 4 14.80 3 14.80 4 12.96 2 13.55 5 13.54 3 14.51 14 9.32% 26 16.83%6 São Paulo 15.42 3 15.49 6 14.31 4 13.52 5 11.79 4 12.87 3 14.16 2 14.55 23 -0.45% 25 19.81%7 Minas Gerais 14.91 6 14.46 7 13.97 7 11.76 7 10.55 7 10.99 6 11.59 6 11.47 20 3.11% 20 35.67%8 Mato Grosso do Sul 11.77 9 11.13 10 9.34 10 8.09 13 7.14 9 7.26 10 8.45 9 8.33 17 5.75% 9 62.12%9 Bahia 10.79 10 9.52 11 9.13 9 8.18 15 6.79 11 6.85 8 8.80 13 7.79 8 13.34% 10 57.52%

10 Mato Grosso 10.73 14 8.09 16 7.18 12 7.75 9 8.21 10 7.04 13 7.97 12 7.86 1 32.63% 13 52.41%11 Goiás 9.98 12 8.82 14 7.64 15 7.05 14 6.88 13 6.00 17 6.84 15 6.46 9 13.15% 7 66.33%12 Espírito Santo 9.19 11 9.03 12 8.71 8 8.37 8 8.31 8 7.65 11 8.40 11 7.88 21 1.77% 24 20.13%13 Acre 9.13 8 12.12 9 10.06 13 7.22 10 7.71 12 6.69 9 8.61 8 9.27 27 -24.67% 19 36.47%14 Rondônia 8.49 15 7.76 8 10.26 14 7.16 12 7.38 14 5.70 14 7.00 16 6.24 13 9.41% 15 48.95%15 Ceará 8.38 16 7.37 15 7.30 16 6.90 16 6.56 16 5.56 16 6.87 14 6.81 7 13.70% 14 50.72%16 Pernambuco 8.36 17 6.89 13 7.72 11 8.03 11 7.62 15 5.65 12 8.12 10 8.14 4 21.34% 16 47.96%17 Pará 8.28 13 8.30 18 6.89 18 5.86 17 5.81 18 4.97 15 6.87 18 6.09 22 -0.24% 6 66.60%18 Tocantins 7.42 20 6.05 24 4.36 22 4.38 20 4.51 23 3.12 23 3.75 22 4.11 3 22.64% 1 137.82%19 Roraima 7.24 19 6.46 17 7.08 21 4.48 23 3.48 21 4.01 18 5.15 19 5.32 10 12.07% 5 80.55%20 Amazonas 6.01 24 5.20 19 5.57 20 4.76 19 4.52 20 4.21 19 4.69 20 4.43 6 15.58% 18 42.76%21 Rio Grande do Norte 5.98 21 5.77 20 5.21 19 4.80 22 3.70 22 3.15 21 4.39 21 4.12 19 3.64% 2 89.84%22 Paraíba 5.77 23 5.27 23 4.74 23 4.10 21 3.87 24 3.07 24 3.58 24 3.06 12 9.49% 3 87.95%23 Sergipe 5.59 22 5.32 22 4.84 24 3.91 18 4.87 19 4.34 22 4.11 23 4.10 18 5.08% 22 28.80%24 Amapá 5.10 18 6.73 21 4.85 17 6.71 24 3.15 17 5.33 20 4.69 17 6.20 26 -24.22% 27 -4.32%25 Piauí 5.06 25 4.73 26 3.59 25 2.95 25 3.05 26 2.72 25 3.03 25 2.78 15 6.98% 4 86.03%26 Alagoas 4.52 26 3.55 25 4.06 26 2.85 26 2.43 25 2.96 26 2.65 26 2.75 2 27.32% 12 52.70%27 Maranhão 3.08 27 2.89 27 2.91 27 1.68 27 2.21 27 1.86 27 1.72 27 2.35 16 6.57% 8 65.59%


93

Ranking by Capitals and Metropolitan Peripheries % Class AB

% % % % % % % % Var (%) Var (%)


1 Florianópolis - SC 36.48 1 35.57 1 36.46 1 31.30 3 21.93 1 29.44 3 24.27 3 25.29 24 2.56% 29 23.91%2 Vitória - ES 29.51 2 30.45 2 31.85 3 25.67 1 30.63 2 28.41 1 31.07 1 29.82 29 -3.09% 36 3.87%3 Porto Alegre - RS 27.25 5 24.00 3 26.31 2 26.27 2 23.73 3 25.51 2 24.82 2 26.56 12 13.54% 34 6.82%4 Curitiba - PR 26.98 4 27.26 7 21.24 4 23.61 5 20.06 7 17.56 5 21.44 4 20.77 28 -1.03% 16 53.64%5 Brasília - DF 26.48 3 27.40 4 24.66 5 23.09 4 21.17 4 20.55 4 22.40 5 20.58 30 -3.36% 27 28.86%6 Belo Horizonte - MG 23.46 6 22.97 5 23.24 6 19.69 7 17.65 6 18.64 7 18.77 7 18.64 25 2.13% 28 25.86%7 Rio de Janeiro - RJ 22.99 8 19.48 6 22.00 7 19.09 6 19.66 5 18.73 6 20.30 6 19.08 10 18.02% 30 22.74%8 Goiânia - GO 21.67 7 20.09 14 14.17 8 17.03 9 16.15 10 13.72 10 15.58 12 13.05 19 7.86% 14 57.94%9 Palmas - TO 20.44 13 15.68 10 16.44 10 14.44 11 15.16 17 8.43 19 10.03 16 10.65 3 30.36% 3 142.47%

10 Cuiabá - MT 19.30 12 15.91 11 15.38 11 13.74 8 16.59 8 16.25 8 17.09 9 16.27 8 21.31% 31 18.77%11 Campo Grande - MS 19.00 10 18.44 13 14.98 16 11.37 14 11.63 12 10.87 11 14.28 10 13.49 23 3.04% 9 74.79%12 São Paulo - SP 18.01 9 18.65 9 18.03 9 16.56 12 14.53 9 15.85 9 16.87 8 17.47 31 -3.43% 33 13.63%13 João Pessoa - PB 14.65 15 13.71 16 13.63 17 10.06 15 10.92 19 7.99 16 11.41 19 9.64 20 6.86% 8 83.35%14 Aracaju - SE 13.52 16 13.57 15 13.98 14 11.58 10 15.30 11 12.86 15 11.77 15 10.81 27 -0.37% 35 5.13%15 Recife - PE 13.27 21 10.50 17 11.85 13 12.78 13 12.94 13 10.29 12 13.96 11 13.24 5 26.38% 26 28.96%16 Rio Branco - AC 13.19 11 17.03 12 15.24 21 9.20 16 10.82 15 8.97 13 12.27 13 13.04 35 -22.55% 18 47.05%17 Teresina - PI 12.97 14 13.72 22 9.63 23 8.32 23 7.82 20 7.97 22 8.89 27 7.00 32 -5.47% 12 62.74%18 Porto Velho - RO 12.92 17 11.97 8 19.75 12 12.83 17 9.86 14 9.08 20 9.99 14 11.32 18 7.94% 22 42.29%19 Natal - RN 12.77 18 11.71 18 11.50 15 11.45 18 9.85 22 7.52 14 11.77 18 10.10 17 9.05% 10 69.81%20 Salvador - BA 12.08 20 10.89 20 10.39 19 9.32 22 7.84 18 8.12 18 10.06 21 8.97 14 10.93% 17 48.77%21 Periferia de São Paulo - SP 12.05 19 11.39 23 9.40 18 9.56 21 8.34 16 8.77 17 10.39 17 10.59 21 5.79% 24 37.40%22 Maceió - AL 10.78 25 9.16 19 11.33 26 7.45 26 6.96 21 7.93 24 7.49 22 7.59 11 17.69% 25 35.94%23 Fortaleza - CE 10.53 24 9.56 21 9.64 20 9.26 20 8.62 23 7.24 21 9.18 20 9.07 15 10.15% 20 45.44%24 Belém - PA 10.18 22 10.19 25 8.85 25 7.50 24 7.64 27 6.43 23 8.64 23 7.56 26 -0.10% 13 58.32%25 Periferia de Porto Alegre - RS 10.17 23 9.79 24 9.03 22 8.92 25 7.33 24 6.99 25 7.23 24 7.46 22 3.88% 19 45.49%26 Periferia do Rio de Janeiro - RJ 9.27 27 7.46 28 7.53 27 6.77 27 6.59 26 6.45 26 6.85 26 7.08 6 24.26% 21 43.72%27 São Luís - MA 9.15 29 7.13 26 8.61 29 6.10 19 9.54 25 6.59 27 6.69 28 6.71 4 28.33% 23 38.85%28 Manaus - AM 8.07 31 6.50 29 6.43 28 6.22 28 5.89 29 5.13 30 5.89 30 5.72 7 24.15% 15 57.31%29 Boa Vista - RR 8.04 30 6.74 27 7.55 30 5.21 31 3.84 30 4.38 29 5.96 29 6.32 9 19.29% 7 83.56%30 Periferia de Belo Horizonte - MG 6.97 32 6.38 32 5.12 33 4.20 33 3.51 32 3.69 32 4.27 31 4.61 16 9.25% 6 88.89%31 Periferia de Curitiba - PR 6.90 28 7.30 31 5.65 32 4.49 30 4.04 31 4.12 31 5.40 33 4.09 33 -5.48% 11 67.48%32 Macapá - AP 6.10 26 7.88 30 6.22 24 7.82 29 4.29 28 5.32 28 6.50 25 7.30 36 -22.59% 32 14.66%33 Periferia de Salvador - BA 6.07 33 4.51 34 4.58 34 4.02 34 2.88 34 2.18 33 3.82 34 3.00 2 34.59% 1 178.44%34 Periferia de Recife - PE 4.98 34 4.41 33 4.86 31 4.76 32 3.79 33 2.51 34 3.70 32 4.53 13 12.93% 5 98.41%35 Periferia de Belém - PA 3.91 35 4.18 35 2.26 35 1.86 36 1.33 35 1.51 35 2.51 35 2.45 34 -6.46% 2 158.94%36 Periferia de Fortaleza - CE 2.80 36 1.95 36 1.68 36 1.17 35 1.38 36 1.34 36 1.10 36 1.19 1 43.59% 4 108.96%


94

2) Household Per Capita Income


R$ R$ R$ R$ R$ R$ R$ R$ Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 592.12 561.30 549.02 503.06 472.10 458.14 486.74 485.60 5.49% 29.24%


R$ R$ R$ R$ R$ R$ R$ R$ Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 429.99 414.81 401.62 368.61 351.20 317.59 351.70 355.14 3.66% 35.39%

Northeast 357.99 332.09 322.03 283.25 267.70 251.20 268.32 262.39 7.80% 42.51% Southeast 708.82 672.61 673.49 620.28 572.95 572.09 611.19 613.07 5.38% 23.90%

South 720.09 690.62 657.89 610.41 589.84 560.42 565.89 571.05 4.27% 28.49%

Center 713.74 675.41 615.77 572.36 541.61 504.24 554.91 532.66 5.68% 41.55%


R$ R$ R$ R$ R$ R$ R$ R$ Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 848.90 809.75 798.10 750.21 689.07 666.10 743.13 738.76 4.83% 27.44% Metropolitan peripheries(Non- capital) 568.01 542.08 521.56 479.99 447.70 443.86 470.97 485.20 4.78% 27.97%

Non-metropolitan urban area 561.57 529.43 521.46 472.46 448.82 435.27 452.30 450.17 6.07% 29.02%

Rural area 276.83 267.72 249.53 221.96 213.11 203.65 191.70 194.83 3.40% 35.93%


95

Ranking by State Income from all sources Renda todas as fontes

R$ R$ R$ R$ R$ R$ R$ R$ Var (%) Var (%)

rank 2008 rank 2007 rank 2006 rank 2005 rank 2004 rank 2003 rank 2002 rank 2001 rank 2007/2008 rank 2003/2008

1 Distrito Federal 1207.36 1 1173.40 1 1052.99 1 960.43 1 872.98 1 861.73 1 941.13 1 889.38 18 2.89% 8 40.11%

2 São Paulo 809.59 2 789.37 3 774.41 2 748.91 3 651.17 2 677.70 2 759.08 2 757.36 22 2.56% 26 19.46%

3 Rio de Janeiro 796.75 4 725.59 2 777.70 3 694.21 2 672.61 3 659.24 3 687.12 3 697.54 5 9.81% 25 20.86%

4 Santa Catarina 762.78 3 735.59 4 719.79 4 648.57 5 587.88 4 593.16 5 571.13 5 592.79 15 3.70% 20 28.60%

5 Rio Grande do Sul 723.17 6 667.20 5 658.07 5 615.43 4 603.22 5 580.85 4 592.73 4 598.56 8 8.39% 24 24.50%

6 Paraná 692.43 5 689.01 6 622.40 6 583.47 6 577.02 6 520.49 6 534.74 6 529.71 23 0.50% 17 33.03%

7 Mato Grosso 623.48 12 502.24 11 510.49 11 468.20 7 477.61 11 415.29 8 485.06 9 468.88 1 24.14% 3 50.13%

8 Mato Grosso do Sul 613.23 7 639.55 7 554.67 10 489.15 11 457.57 7 453.56 7 499.16 8 475.36 26 -4.12% 14 35.20%

9 Goiás 588.80 8 566.82 12 509.03 9 495.94 9 469.71 9 421.42 11 453.74 11 441.51 14 3.88% 9 39.72%

10 Espírito Santo 578.18 9 563.03 9 540.02 7 507.46 8 473.07 8 441.44 9 484.54 10 450.31 20 2.69% 19 30.98%

11 Minas Gerais 573.57 11 533.46 10 523.01 12 467.94 12 438.48 10 417.90 13 442.32 13 431.82 10 7.52% 12 37.25%

12 Acre 516.07 10 533.83 13 505.44 13 421.22 13 411.26 12 401.99 10 482.20 7 499.80 25 -3.33% 21 28.38%

13 Rondônia 508.86 13 494.65 8 549.81 8 501.44 10 466.60 13 386.91 12 444.06 14 410.91 19 2.87% 18 31.52%

14 Tocantins 459.83 17 403.96 17 369.26 18 336.37 15 334.95 17 306.86 21 295.33 17 333.70 2 13.83% 5 49.85%

15 Roraima 451.35 14 423.11 14 456.07 20 316.10 21 286.98 15 354.92 17 325.38 15 379.43 11 6.67% 22 27.17%

16 Amazonas 423.74 16 409.58 15 405.09 15 375.55 14 350.98 16 318.40 16 335.20 16 350.43 16 3.46% 16 33.08%

17 Rio Grande do Norte 417.26 19 388.00 18 366.86 16 355.94 19 299.41 20 273.47 18 305.56 19 304.36 9 7.54% 2 52.58%

18 Pará 403.82 18 391.97 19 365.27 17 339.44 16 333.47 19 289.23 15 345.25 18 328.72 17 3.02% 10 39.62%

19 Amapá 401.22 15 414.25 16 391.77 14 412.89 18 319.91 14 374.14 14 352.33 12 432.65 24 -3.15% 27 7.24%

20 Paraíba 396.98 21 364.68 21 347.61 21 311.99 22 284.11 21 264.81 22 287.09 24 262.28 6 8.86% 4 49.91%

21 Sergipe 387.61 20 371.03 20 363.00 19 328.16 17 333.04 18 305.06 19 300.57 21 275.49 13 4.47% 23 27.06%

22 Bahia 371.05 23 341.90 23 328.05 23 288.83 23 267.27 23 257.85 23 269.51 23 262.37 7 8.53% 7 43.90%

23 Pernambuco 362.70 25 327.03 22 336.45 22 307.60 20 296.28 22 260.99 20 295.89 20 294.19 4 10.91% 11 38.97%

24 Piauí 360.75 22 343.27 24 310.19 25 263.19 25 247.90 25 234.74 25 253.69 25 234.87 12 5.09% 1 53.68%

25 Ceará 351.01 26 310.40 26 297.92 24 276.21 24 258.04 24 242.17 24 262.46 22 264.15 3 13.08% 6 44.94%

26 Alagoas 315.51 24 329.77 25 308.80 26 238.60 27 219.91 26 232.83 26 231.02 26 233.64 27 -4.32% 13 35.51%

27 Maranhão 289.07 27 281.75 27 282.40 27 211.16 26 229.27 27 215.42 27 221.01 27 219.17 21 2.60% 15 34.19% Source: CPS/FGV based on PNAD/IBGE microdata

96

Ranking by Capitals and Metropolitan Peripheries Income from all sources Renda todas as fontes

R$ R$ R$ R$ R$ R$ R$ R$ Var (%) Var (%)


1 Florianópolis - SC 1248.98 1 1223.93 3 1148.69 2 1023.13 4 905.43 1 1062.17 4 929.72 3 1092.92 24 2.05% 31 17.59%

2 Porto Alegre - RS 1209.73 5 1076.71 1 1167.98 1 1161.14 2 1033.89 2 1031.57 2 1080.15 1 1148.15 6 12.35% 33 17.27%

3 Brasília - DF 1207.36 3 1173.40 4 1052.99 5 960.43 5 872.98 4 861.73 3 941.13 5 889.38 20 2.89% 13 40.11%

4 Vitória - ES 1207.27 2 1218.61 2 1150.46 3 1021.68 1 1100.05 3 1027.96 1 1276.20 2 1132.70 30 -0.93% 32 17.44%

5 Curitiba - PR 1148.15 4 1112.96 7 918.51 4 962.94 3 971.11 6 785.52 5 892.86 4 925.68 18 3.16% 7 46.16%

6 Rio de Janeiro - RJ 1014.68 10 890.89 5 998.22 6 896.76 6 864.44 5 822.39 7 872.94 6 871.25 4 13.90% 28 23.38%

7 Belo Horizonte - MG 1000.64 6 952.95 6 959.86 9 852.95 8 767.88 8 757.68 8 827.20 9 766.64 15 5.00% 21 32.07%

8 Cuiabá - MT 952.31 11 751.03 10 774.92 12 651.84 10 711.14 10 666.27 10 704.15 10 749.37 1 26.80% 10 42.93%

9 Goiânia - GO 912.68 7 922.82 12 716.03 8 870.55 7 784.79 9 669.10 9 724.28 11 739.99 31 -1.10% 18 36.40%

10 São Paulo - SP 901.87 9 902.73 8 885.26 7 876.45 9 747.96 7 769.11 6 881.46 7 867.08 27 -0.10% 34 17.26%

11 Palmas - TO 822.22 12 677.10 14 640.55 11 654.18 11 628.71 14 534.52 23 468.22 8 770.50 3 21.43% 4 53.82%

12 Campo Grande - MS 785.57 8 919.20 11 763.45 14 614.23 13 600.50 11 590.97 11 690.50 12 642.48 36 -14.54% 20 32.93%

13 João Pessoa - PB 724.28 13 671.20 18 622.08 19 524.69 19 516.26 20 458.55 15 595.48 19 517.87 8 7.91% 2 57.95%

14 Periferia de São Paulo - SP 689.23 17 642.70 17 628.43 16 582.92 17 529.50 12 552.15 16 588.71 14 608.59 10 7.24% 26 24.83%

15 Recife - PE 675.91 24 553.29 16 633.14 15 591.96 15 568.22 17 493.95 13 600.61 15 586.53 2 22.16% 17 36.84%

16 Teresina - PI 659.25 16 643.15 24 524.41 22 484.13 24 451.24 24 423.29 25 456.01 28 418.43 21 2.50% 3 55.74%

17 Aracaju - SE 650.64 15 647.64 15 635.16 18 568.84 12 628.14 13 550.05 17 547.20 24 465.13 26 0.46% 30 18.29%

18 Salvador - BA 648.11 21 607.10 22 565.81 20 497.90 23 452.47 21 444.57 18 536.14 18 522.81 12 6.76% 8 45.78%

19 Natal - RN 646.56 19 612.51 19 601.68 13 620.75 18 521.93 22 434.57 19 528.64 20 513.12 14 5.56% 6 48.78%

20 Periferia de Porto Alegre - RS 636.75 18 618.66 21 596.78 17 572.84 16 548.03 15 506.58 20 527.55 17 533.08 19 2.92% 25 25.70%

21 Rio Branco - AC 630.68 14 659.38 13 643.98 21 493.82 20 508.68 16 498.05 12 626.41 13 620.84 34 -4.35% 24 26.63%

22 Porto Velho - RO 628.46 20 608.22 9 776.74 10 690.56 14 576.46 18 476.09 14 599.73 16 551.85 17 3.33% 22 32.00%

23 Belém - PA 565.41 23 562.37 26 503.00 25 450.89 25 446.03 28 389.16 21 477.70 25 462.48 25 0.54% 9 45.29%

24 Periferia do Rio de Janeiro - RJ 564.71 26 540.17 23 524.84 23 474.21 22 453.82 19 470.40 22 470.94 21 493.56 16 4.54% 29 20.05%

25 Fortaleza - CE 547.79 28 485.31 27 487.34 24 468.37 26 443.28 26 403.02 24 467.81 22 492.66 5 12.87% 19 35.92%

26 Periferia do Curitiba - PR 532.90 25 541.88 28 475.86 28 428.72 27 423.20 29 384.03 27 424.86 30 410.47 33 -1.66% 14 38.77%

27 Maceió - AL 514.83 22 580.58 20 599.95 29 403.68 29 359.15 25 405.34 29 415.39 26 427.82 35 -11.32% 23 27.01%

28 São Luís - MA 504.22 27 510.71 25 514.89 30 387.23 21 476.21 23 432.22 26 438.74 27 426.10 32 -1.27% 35 16.66%

29 Manaus - AM 499.74 30 465.15 30 462.58 26 439.37 28 403.31 31 360.60 30 378.96 31 409.44 9 7.44% 15 38.59%

30 Periferia de Belo Horizonte - MG 486.51 29 476.68 31 441.69 31 383.08 30 351.55 32 346.98 31 368.69 32 374.51 23 2.06% 12 40.21%

31 Boa Vista - RR 467.68 32 440.97 29 472.43 34 340.33 33 308.92 30 376.70 32 348.37 29 418.02 13 6.06% 27 24.15%

32 Macapá - AP 442.01 31 445.61 32 430.75 27 434.82 31 343.93 27 402.00 28 418.08 23 486.13 29 -0.81% 36 9.95%

33 Periferia de Salvador - BA 415.74 33 383.89 33 389.12 32 347.24 34 292.64 35 249.15 33 317.64 34 312.81 7 8.30% 1 66.86%

34 Periferia de Recife - PE 377.92 34 369.73 34 362.16 33 344.12 32 309.34 33 268.08 34 311.83 33 339.21 22 2.22% 11 40.97%

35 Periferia de Belém - PA 365.49 35 366.05 35 316.62 35 293.24 35 272.66 34 264.29 35 279.55 35 268.84 28 -0.15% 16 38.29%

36 Periferia de Fortaleza - CE 297.85 36 278.74 36 249.14 36 216.70 36 215.22 36 195.48 36 202.67 36 195.40 11 6.86% 5 52.37% Source: CPS/FGV based on PNAD/IBGE microdata

3) Inequality Index

97

INEQUALITY

GINI GINI GINI GINI GINI GINI GINI GINI Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 0.5486 0.5550 0.5623 0.5682 0.5711 0.5830 0.5886 0.5957 -1.15% -5.90%

INEQUALITY


2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 0.5099 0.5268 0.5221 0.5258 0.5321 0.5450 0.5654 0.5647 -3.21% -6.44% Northeast 0.5569 0.5633 0.5701 0.5676 0.5783 0.5828 0.5920 0.5970 -1.14% -4.44% Southeast 0.5219 0.5267 0.5394 0.5441 0.5446 0.5589 0.5627 0.5687 -0.91% -6.62% South 0.4968 0.5025 0.5060 0.5159 0.5206 0.5281 0.5291 0.5464 -1.13% -5.93% Center 0.5662 0.5725 0.5594 0.5726 0.5666 0.5759 0.5910 0.5940 -1.10% -1.68%

INEQUALITY


2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 0.5791 0.5898 0.5910 0.5980 0.5984 0.6078 0.6062 0.6109 -1.81% -4.72% Metropolitan peripheries(Non- capital) 0.4882 0.4949 0.4974 0.5045 0.5119 0.5196 0.5211 0.5366 -1.35% -6.04% Non-metropolitan urban area 0.5138 0.5158 0.5265 0.5293 0.5358 0.5487 0.5529 0.5573 -0.39% -6.36% Rural area 0.5000 0.5140 0.5127 0.5084 0.5139 0.5328 0.5114 0.5431 -2.72% -6.16%


98

States INEQUALITY


2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Rondônia 0.4846 0.5000 0.5434 0.5721 0.5200 0.5036 0.5413 0.5479 -3.08% -3.77% Acre 0.5482 0.5677 0.5725 0.5723 0.5916 0.5784 0.6202 0.6332 -3.43% -5.22% Amazonas 0.5042 0.5374 0.5037 0.4991 0.5283 0.5554 0.5616 0.5720 -6.18% -9.22% Roraima 0.5065 0.5079 0.5704 0.5613 0.5598 0.5277 0.5626 0.5423 -0.28% -4.02% Pará 0.5273 0.5389 0.5382 0.5401 0.5459 0.5556 0.5780 0.5814 -2.15% -5.09% Amapá 0.4475 0.5011 0.4798 0.5181 0.5357 0.5949 0.5486 0.4780 -10.70% -24.78% Tocantins 0.5395 0.5430 0.5188 0.5351 0.5512 0.5647 0.5614 0.6002 -0.64% -4.46% Maranhão 0.5192 0.5536 0.5918 0.5158 0.6029 0.5730 0.5633 0.5706 -6.21% -9.39% Piauí 0.5727 0.5882 0.5891 0.5816 0.5765 0.5983 0.6093 0.5862 -2.64% -4.28% Ceará 0.5594 0.5456 0.5531 0.5708 0.5938 0.5826 0.5957 0.6230 2.53% -3.98% Rio Grande do North 0.5487 0.5640 0.5539 0.5922 0.5658 0.5608 0.5814 0.5847 -2.71% -2.16% Paraíba 0.5819 0.5922 0.5589 0.5683 0.5848 0.5641 0.5935 0.5920 -1.74% 3.16% Pernambuco 0.5968 0.5743 0.6043 0.6185 0.6255 0.6109 0.6211 0.6202 3.92% -2.31% Alagoas 0.5782 0.6045 0.6223 0.5694 0.5696 0.6049 0.5942 0.5980 -4.35% -4.41% Sergipe 0.5348 0.5390 0.5569 0.5503 0.5585 0.5760 0.5587 0.5693 -0.78% -7.15% Bahia 0.5779 0.5920 0.5791 0.5865 0.5899 0.6137 0.6311 0.6244 -2.38% -5.83% Minas Gerais 0.5368 0.5496 0.5561 0.5507 0.5571 0.5693 0.5657 0.5584 -2.33% -5.71% Espírito Santo 0.5180 0.5242 0.5358 0.5550 0.5471 0.5597 0.5755 0.5925 -1.18% -7.45% Rio de Janeiro 0.5640 0.5822 0.5712 0.5668 0.5705 0.5756 0.5564 0.5794 -3.13% -2.02% São Paulo 0.5242 0.5345 0.5495 0.5604 0.5531 0.5690 0.5789 0.5782 -1.93% -7.87% Paraná 0.5132 0.5181 0.5158 0.5362 0.5648 0.5371 0.5374 0.5652 -0.95% -4.45% Santa Catarina 0.4634 0.4624 0.4640 0.4639 0.4608 0.4785 0.4710 0.4980 0.22% -3.16% Rio Grande do South 0.5355 0.5243 0.5451 0.5551 0.5418 0.5496 0.5669 0.5635 2.14% -2.57% Mato Grosso do South 0.5290 0.5624 0.5294 0.5283 0.5281 0.5381 0.5583 0.5658 -5.94% -1.69% Mato Grosso 0.5447 0.5161 0.5282 0.5202 0.5247 0.5498 0.5711 0.5685 5.54% -0.93% Goiás 0.5084 0.5217 0.5054 0.5505 0.5324 0.5257 0.5503 0.5627 -2.55% -3.29% Distrito Federal 0.6218 0.6121 0.6060 0.6039 0.6189 0.6268 0.6240 0.6193 1.58% -0.80%


99

Capitals and Metropolitan Peripheries INEQUALITY


2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Porto Velho - RO 0.5214 0.5182 0.5762 0.6094 0.5606 0.5342 0.5691 0.5798 0.62% -2.40% Rio Branco - AC 0.5480 0.5504 0.5708 0.5717 0.5835 0.5586 0.6016 0.6177 -0.44% -1.90% Manaus - AM 0.4872 0.5418 0.4875 0.4865 0.5177 0.5620 0.5635 0.5755 -10.08% -13.31% Boa Vista - RR 0.4977 0.4991 0.5515 0.5626 0.5397 0.5222 0.5605 0.5431 -0.28% -4.69% Belém - PA 0.5470 0.5537 0.5561 0.5560 0.5636 0.5773 0.5863 0.5859 -1.21% -5.25% PA Periferia 0.4341 0.4696 0.4445 0.4572 0.4456 0.4653 0.5074 0.5213 -7.56% -6.71% Macapá - AP 0.4567 0.5011 0.4919 0.5260 0.5302 0.5727 0.5568 0.4593 -8.86% -20.25% Palmas - TO 0.5423 0.5420 0.5295 0.5559 0.5727 0.6096 0.5727 0.6491 0.06% -11.04% São Luís - MA 0.5694 0.5695 0.6576 0.5837 0.6395 0.6560 0.6548 0.6368 -0.02% -13.20% Teresina - PI 0.5792 0.6044 0.5772 0.6122 0.6056 0.6098 0.5907 0.5957 -4.17% -5.02% Fortaleza - CE 0.5714 0.5580 0.5643 0.5800 0.6019 0.5939 0.5998 0.6244 2.40% -3.79% CE Periferia 0.4514 0.4451 0.4353 0.4330 0.4736 0.4485 0.4684 0.4917 1.42% 0.65% Natal - RN 0.5599 0.5701 0.5751 0.6122 0.5961 0.5802 0.5715 0.5735 -1.79% -3.50% João Pessoa - PB 0.6180 0.6298 0.6084 0.6095 0.6477 0.5965 0.6392 0.6290 -1.87% 3.60% Recife - PE 0.6447 0.6041 0.6500 0.6545 0.6556 0.6457 0.6422 0.6524 6.72% -0.15% PE Periferia 0.5212 0.5338 0.5338 0.5591 0.5611 0.5458 0.5566 0.5606 -2.36% -4.51% Maceió - AL 0.6148 0.6390 0.6565 0.5961 0.6140 0.6390 0.6373 0.6297 -3.79% -3.79% Aracaju - SE 0.5347 0.5527 0.5789 0.5527 0.5600 0.5845 0.5648 0.5739 -3.26% -8.52% Salvador - BA 0.5812 0.6082 0.5842 0.5931 0.5931 0.6180 0.6326 0.6272 -4.44% -5.95% BA Periferia 0.5337 0.4757 0.5294 0.5359 0.5446 0.5431 0.5793 0.5621 12.19% -1.73% Belo Horizonte - MG 0.5568 0.5662 0.5677 0.5689 0.5770 0.5889 0.5726 0.5640 -1.66% -5.45% MG Periferia 0.4387 0.4625 0.4578 0.4377 0.4471 0.4589 0.4706 0.4825 -5.15% -4.40% Vitória - ES 0.5591 0.5452 0.5358 0.5657 0.5380 0.5457 0.5384 0.5789 2.55% 2.46% Rio de Janeiro - RJ 0.5764 0.6119 0.5880 0.5749 0.5701 0.5899 0.5661 0.5847 -5.80% -2.29% RJ Periferia 0.4988 0.5013 0.4861 0.5009 0.5161 0.5100 0.4859 0.5263 -0.50% -2.20% São Paulo - SP 0.5518 0.5560 0.5752 0.5895 0.5799 0.5923 0.6028 0.5972 -0.76% -6.84% SP Periferia 0.4708 0.4849 0.4904 0.4912 0.4960 0.5136 0.5173 0.5306 -2.91% -8.33% Curitiba - PR 0.5166 0.5263 0.5226 0.5383 0.5703 0.5420 0.5305 0.5662 -1.84% -4.69% PR Periferia 0.4105 0.4170 0.4324 0.4274 0.4510 0.4501 0.4696 0.4664 -1.56% -8.80% Florianópolis - SC 0.4839 0.5075 0.4968 0.5020 0.5202 0.5356 0.5097 0.5599 -4.65% -9.65% Porto Alegre - RS 0.5664 0.5705 0.5795 0.5833 0.5625 0.5713 0.5890 0.5814 -0.72% -0.86% RS Periferia 0.4739 0.4635 0.4767 0.4873 0.4838 0.4796 0.4978 0.4860 2.24% -1.19% Campo Grande - MS 0.5422 0.5973 0.5588 0.5493 0.5469 0.5634 0.5741 0.5882 -9.22% -3.76% Cuiabá - MT 0.5909 0.5462 0.5484 0.5220 0.5532 0.5703 0.5605 0.6070 8.18% 3.61% Goiânia - GO 0.5268 0.5436 0.5028 0.6015 0.5710 0.5336 0.5438 0.5868 -3.09% -1.27% Brasília - DF 0.6218 0.6121 0.6060 0.6039 0.6189 0.6268 0.6240 0.6193 1.58% -0.80%


100

4) 5) Participation of different incomes in the total income

5.1) Work earnings Income from all jobs

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 76.05 76.77 75.79 75.85 76.20 76.52 77.25 77.80 -0.94% -0.62%

Income from all jobs

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 82.00 83.29 82.21 82.16 82.81 83.27 83.67 84.12 -1.55% -1.52%

Northeast 71.85 71.05 71.62 71.59 71.34 72.08 72.70 74.13 1.12% -0.32% Southeast 75.99 77.23 75.75 75.46 75.89 76.37 77.30 77.96 -1.61% -0.49%

South 76.01 76.67 75.97 76.40 76.88 76.61 77.03 76.72 -0.86% -0.79%

Center 81.43 81.70 80.58 81.98 82.62 82.73 82.88 83.13 -0.33% -1.57%

Income from all jobs

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 76.80 77.65 75.61 75.91 75.65 75.87 76.58 77.24 -1.09% 1.22% Metropolitan peripheries(Non- capital) 78.28 78.87 76.65 78.08 76.92 78.24 79.33 80.09 -0.75% 0.05%

Non-metropolitan urban area 76.09 76.81 76.70 75.91 77.08 77.09 77.81 78.13 -0.94% -1.30%

Rural area 67.24 67.91 68.78 70.63 71.69 72.74 72.77 73.88 -0.99% -7.57%

101

Source: CPS/FGV based on PNAD/IBGE microdata Ranking by State Participation in the income (%) Income from all jobs PARTICIPAÇÃO NA RENDA (%) Renda todos os trabalhos

% % % % % % % % Var (%) Var (%)


1 Amapá 88,16 3 85,48 5 83,34 4 83,66 13 79,23 5 85,22 1 89,63 14 78,34 2 3,13% 2 3,45%

2 Roraima 86,26 5 84,25 1 85,82 3 84,81 1 90,18 4 86,19 3 88,58 1 89,37 5 2,38% 12 0,09%

3 Mato Grosso 85,69 4 85,47 4 83,67 2 86,38 3 87,67 1 87,02 2 88,91 2 89,27 14 0,26% 21 -1,53%

4 Amazonas 83,94 1 87,89 3 84,21 5 82,81 4 85,17 3 86,25 6 84,91 3 87,31 27 -4,49% 23 -2,68%

5 Rondônia 83,00 2 86,20 2 85,41 1 88,08 2 88,75 2 86,97 4 87,67 4 86,68 25 -3,72% 27 -4,56%

6 Mato Grosso do Sul 81,90 11 79,76 10 79,91 9 81,38 8 81,72 8 82,33 7 84,01 9 82,49 4 2,69% 17 -0,52%

7 Acre 81,41 6 83,61 8 80,75 7 81,55 6 83,62 10 80,79 14 78,90 8 82,68 24 -2,63% 9 0,77%

8 Tocantins 81,29 10 80,16 7 80,83 6 81,61 5 84,08 6 84,23 5 85,17 5 84,77 7 1,41% 26 -3,48%

9 São Paulo 80,66 8 82,57 11 79,53 10 80,61 10 80,56 9 81,33 8 81,96 6 83,59 22 -2,31% 18 -0,82%

10 Goiás 80,51 7 82,66 6 81,14 8 81,53 7 81,96 7 83,11 9 81,88 7 83,09 23 -2,60% 24 -3,13%

11 Pará 79,99 9 80,50 9 80,23 12 79,85 12 79,38 12 80,13 10 81,31 10 82,16 17 -0,64% 13 -0,17%

12 Distrito Federal 79,62 12 79,67 13 78,48 11 80,22 9 80,57 13 79,97 11 79,64 13 79,55 16 -0,06% 15 -0,44%

13 Paraná 78,86 13 78,66 12 78,69 14 78,51 11 79,70 14 79,25 13 79,22 12 79,77 15 0,25% 16 -0,49%

14 Sergipe 77,46 15 77,20 19 74,06 18 73,11 17 75,79 18 73,96 17 75,73 17 76,74 13 0,34% 1 4,73%

15 Santa Catarina 77,38 14 78,25 14 77,72 13 79,18 14 78,59 11 80,15 12 79,44 15 78,06 19 -1,11% 25 -3,45%

16 Maranhão 76,40 20 72,44 16 74,95 15 75,60 15 78,11 15 76,65 15 77,60 11 81,99 1 5,47% 14 -0,32%

17 Espírito Santo 76,17 16 75,89 15 75,34 19 72,93 16 76,62 16 75,39 16 76,76 16 76,94 12 0,37% 8 1,03%

18 Minas Gerais 74,94 17 75,78 17 74,56 16 73,80 18 74,48 19 73,81 19 74,95 19 75,17 20 -1,11% 6 1,53%

19 Bahia 72,99 21 72,36 18 74,06 17 73,68 20 73,26 17 74,75 18 75,22 18 76,74 8 0,87% 22 -2,35%

20 Alagoas 72,96 22 72,34 21 71,93 22 70,80 26 65,63 22 71,19 25 69,58 25 69,63 9 0,87% 4 2,48%

21 Rio Grande do Norte 72,77 19 72,47 23 70,41 24 70,10 23 69,29 23 70,86 20 74,84 20 73,64 11 0,43% 3 2,70%

22 Rio Grande do Sul 72,53 18 73,70 20 72,43 20 72,87 19 73,37 20 72,41 21 73,90 21 73,45 21 -1,59% 11 0,16%

23 Ceará 70,91 24 69,87 22 70,96 21 71,35 21 70,65 21 72,00 22 71,49 23 72,08 6 1,49% 20 -1,51%

24 Pernambuco 70,11 25 69,79 25 68,61 23 70,34 22 70,47 24 69,12 23 70,61 22 72,87 10 0,46% 7 1,43%

25 Rio de Janeiro 69,54 23 70,20 26 68,35 25 68,01 24 67,25 25 68,41 24 70,57 24 70,53 18 -0,95% 5 1,65%

26 Paraíba 68,57 27 66,66 24 68,94 26 67,33 27 65,22 26 68,21 26 68,18 26 68,93 3 2,88% 10 0,53%

27 Piauí 64,65 26 67,37 27 67,33 27 66,83 25 66,14 27 65,54 27 66,03 27 66,59 26 -4,04% 19 -1,36% Source: CPS/FGV based on PNAD/IBGE microdata

102

Ranking by Capitals and Metropolitan Peripheries Participation in the income (%) Income from all jobs PARTICIPAÇÃO NA RENDA (%) Renda todos os trabalhos

% % % % % % % % Var (%) Var (%)


1 Palmas - TO 88,31 2 88,53 1 88,05 1 88,55 1 90,74 1 92,92 1 92,01 1 89,90 19 -0,24% 36 -4,95%

2 Macapá - AP 86,74 3 86,67 7 80,59 11 81,59 20 75,71 5 83,10 2 88,33 23 75,52 16 0,08% 8 4,38%

3 Boa Vista - RR 86,11 7 83,80 2 85,96 3 85,47 2 89,69 2 86,18 3 87,70 2 89,70 7 2,76% 22 -0,08%

4 Periferia de Curitiba - PR 85,16 5 84,01 5 82,79 6 83,23 6 84,18 6 82,60 6 83,43 4 85,24 12 1,36% 11 3,10%

5 Manaus - AM 84,80 1 89,06 4 85,00 8 82,25 4 85,54 3 85,69 5 84,74 3 87,67 34 -4,78% 29 -1,04%

6 Campo Grande - MS 82,75 16 79,45 15 78,96 13 80,86 9 81,47 10 81,64 8 82,48 9 82,98 5 4,16% 17 1,36%

7 São Luís - MA 82,62 34 70,17 31 70,58 4 85,13 7 83,64 20 76,48 19 78,06 5 83,94 1 17,73% 4 8,02%

8 Rio Branco - AC 82,51 6 84,00 6 80,76 7 83,17 5 84,79 8 82,13 17 78,71 14 81,21 27 -1,77% 20 0,46%

9 Porto Velho - RO 81,95 4 86,54 3 85,36 2 88,15 3 88,98 4 85,18 4 85,24 13 81,26 35 -5,31% 34 -3,79%

10 Periferia de Salvador - BA 81,89 14 80,02 10 79,65 18 77,43 15 78,74 21 76,28 16 78,88 10 81,76 8 2,34% 5 7,36%

11 Cuiabá - MT 81,54 8 83,63 8 80,33 5 83,36 8 82,31 12 80,79 9 82,42 12 81,38 29 -2,50% 19 0,94%

12 Periferia de Belo Horizonte - MG 81,29 13 80,48 18 77,78 22 75,81 18 75,97 18 76,91 20 77,95 16 79,08 13 1,01% 6 5,70%

13 Periferia de Belém - PA 81,09 11 82,20 11 79,48 12 80,90 14 80,25 14 79,71 11 80,89 11 81,51 25 -1,35% 16 1,73%

14 Periferia de São Paulo - SP 80,93 12 81,94 14 79,03 10 81,63 13 80,41 7 82,35 7 82,51 7 83,57 24 -1,24% 31 -1,72%

15 São Paulo - SP 80,51 9 82,91 9 79,80 15 80,09 11 80,64 11 80,80 10 81,69 6 83,60 31 -2,90% 24 -0,36%

16 Maceió - AL 80,36 20 76,93 19 77,22 32 70,17 36 64,73 33 69,48 33 69,64 34 66,59 4 4,46% 1 15,65%

17 Aracaju - SE 79,67 19 77,38 25 75,31 27 73,04 22 74,99 30 72,38 29 71,94 29 73,93 6 2,96% 2 10,07%

18 Brasília - DF 79,62 15 79,67 16 78,48 14 80,22 12 80,57 13 79,97 13 79,64 15 79,55 18 -0,06% 25 -0,44%

19 Goiânia - GO 79,07 10 82,86 12 79,31 9 81,97 10 80,82 9 82,03 18 78,20 8 83,10 33 -4,58% 33 -3,62%

20 Natal - RN 78,89 29 74,00 27 71,78 25 73,81 28 71,41 29 72,62 12 79,89 26 74,81 3 6,61% 3 8,63%

21 Salvador - BA 77,69 18 77,98 13 79,15 19 77,30 23 74,74 19 76,67 23 75,63 17 78,85 20 -0,37% 18 1,32%

22 Curitiba - PR 77,13 25 75,87 23 75,56 20 77,24 16 78,46 15 78,32 24 75,59 22 75,68 10 1,67% 30 -1,51%

23 Florianópolis - SC 77,00 26 75,36 26 74,17 36 64,81 33 68,95 23 75,41 35 68,67 36 58,92 9 2,18% 14 2,11%

24 Periferia de Porto Alegre - RS 76,90 17 78,43 17 77,97 16 79,16 17 78,04 17 77,25 15 78,97 20 77,40 28 -1,95% 26 -0,44%

25 Belém - PA 75,98 22 76,63 22 76,00 26 73,74 25 73,60 26 74,11 21 77,70 18 78,35 21 -0,84% 13 2,53%

26 Periferia de Fortaleza - CE 75,83 23 76,48 21 76,22 17 78,06 19 75,83 16 77,85 14 79,43 19 78,24 22 -0,85% 32 -2,60%

27 Fortaleza - CE 75,75 27 74,72 20 76,35 21 76,04 27 73,16 22 75,43 26 73,41 25 74,84 11 1,38% 21 0,43%

28 Belo Horizonte - MG 75,49 24 76,34 24 75,42 23 75,07 21 75,38 25 74,11 22 76,45 24 75,41 23 -1,10% 15 1,86%

29 João Pessoa - PB 73,92 35 69,31 33 70,11 33 69,27 35 64,82 27 74,10 34 68,93 32 71,75 2 6,66% 23 -0,24%

30 Periferia do Rio de Janeiro - RJ 72,51 31 72,52 30 70,69 29 72,38 29 70,19 28 73,00 25 75,33 21 75,99 17 -0,01% 27 -0,67%

31 Periferia de Recife - PE 72,38 28 74,37 29 70,89 31 71,09 32 69,52 32 69,92 31 70,29 30 73,16 30 -2,68% 9 3,51%

32 Porto Alegre - RS 72,26 32 71,63 32 70,30 30 71,93 31 69,79 35 69,14 28 72,45 31 71,83 14 0,87% 7 4,50%

33 Teresina - PI 71,67 30 73,84 28 71,68 24 73,82 30 69,87 24 74,59 27 72,94 35 66,38 32 -2,94% 35 -3,92%

34 Recife - PE 71,53 33 71,19 34 69,70 28 72,90 26 73,53 34 69,30 32 69,76 28 74,01 15 0,47% 10 3,21%

35 Vitória - ES 69,97 21 76,84 36 66,42 34 66,51 24 73,86 31 70,57 30 71,11 27 74,35 36 -8,93% 28 -0,85%

36 Rio de Janeiro - RJ 67,98 36 68,95 35 67,28 35 65,88 34 65,89 36 66,14 36 68,36 33 67,89 26 -1,40% 12 2,79%

103

5.2 ) Private transfers Other private income

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 2.17 1.99 2.45 2.53 2.48 2.47 2.68 2.64 9.14% -11.95%

Other private income

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 2.04 1.74 2.30 2.91 2.28 2.14 2.72 2.38 17.61% -4.38%

Northeast 1.99 1.76 2.10 2.25 2.06 2.30 2.52 2.38 12.87% -13.29% Southeast 2.07 1.81 2.42 2.59 2.46 2.43 2.65 2.60 14.48% -15.00%

South 2.54 2.42 2.81 2.59 2.79 2.77 2.85 2.92 4.91% -8.22%

Center 2.43 2.73 2.73 2.40 2.81 2.48 2.84 2.94 -10.97% -2.23%

Other private income

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 2.37 2.10 2.75 2.93 2.55 2.51 2.76 2.73 12.93% -5.49% Metropolitan peripheries(Non- capital) 1.37 1.42 1.87 1.88 2.04 2.25 2.18 1.82 -2.95% -38.79%

Non-metropolitan urban area 2.38 2.19 2.53 2.59 2.71 2.62 2.88 2.88 8.78% -9.15%

Rural area 1.22 1.22 1.48 1.28 1.37 1.61 1.77 2.13 0.57% -23.97%


104

Ranking by State Participation in the income (%) Other private income

% % % % % % % % Var (%) Var (%)


1 Tocantins 3,51 2 3,25 1 3,77 20 2,08 9 2,69 16 2,46 14 2,52 1 3,85 13 8,15% 3 42,66%

2 Piauí 3,33 12 1,99 20 2,11 12 2,47 19 2,14 17 2,45 9 2,80 2 3,58 2 67,36% 4 36,14%

3 Acre 3,19 10 2,08 4 2,96 23 1,90 26 1,17 25 1,30 1 3,45 21 2,05 4 53,62% 1 146,24%

4 Santa Catarina 3,14 5 2,68 5 2,94 13 2,33 3 3,07 21 2,02 24 1,91 8 2,83 10 17,05% 2 55,66%

5 Mato Grosso do Sul 3,06 1 3,27 3 3,33 14 2,27 1 3,75 15 2,50 3 3,37 4 3,40 21 -6,39% 7 22,47%

6 Goiás 2,81 3 3,21 15 2,50 6 2,87 2 3,31 6 2,81 5 3,06 3 3,51 22 -12,66% 9 -0,16%

7 Mato Grosso 2,55 7 2,29 2 3,76 24 1,82 4 2,94 7 2,78 22 2,21 19 2,21 12 11,57% 14 -8,15%

8 Paraná 2,53 4 3,03 6 2,85 4 3,17 5 2,91 1 3,38 2 3,42 6 2,87 23 -16,46% 21 -25,17%

9 Minas Gerais 2,42 13 1,98 13 2,62 11 2,61 7 2,78 14 2,53 11 2,70 11 2,65 9 21,84% 11 -4,64%

10 Rio Grande do Sul 2,19 20 1,65 10 2,70 16 2,21 11 2,52 12 2,66 8 2,85 5 3,03 7 32,90% 16 -17,44%

11 Rio Grande do Norte 2,12 8 2,26 7 2,80 8 2,75 10 2,67 10 2,67 13 2,61 12 2,57 20 -6,27% 19 -20,65%

12 Pará 2,11 11 2,04 17 2,36 3 3,20 6 2,87 11 2,67 4 3,32 10 2,70 16 3,38% 20 -20,94%

13 São Paulo 2,09 23 1,39 12 2,65 5 3,03 16 2,25 13 2,61 17 2,42 15 2,44 5 49,93% 18 -19,84%

14 Bahia 2,05 17 1,81 26 1,73 18 2,10 24 1,59 22 2,00 15 2,47 18 2,26 11 13,61% 8 2,62%

15 Ceará 2,02 24 1,39 24 1,81 19 2,09 20 1,83 18 2,19 19 2,36 7 2,86 6 45,26% 13 -8,01%

16 Rondônia 1,99 25 1,24 27 1,54 21 1,94 22 1,61 20 2,03 23 1,93 22 2,03 3 60,80% 10 -1,91%

17 Pernambuco 1,96 22 1,49 11 2,65 7 2,78 13 2,46 8 2,73 10 2,72 20 2,09 8 31,82% 22 -28,14%

18 Paraíba 1,94 18 1,80 14 2,55 27 1,61 8 2,71 3 2,89 6 2,90 16 2,38 14 7,36% 23 -33,03%

19 Alagoas 1,92 14 1,97 23 1,81 10 2,67 17 2,23 2 3,29 18 2,40 9 2,81 18 -2,41% 24 -41,54%

20 Rio de Janeiro 1,80 16 1,90 18 2,25 22 1,93 12 2,47 24 1,92 21 2,31 24 1,91 19 -5,24% 12 -6,07%

21 Espírito Santo 1,71 21 1,60 8 2,78 2 3,65 14 2,39 19 2,11 7 2,88 17 2,35 15 6,79% 17 -19,23%

22 Distrito Federal 1,62 9 2,13 21 2,10 15 2,23 23 1,61 23 1,92 12 2,69 14 2,46 24 -23,76% 15 -15,71%

23 Maranhão 1,30 19 1,71 22 1,91 25 1,73 18 2,22 26 1,04 20 2,33 25 1,68 25 -23,76% 5 25,99%

24 Amazonas 1,26 27 0,73 25 1,74 1 3,68 21 1,72 27 1,01 16 2,43 26 1,42 1 72,95% 6 24,85%

25 Sergipe 1,21 6 2,48 19 2,11 17 2,15 25 1,53 9 2,71 25 1,91 23 1,97 27 -51,15% 25 -55,22%

26 Amapá 1,17 26 1,20 16 2,46 9 2,69 15 2,35 4 2,88 27 0,82 13 2,47 17 -2,37% 26 -59,43%

27 Roraima 1,08 15 1,92 9 2,73 26 1,66 27 0,65 5 2,83 26 1,19 27 1,17 26 -44,03% 27 -61,94% Source: CPS/FGV based on PNAD/IBGE microdata

105

Ranking by Capitals and Metropolitan Peripheries Participation in the income (%) Other private income PARTICIPAÇÃO NA RENDA (%) Outras rendas privadas

% % % % % % % % Var (%) Var (%)


1 Teresina - PI 4,54 18 2,14 16 2,64 19 2,31 23 2,12 6 3,38 33 1,43 3 5,37 2 111,84% 3 34,07%

2 Rio Branco - AC 3,60 14 2,37 5 3,64 27 2,00 32 1,17 33 1,40 10 3,40 18 2,36 9 51,94% 1 157,19%

3 Palmas - TO 3,35 7 2,93 1 6,08 24 2,08 3 4,37 26 2,14 8 3,42 2 5,87 16 14,21% 2 56,31%

4 Campo Grande - MS 3,16 3 3,37 12 2,87 17 2,37 6 3,16 15 2,50 2 4,51 8 3,47 20 -6,13% 4 26,30%

5 Goiânia - GO 3,10 1 3,87 22 2,17 7 3,45 1 4,95 9 3,18 6 3,65 5 3,83 23 -19,87% 13 -2,35%

6 Recife - PE 2,83 20 2,04 2 4,10 3 3,85 8 3,12 5 3,55 7 3,48 25 1,96 13 38,85% 23 -20,21%

7 Belo Horizonte - MG 2,82 22 1,94 13 2,84 18 2,32 15 2,35 16 2,46 13 2,89 15 2,74 10 45,82% 7 14,94%

8 João Pessoa - PB 2,61 25 1,55 6 3,59 32 1,69 11 2,84 13 2,61 26 2,26 27 1,84 5 68,07% 12 -0,22%

9 Fortaleza - CE 2,58 24 1,69 21 2,19 13 2,62 20 2,23 22 2,28 20 2,48 10 3,14 8 52,66% 8 13,37%

10 Vitória - ES 2,57 34 0,95 28 1,97 1 4,52 5 3,49 29 2,06 4 4,33 4 4,29 1 171,27% 5 24,96%

11 Salvador - BA 2,49 11 2,62 20 2,24 20 2,25 19 2,25 24 2,25 11 3,07 16 2,70 19 -4,98% 9 10,74%

12 Porto Alegre - RS 2,49 23 1,71 15 2,65 10 3,25 13 2,57 10 3,10 15 2,66 11 3,11 11 45,71% 22 -19,73%

13 Natal - RN 2,47 5 3,25 9 3,05 9 3,32 4 4,05 7 3,38 21 2,47 12 2,89 26 -23,84% 25 -26,79%

14 Maceió - AL 2,47 8 2,77 26 2,08 11 3,25 18 2,29 1 4,81 17 2,57 7 3,77 21 -10,88% 30 -48,72%

15 Curitiba - PR 2,43 4 3,34 8 3,20 12 3,05 10 2,88 28 2,06 9 3,40 13 2,81 28 -27,11% 6 18,04%

16 São Paulo - SP 2,39 28 1,44 11 2,95 5 3,52 21 2,20 12 2,62 18 2,57 14 2,76 6 66,13% 16 -8,75%

17 Rio de Janeiro - RJ 2,34 16 2,30 14 2,65 22 2,21 12 2,71 27 2,12 22 2,47 23 2,10 18 1,85% 10 10,70%

18 Belém - PA 2,19 13 2,54 19 2,28 4 3,80 7 3,13 18 2,44 12 2,98 29 1,80 22 -13,97% 17 -10,26%

19 Periferia de Belém - PA 2,14 27 1,48 27 1,98 28 1,96 31 1,49 21 2,34 24 2,33 22 2,15 12 44,77% 15 -8,65%

20 Florianópolis - SC 2,12 2 3,65 4 3,99 6 3,51 9 2,94 2 4,29 35 1,06 1 6,15 32 -41,96% 33 -50,58%

21 Periferia de Salvador - BA 2,08 9 2,75 31 1,91 8 3,36 34 1,08 17 2,44 1 5,16 34 1,43 27 -24,13% 18 -14,64%

22 Periferia de Recife - PE 1,78 26 1,53 29 1,95 15 2,52 17 2,31 23 2,26 27 2,24 26 1,93 15 16,33% 24 -21,22%

23 Cuiabá - MT 1,74 10 2,74 10 2,96 31 1,69 14 2,53 8 3,25 3 4,50 6 3,82 31 -36,36% 28 -46,43%

24 Porto Velho - RO 1,63 35 0,79 36 0,94 36 1,26 27 1,69 31 1,91 31 1,82 35 1,40 3 105,18% 19 -14,66%

25 Brasília - DF 1,62 19 2,13 25 2,10 21 2,23 30 1,61 30 1,92 14 2,69 17 2,46 25 -23,76% 21 -15,71%

26 Periferia de São Paulo - SP 1,58 30 1,31 24 2,10 25 2,06 16 2,34 14 2,59 28 2,10 28 1,82 14 19,95% 27 -39,06%

27 Periferia de Porto Alegre - RS 1,56 33 1,00 30 1,95 33 1,62 22 2,17 25 2,16 23 2,47 19 2,30 7 55,86% 26 -27,72%

28 Periferia de Fortaleza - CE 1,29 31 1,22 33 1,55 34 1,54 33 1,12 34 1,36 30 1,84 20 2,29 17 5,66% 14 -4,65%

29 Periferia de Belo Horizonte - MG 1,28 21 1,95 23 2,12 30 1,78 28 1,67 19 2,42 25 2,31 24 1,98 30 -34,22% 29 -46,93%

30 Manaus - AM 1,27 36 0,63 32 1,90 2 4,39 24 2,05 35 1,25 19 2,54 30 1,64 4 103,75% 11 2,14%

31 Boa Vista - RR 1,19 17 2,21 17 2,52 29 1,86 36 0,62 20 2,37 34 1,31 36 1,00 33 -46,12% 32 -49,74%

32 Aracaju - SE 1,18 6 3,14 18 2,42 14 2,53 29 1,64 4 3,74 16 2,60 21 2,28 35 -62,26% 35 -68,32%

33 Periferia de Curitiba - PR 1,13 12 2,58 34 1,40 23 2,14 35 0,93 11 2,96 32 1,72 31 1,58 34 -56,17% 34 -61,75%

34 Macapá - AP 1,02 29 1,32 7 3,24 16 2,41 26 1,90 3 4,08 36 1,01 9 3,46 24 -22,81% 36 -75,00%

35 São Luís - MA 0,87 15 2,36 3 4,00 26 2,00 2 4,65 36 1,02 5 3,77 32 1,58 36 -63,13% 20 -14,67%

36 Periferia do Rio de Janeiro - RJ 0,77 32 1,16 35 1,39 35 1,34 25 1,93 32 1,52 29 1,97 33 1,50 29 -34,05% 31 -49,48%

106

5.3) Public Transfers Public transfer Family Grant - BF*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 2.15 1.73 2.17 1.77 1.60 1.07 1.29 0.93 24.02% 100.60%

Public transfer Family Grant - BF*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 2.72 2.16 2.82 1.67 1.66 1.19 0.97 0.62 25.86% 129.02%

Northeast 3.83 4.12 4.28 3.32 3.38 1.63 1.66 1.21 -7.17% 135.04% Southeast 1.72 0.96 1.48 1.42 1.06 0.86 1.38 0.78 79.08% 99.49%

South 1.79 1.65 2.08 1.58 1.63 1.23 0.98 1.27 8.62% 45.29%

Center 1.75 1.58 2.14 1.52 1.43 0.96 0.83 0.85 10.46% 82.31%

Public transfer Family Grant - BF*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 1.68 1.06 1.56 1.39 1.22 0.83 1.53 1.06 58.91% 103.07% Metropolitan peripheries(Non- capital) 1.58 1.00 1.76 1.03 0.97 0.64 0.84 0.59 57.54% 146.22%

Non-metropolitan urban area 2.23 1.84 2.31 1.88 1.69 1.21 1.07 0.78 21.41% 83.94%

Rural area 5.21 5.90 5.38 4.63 4.18 2.27 2.49 1.97 -11.80% 129.45%

Source: CPS/FGV based on PNAD/IBGE microdata Ranking by State Participation in the income (%) Public Transfers Family grant

107

PARTICIPAÇÃO NA RENDA (%) Transferências Pública - BF*

% % % % % % % % Var (%) Var (%)


1 Alagoas 4,43 8 3,38 6 4,24 11 2,49 5 3,51 13 1,33 6 1,64 14 0,87 9 30,84% 5 233,63%

2 Pernambuco 4,35 5 3,63 2 5,25 2 3,78 2 4,09 5 1,97 4 2,10 5 1,46 12 19,69% 11 120,34%

3 Maranhão 4,17 6 3,51 1 5,27 5 3,58 8 2,75 19 0,87 9 1,21 25 0,21 13 18,88% 3 377,65%

4 Paraíba 4,13 4 3,72 10 3,35 3 3,78 4 3,64 2 2,17 3 2,34 3 1,75 15 11,25% 15 90,37%

5 Ceará 3,97 3 4,16 3 4,33 4 3,72 3 4,00 6 1,75 5 1,92 2 1,79 20 -4,44% 10 126,29%

6 Piauí 3,81 2 4,26 4 4,32 1 3,89 1 4,10 1 2,56 1 2,50 1 1,82 22 -10,57% 22 49,12%

7 Bahia 3,50 1 5,40 7 4,09 8 3,06 6 2,99 9 1,52 8 1,30 12 0,91 26 -35,11% 8 129,87%

8 Roraima 3,25 13 2,44 8 3,93 13 2,03 10 2,52 3 2,10 21 0,66 23 0,53 8 32,82% 20 54,42%

9 Rio Grande do Norte 3,23 10 2,76 11 3,15 10 2,67 7 2,95 12 1,34 15 1,04 7 1,40 14 17,06% 7 141,21%

10 Amapá 3,06 20 1,55 21 1,62 25 1,02 22 0,98 27 0,02 26 0,41 10 1,27 3 97,80% 1 16258,82%

11 Amazonas 2,82 19 1,62 9 3,71 23 1,15 24 0,94 25 0,49 24 0,48 27 0,17 6 74,36% 2 472,41%

12 Pará 2,75 12 2,62 14 2,47 16 1,77 14 1,93 8 1,54 11 1,18 21 0,56 16 5,20% 16 78,98%

13 Tocantins 2,75 7 3,48 5 4,29 7 3,33 9 2,74 4 2,03 7 1,47 6 1,45 24 -20,91% 23 35,18%

14 Rondônia 2,61 26 0,69 22 1,53 24 1,10 21 1,08 23 0,67 10 1,20 17 0,79 1 280,80% 4 286,87%

15 Sergipe 2,27 11 2,67 15 2,45 15 1,78 18 1,47 18 0,99 20 0,89 20 0,62 23 -14,99% 9 129,59%

16 Mato Grosso do Sul 2,10 9 2,92 12 2,81 9 2,77 12 2,06 16 1,08 18 0,94 9 1,31 25 -27,94% 14 94,72%

17 Minas Gerais 2,10 17 1,64 18 2,26 12 2,30 16 1,68 7 1,62 12 1,16 18 0,72 10 28,04% 24 29,41%

18 Goiás 2,10 21 1,45 13 2,81 18 1,61 15 1,86 17 1,07 19 0,90 15 0,82 7 44,93% 13 96,15%

19 São Paulo 2,07 25 0,71 25 1,36 20 1,26 27 0,65 24 0,65 2 2,43 13 0,88 2 190,25% 6 219,01%

20 Paraná 2,06 15 2,00 19 2,23 19 1,57 11 2,19 14 1,21 14 1,08 8 1,37 17 2,90% 18 69,61%

21 Rio Grande do Sul 1,81 16 1,85 16 2,39 14 1,78 17 1,61 11 1,48 13 1,12 4 1,51 18 -2,28% 25 21,69%

22 Acre 1,54 18 1,63 17 2,33 17 1,73 13 2,02 10 1,50 23 0,49 26 0,20 21 -5,36% 27 3,17%

23 Distrito Federal 1,48 23 0,75 24 1,43 26 0,97 23 0,97 20 0,84 16 1,01 16 0,82 4 96,02% 17 75,05%

24 Santa Catarina 1,34 24 0,75 26 1,35 21 1,24 26 0,67 22 0,81 22 0,55 19 0,66 5 79,72% 19 66,75%

25 Mato Grosso 1,28 14 2,15 23 1,47 22 1,23 25 0,80 21 0,83 27 0,33 22 0,56 27 -40,55% 21 53,88%

26 Espírito Santo 1,25 22 1,29 20 2,16 6 3,50 19 1,22 15 1,20 17 0,96 24 0,52 19 -3,29% 26 3,81%

27 Rio de Janeiro 0,79 27 0,66 27 0,89 27 0,75 20 1,12 26 0,36 25 0,44 11 1,16 11 19,84% 12 116,85%


108

Ranking by Capitals and Metropolitan Peripheries Participation in the income (%) Public Transfers Family Grant

% % % % % % % % Var (%) Var (%)


1 Periferia de Fortaleza - CE 3,85 1 4,42 1 5,04 1 4,30 1 4,10 2 2,37 5 1,46 1 2,24 30 -12,88% 23 62,10%

2 Periferia de Belém - PA 3,34 5 2,20 6 3,09 8 2,01 10 1,82 11 1,15 23 0,64 12 0,99 11 51,72% 9 191,39%

3 Macapá - AP 3,15 15 1,49 32 1,05 23 1,13 29 0,83 36 0,02 34 0,27 3 1,70 7 110,71% 1 12559,99%

4 Boa Vista - RR 3,11 4 2,24 4 3,63 12 1,74 5 2,48 3 1,79 24 0,64 30 0,28 17 38,53% 22 73,13%

5 Recife - PE 2,90 14 1,54 7 2,88 9 2,00 3 2,52 7 1,56 3 1,52 10 1,05 10 88,12% 19 85,59%

6 Curitiba - PR 2,38 6 2,14 21 1,63 10 1,91 6 2,39 13 0,98 7 1,26 2 2,09 22 11,23% 16 142,34%

7 João Pessoa - PB 2,37 10 1,69 23 1,60 11 1,78 8 2,04 26 0,59 26 0,57 20 0,59 14 40,36% 7 300,19%

8 São Paulo - SP 2,18 27 0,76 30 1,15 20 1,48 31 0,70 19 0,80 1 3,15 11 1,04 5 184,62% 13 170,74%

9 Florianópolis - SC 2,17 36 0,07 10 2,61 16 1,53 11 1,77 31 0,38 36 0,03 18 0,60 1 2912,21% 2 469,41%

10 Periferia de Recife - PE 2,15 7 2,01 2 4,33 4 2,36 4 2,50 18 0,81 12 0,97 16 0,81 23 6,64% 14 163,89%

11 Belém - PA 2,00 17 1,43 17 1,84 14 1,68 17 1,28 6 1,74 15 0,88 25 0,44 16 39,32% 30 14,61%

12 Periferia de Salvador - BA 1,95 3 2,31 13 2,22 5 2,36 12 1,47 4 1,75 8 1,11 29 0,29 31 -15,56% 31 11,61%

13 Maceió - AL 1,94 16 1,45 11 2,51 21 1,46 2 3,15 29 0,44 4 1,50 23 0,54 18 33,93% 6 337,40%

14 Periferia de São Paulo - SP 1,88 32 0,62 18 1,75 30 0,83 33 0,58 33 0,35 13 0,93 21 0,58 4 204,64% 3 435,59%

15 Fortaleza - CE 1,85 18 1,41 19 1,69 19 1,52 7 2,08 23 0,65 20 0,81 14 0,82 19 31,98% 10 185,30%

16 Manaus - AM 1,75 33 0,51 8 2,75 33 0,71 32 0,69 32 0,37 29 0,46 34 0,11 2 245,49% 4 378,86%

17 Goiânia - GO 1,75 31 0,65 28 1,35 34 0,60 30 0,74 22 0,68 32 0,35 24 0,51 6 169,97% 15 155,15%

18 São Luís - MA 1,68 24 0,83 3 3,81 13 1,68 24 1,06 25 0,61 16 0,87 36 0,04 8 103,29% 12 174,63%

19 Teresina - PI 1,55 9 1,74 22 1,60 27 0,94 9 1,97 14 0,96 6 1,27 7 1,46 29 -10,96% 24 61,15%

20 Natal - RN 1,50 12 1,59 14 2,01 22 1,36 26 0,96 20 0,78 30 0,44 19 0,59 27 -5,36% 18 91,78%

21 Campo Grande - MS 1,48 2 2,33 9 2,72 7 2,26 19 1,23 30 0,39 22 0,76 8 1,41 35 -36,53% 8 275,50%

22 Periferia de Belo Horizonte - MG 1,48 21 1,06 25 1,56 15 1,60 20 1,15 10 1,16 19 0,85 28 0,34 15 39,69% 29 28,06%

23 Brasília - DF 1,48 28 0,75 27 1,43 25 0,97 25 0,97 17 0,84 10 1,01 15 0,82 9 96,02% 21 75,05%

24 Periferia de Porto Alegre - RS 1,47 19 1,39 20 1,67 24 1,13 13 1,44 12 1,05 17 0,86 9 1,23 24 5,89% 26 40,20%

25 Salvador - BA 1,46 11 1,60 24 1,56 29 0,87 23 1,10 15 0,94 9 1,04 17 0,67 28 -9,12% 25 54,54%

26 Porto Velho - RO 1,43 35 0,46 35 0,82 35 0,44 22 1,10 24 0,62 21 0,80 6 1,54 3 211,50% 17 129,82%

27 Periferia de Curitiba - PR 1,33 23 0,93 16 1,91 28 0,90 18 1,24 21 0,72 18 0,86 22 0,58 12 42,96% 20 84,71%

28 Belo Horizonte - MG 1,28 20 1,22 15 2,00 3 2,39 15 1,39 8 1,35 2 1,53 26 0,40 25 4,90% 33 -5,37%

29 Palmas - TO 1,26 13 1,58 29 1,28 6 2,30 27 0,90 35 0,29 28 0,51 27 0,37 32 -19,96% 5 341,04%

30 Porto Alegre - RS 1,20 8 1,79 12 2,32 17 1,52 21 1,10 5 1,75 11 1,00 4 1,68 34 -32,63% 34 -31,09%

31 Rio Branco - AC 0,95 25 0,82 26 1,49 18 1,52 16 1,30 16 0,86 33 0,27 33 0,15 21 15,53% 32 10,19%

32 Rio de Janeiro - RJ 0,86 30 0,66 33 0,96 26 0,96 14 1,41 34 0,31 31 0,38 5 1,64 20 30,05% 11 180,28%

33 Aracaju - SE 0,75 22 0,98 34 0,91 32 0,73 34 0,55 27 0,57 25 0,61 35 0,07 33 -23,54% 28 31,12%

34 Cuiabá - MT 0,72 34 0,51 31 1,14 31 0,75 28 0,85 9 1,21 35 0,24 13 0,83 13 41,54% 35 -40,58%

35 Periferia do Rio de Janeiro - RJ 0,66 29 0,66 36 0,74 36 0,34 35 0,50 28 0,48 27 0,56 32 0,18 26 0,21% 27 37,24%

36 Vitória - ES 0,46 26 0,77 5 3,13 2 2,51 36 0,28 1 2,41 14 0,92 31 0,21 36 -40,01% 36 -80,70%

109

5.4 ) Social Security benefit up to one minimum wage Basic social security benefit- SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 4.74 4.92 4.66 4.53 4.24 4.44 3.96 3.62 -3.66% 6.70%

Basic social security benefit- SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 4.62 4.48 4.33 4.48 4.36 4.63 4.22 3.41 3.00% -0.28%

Northeast 9.52 10.06 9.46 9.52 9.17 10.39 9.07 8.15 -5.35% -8.41% Southeast 3.52 3.71 3.52 3.38 3.11 3.07 2.69 2.53 -5.18% 14.81%

South 4.35 4.41 4.20 4.03 3.62 3.77 3.66 3.43 -1.29% 15.56%

Center 3.27 3.33 3.36 3.21 2.98 3.42 3.04 2.79 -1.96% -4.64%

Basic social security benefit- SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 2.00 2.23 2.06 2.02 1.84 1.93 1.60 1.46 -10.62% 3.50% Metropolitan peripheries(Non- capital) 3.87 4.02 3.89 3.71 3.45 3.40 2.91 2.58 -3.78% 13.80%

Non-metropolitan urban area 5.33 5.44 5.18 5.14 4.75 5.04 4.64 4.23 -1.96% 5.87%

Rural area 16.84 16.85 16.48 15.64 14.97 15.84 16.12 14.49 -0.05% 6.37%


110

Ranking by State Participation in the income (%) Basic social security benefit MW PARTICIPAÇÃO NA RENDA (%) Piso Previdencia - SM*

% % % % % % % % Var (%) Var (%)


1 Ceará 10,83 2 11,34 1 10,97 4 10,55 5 9,69 3 11,46 4 9,93 3 9,70 14 -4,48% 18 -5,50%

2 Alagoas 10,77 5 10,33 5 9,26 3 11,07 2 11,40 5 10,20 5 9,16 7 8,04 4 4,30% 7 5,58%

3 Piauí 10,63 4 10,44 2 10,73 2 11,24 1 12,00 1 13,06 3 10,06 1 11,48 6 1,82% 25 -18,59%

4 Maranhão 10,45 1 12,54 4 9,75 1 12,48 3 10,63 2 12,29 1 11,09 6 8,09 25 -16,66% 23 -14,98%

5 Paraíba 10,36 3 10,46 3 10,72 5 9,61 4 10,06 4 11,38 2 10,15 2 11,24 10 -0,91% 19 -8,95%

6 Pernambuco 8,88 6 9,71 9 8,38 9 7,93 8 7,23 8 9,00 8 7,81 11 4,36 19 -8,59% 15 -1,35%

7 Bahia 8,87 7 9,33 7 8,88 6 9,01 6 9,13 6 9,94 6 8,72 4 8,87 17 -4,88% 20 -10,68%


9 Sergipe 6,95 10 6,61 8 8,63 7 8,15 10 5,89 10 6,87 10 6,87 10 4,42 2 5,01% 13 1,08%

10 Tocantins 6,92 9 7,02 10 7,31 10 7,24 9 6,50 9 7,09 9 7,33 8 5,75 11 -1,43% 16 -2,41%

11 Minas Gerais 5,97 11 6,27 11 6,03 11 5,92 11 5,48 11 5,74 11 5,13 9 4,99 16 -4,80% 9 3,92%

12 Pará 5,43 14 4,81 12 5,07 13 4,81 12 4,82 12 5,06 13 4,54 16 3,52 1 12,76% 6 7,19%

13 Rio Grande do Sul 4,73 13 4,84 14 4,56 14 4,34 17 3,78 18 3,98 15 3,94 14 3,65 13 -2,14% 4 19,07%

14 Goiás 4,65 16 4,43 15 4,51 16 4,22 14 4,05 13 4,84 14 4,50 12 4,12 3 4,96% 17 -3,77%

15 Espírito Santo 4,62 12 5,05 13 4,91 19 4,05 13 4,34 14 4,46 12 4,59 13 4,05 18 -8,48% 10 3,52%

16 Mato Grosso do Sul 4,30 20 4,14 17 4,35 20 3,82 16 3,92 15 4,35 16 3,85 17 3,48 5 3,78% 14 -1,13%

17 Paraná 4,24 18 4,30 18 4,11 18 4,14 18 3,66 17 4,05 17 3,83 15 3,54 12 -1,47% 8 4,63%

18 Acre 3,94 21 3,88 21 3,65 17 4,16 24 2,81 19 3,81 19 3,11 21 2,65 7 1,35% 11 3,20%

19 Santa Catarina 3,88 22 3,88 20 3,74 24 3,34 21 3,25 23 2,94 23 2,86 20 2,83 8 -0,01% 2 32,00%

20 Roraima 3,86 17 4,34 25 2,51 12 5,06 22 3,14 24 2,82 21 2,99 19 3,03 21 -11,06% 1 36,71%

21 Mato Grosso 3,83 15 4,65 16 4,37 15 4,26 20 3,35 20 3,73 22 2,90 23 2,62 26 -17,72% 12 2,67%

22 Rondônia 3,71 19 4,29 19 3,88 21 3,59 15 3,95 16 4,25 20 3,02 18 3,12 23 -13,34% 21 -12,59%

23 Amazonas 3,15 24 3,15 23 2,62 22 3,42 19 3,45 21 3,61 18 3,60 22 2,63 9 -0,05% 22 -12,71%

24 Rio de Janeiro 2,52 25 2,84 24 2,59 25 2,61 25 2,40 25 2,31 25 1,66 25 1,84 22 -11,32% 5 8,84%

25 Amapá 2,22 23 3,62 22 3,11 23 3,41 23 2,83 22 3,35 24 2,48 24 2,50 27 -38,81% 27 -33,94%

26 São Paulo 1,96 26 2,19 26 2,10 26 1,98 26 1,80 26 1,64 26 1,37 26 1,28 20 -10,55% 3 19,54%

27 Distrito Federal 0,85 27 0,98 27 0,98 27 1,05 27 0,87 27 1,11 27 1,00 27 0,94 24 -13,44% 26 -23,24% Source: CPS/FGV based on PNAD/IBGE microdata

111

Ranking by Capitals and Metropolitan Peripheries Participation in the income (%)Basic social security benefit MW PARTICIPAÇÃO NA RENDA (%) Piso Previdencia - SM*

% % % % % % % % Var (%) Var (%)


1 Periferia de Fortaleza - CE 10,53 1 10,07 1 10,07 1 11,06 1 9,69 1 11,10 1 8,36 1 8,19 6 4,59% 24 -5,11%

2 Periferia de Recife - PE 7,07 2 6,60 2 5,84 2 5,66 3 5,26 2 6,87 2 5,87 8 3,21 3 7,18% 16 2,88%



5 Periferia do Rio de Janeiro - RJ 4,28 7 4,35 5 4,47 6 4,23 8 4,00 12 3,58 13 2,83 11 2,83 13 -1,74% 7 19,46%

6 Periferia de Porto Alegre - RS 3,95 11 3,75 10 3,79 16 3,18 16 2,74 15 3,33 11 2,93 12 2,80 4 5,29% 8 18,53%

7 Periferia de Belém - PA 3,79 12 3,72 7 4,37 5 4,45 6 4,36 9 3,74 6 3,68 6 3,31 10 1,92% 17 1,44%

8 Periferia de Curitiba - PR 3,62 8 4,21 6 4,40 9 3,89 7 4,31 7 4,01 7 3,26 4 3,69 28 -13,89% 27 -9,63%

9 Maceió - AL 3,61 9 4,17 11 3,70 3 5,33 4 5,01 6 4,23 16 2,68 10 2,88 25 -13,43% 30 -14,75%

10 Fortaleza - CE 3,58 10 4,08 9 3,86 11 3,66 12 3,19 10 3,72 8 3,23 14 2,49 24 -12,39% 22 -3,98%

11 Natal - RN 3,57 14 3,42 12 3,57 17 3,14 14 2,91 11 3,66 14 2,76 7 3,31 7 4,37% 20 -2,54%

12 Boa Vista - RR 3,53 13 3,71 21 2,45 8 3,90 11 3,24 21 2,69 10 2,96 15 2,33 18 -4,85% 4 31,28%

13 Teresina - PI 3,43 17 3,27 8 4,23 12 3,58 9 3,76 5 4,29 5 3,79 5 3,54 5 4,99% 33 -20,01%

14 Belém - PA 3,39 15 3,38 14 3,16 14 3,22 10 3,37 14 3,40 9 3,07 21 1,87 11 0,30% 18 -0,17%

15 São Luís - MA 3,30 4 4,76 25 2,33 10 3,86 26 1,88 17 3,11 23 2,16 13 2,57 35 -30,70% 15 6,08%

16 Recife - PE 3,22 5 4,65 22 2,43 20 2,86 22 2,23 13 3,51 15 2,75 31 1,09 36 -30,80% 26 -8,28%

17 Aracaju - SE 2,96 23 2,87 13 3,50 19 2,93 24 2,02 20 2,71 18 2,60 19 2,03 9 2,96% 14 9,07%

18 João Pessoa - PB 2,84 16 3,33 17 3,04 21 2,70 17 2,70 8 3,75 17 2,62 9 3,19 29 -14,71% 35 -24,33%

19 Salvador - BA 2,80 21 2,93 16 3,07 18 3,00 15 2,89 19 2,84 19 2,47 17 2,15 15 -4,38% 19 -1,56%

20 Rio Branco - AC 2,74 19 3,17 24 2,39 13 3,33 23 2,07 23 2,37 21 2,19 22 1,81 27 -13,49% 11 15,75%

21 Periferia de São Paulo - SP 2,64 20 2,95 19 2,69 22 2,66 21 2,26 25 2,09 25 1,87 24 1,61 21 -10,44% 6 26,52%

22 Campo Grande - MS 2,49 27 2,39 20 2,52 27 2,15 13 2,92 18 2,84 22 2,18 16 2,29 8 4,23% 29 -12,42%

23 Belo Horizonte - MG 2,42 26 2,43 26 2,24 26 2,22 25 1,98 26 2,08 27 1,76 25 1,60 12 -0,41% 9 16,56%

24 Goiânia - GO 2,33 28 2,06 18 2,73 29 1,65 27 1,86 27 2,01 20 2,42 18 2,10 1 13,28% 10 16,33%

25 Porto Velho - RO 2,33 25 2,45 29 1,82 25 2,26 19 2,46 28 1,76 28 1,47 26 1,57 17 -4,71% 3 32,62%

26 Macapá - AP 2,29 18 3,22 15 3,12 15 3,20 18 2,68 16 3,16 24 1,96 20 2,02 33 -29,02% 36 -27,60%

27 Manaus - AM 2,13 24 2,52 28 2,05 23 2,64 20 2,40 22 2,42 12 2,88 23 1,63 31 -15,57% 28 -11,85%

28 Cuiabá - MT 2,03 22 2,88 23 2,42 24 2,49 28 1,70 24 2,10 26 1,82 29 1,23 34 -29,59% 21 -3,57%

29 Porto Alegre - RS 1,65 31 1,72 33 1,40 33 1,37 32 1,24 33 1,31 33 1,12 33 1,04 14 -4,05% 5 26,64%

30 Vitória - ES 1,64 35 1,50 34 1,33 34 1,28 31 1,45 36 0,96 30 1,14 32 1,06 2 8,86% 1 71,51%

31 Rio de Janeiro - RJ 1,60 29 2,02 31 1,73 28 1,82 29 1,66 29 1,69 32 1,12 27 1,36 32 -20,98% 25 -5,35%

32 São Paulo - SP 1,56 30 1,78 30 1,78 30 1,64 30 1,53 31 1,41 31 1,13 30 1,10 22 -12,02% 12 10,95%

33 Curitiba - PR 1,54 33 1,61 32 1,63 31 1,44 34 1,05 32 1,39 29 1,22 34 0,98 16 -4,69% 13 10,72%

34 Florianópolis - SC 1,41 34 1,61 36 0,92 32 1,37 33 1,16 35 0,96 34 1,04 36 0,86 23 -12,02% 2 47,88%

35 Palmas - TO 1,39 32 1,63 27 2,06 35 1,22 36 0,83 30 1,67 36 0,62 28 1,33 30 -14,82% 32 -16,51%

36 Brasília - DF 0,85 36 0,98 35 0,98 36 1,05 35 0,87 34 1,11 35 1,00 35 0,94 26 -13,44% 34 -23,24%

112

5.5 ) Social Security benefit above one minimum wage Social security above one minimum wage> SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Total 14.90 14.59 14.91 15.32 15.48 15.50 14.82 15.01 2.07% -3.87%

Social security above one minimum wage> SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 North 8.61 8.33 8.33 8.79 8.90 8.77 8.43 9.48 3.45% -1.80%

Northeast 12.82 13.01 12.54 13.32 14.05 13.60 14.06 14.14 -1.48% -5.78% Southeast 16.70 16.29 16.83 17.16 17.48 17.27 15.98 16.14 2.52% -3.31%

South 15.31 14.85 14.94 15.39 15.10 15.62 15.47 15.66 3.08% -1.98%

Center 11.12 10.66 11.18 10.90 10.16 10.40 10.41 10.30 4.36% 6.93%

Social security above one minimum wage> SM*

% % % % % % % % Var (%) Var (%)

2008 2007 2006 2005 2004 2003 2002 2001 2007/2008 2003/2008 Capital 17.15 16.96 18.03 17.76 18.74 18.86 17.53 17.51 1.12% -9.07% Metropolitan peripheries(Non- capital) 14.89 14.68 15.83 15.29 16.62 15.47 14.74 14.93 1.42% -3.71%

Non-metropolitan urban area 13.97 13.73 13.28 14.48 13.76 14.04 13.61 13.98 1.77% -0.47%

Rural area 9.49 8.12 7.87 7.82 7.79 7.54 6.85 7.52 16.96% 25.87%


113

Ranking by State Participation in the income (%) Social security above one minimum wage

% % % % % % % % Var (%) Var (%)


1 Rio de Janeiro 25,35 1 24,40 1 25,91 1 26,70 1 26,76 1 27,00 1 25,02 1 24,57 10 3,91% 16 -6,08%

2 Rio Grande do Sul 18,74 2 17,97 2 17,92 2 18,80 2 18,71 2 19,47 3 18,19 4 18,37 9 4,30% 14 -3,77%

3 Piauí 17,57 6 15,93 4 15,51 6 15,57 8 15,62 5 16,40 2 18,61 5 16,52 7 10,31% 5 7,17%

4 Distrito Federal 16,43 4 16,47 3 17,01 7 15,53 6 15,98 7 16,16 8 15,66 8 16,23 15 -0,25% 9 1,69%

5 Espírito Santo 16,25 5 16,17 6 14,81 5 15,87 10 15,43 4 16,83 10 14,80 9 16,13 14 0,50% 13 -3,40%

6 Paraíba 14,99 3 17,36 9 14,45 3 17,67 3 18,37 9 15,34 6 16,43 10 15,70 23 -13,65% 11 -2,28%

7 Pernambuco 14,70 7 15,38 5 15,10 9 15,18 7 15,75 3 17,17 5 16,76 2 19,22 19 -4,40% 22 -14,40%

8 Minas Gerais 14,57 9 14,32 8 14,53 8 15,37 9 15,57 6 16,29 7 16,06 6 16,47 12 1,72% 20 -10,55%

9 Santa Catarina 14,25 8 14,44 11 14,26 11 13,91 14 14,43 11 14,09 9 15,24 11 15,62 16 -1,31% 10 1,17%


11 São Paulo 13,22 12 13,13 10 14,36 12 13,12 12 14,75 13 13,77 17 11,82 17 11,81 13 0,65% 15 -4,02%

12 Bahia 12,58 15 11,10 16 11,24 16 12,16 16 13,04 17 11,79 16 12,30 18 11,23 5 13,32% 6 6,65%

13 Paraná 12,31 13 12,00 14 12,13 14 12,60 17 11,54 16 12,10 15 12,46 15 12,45 11 2,59% 8 1,72%

14 Ceará 12,27 11 13,25 15 11,94 15 12,30 15 13,82 15 12,59 12 14,30 14 13,58 20 -7,38% 12 -2,55%

15 Sergipe 12,10 16 11,03 13 12,75 10 14,83 11 15,32 8 15,47 11 14,60 7 16,24 8 9,76% 25 -21,75%

16 Goiás 9,93 21 8,25 21 9,04 19 9,78 20 8,82 23 8,17 18 9,67 22 8,46 3 20,38% 3 21,46%

17 Acre 9,93 20 8,81 17 10,31 17 10,66 19 10,38 14 12,61 13 14,05 16 12,42 6 12,71% 24 -21,29%

18 Alagoas 9,92 14 11,99 12 12,76 13 12,97 4 17,23 12 13,99 4 17,22 3 18,65 24 -17,26% 26 -29,10%

19 Pará 9,72 17 10,03 18 9,87 18 10,37 18 11,00 18 10,61 19 9,65 19 11,06 18 -3,06% 17 -8,35%

20 Amazonas 8,82 25 6,61 23 7,72 22 8,94 21 8,73 21 8,63 20 8,58 21 8,49 1 33,52% 7 2,17%

21 Rondônia 8,68 23 7,59 24 7,64 27 5,29 25 4,60 24 6,07 25 6,19 24 7,38 4 14,46% 1 42,95%

22 Mato Grosso do Sul 8,64 18 9,91 19 9,60 20 9,76 22 8,55 19 9,74 21 7,82 20 9,32 22 -12,86% 21 -11,31%

23 Maranhão 7,68 19 9,80 22 8,13 23 6,61 23 6,28 20 9,16 22 7,76 23 8,03 26 -21,69% 23 -16,19%

24 Mato Grosso 6,65 27 5,45 25 6,74 25 6,31 24 5,25 26 5,65 26 5,64 26 5,35 2 22,20% 4 17,85%

25 Roraima 5,55 24 7,04 26 5,01 24 6,45 27 3,52 25 6,06 24 6,58 25 5,90 25 -21,08% 18 -8,35%

26 Tocantins 5,53 26 6,09 27 3,79 26 5,73 26 3,99 27 4,19 27 3,51 27 4,18 21 -9,30% 2 32,01%

27 Amapá 5,39 22 8,15 20 9,47 21 9,22 13 14,61 22 8,52 23 6,66 12 15,42 27 -33,82% 27 -36,72% Source: CPS/FGV based on PNAD/IBGE microdata

114

Ranking by Capitals and Metropolitan Peripheries Participation in the income (%) Social security above one minimum wage

% % % % % % % % Var (%) Var (%)


1 Rio de Janeiro - RJ 27,22 1 26,07 1 27,38 1 29,13 1 28,32 1 29,75 2 27,67 2 27,01 13 4,41% 16 -8,50%

2 Vitória - ES 25,35 7 19,94 2 27,15 3 25,18 8 20,92 3 24,01 6 22,50 10 20,09 5 27,16% 7 5,60%

3 Porto Alegre - RS 22,39 3 23,14 3 23,33 5 21,93 3 25,30 2 24,70 5 22,78 6 22,33 24 -3,23% 18 -9,33%

4 Periferia do Rio de Janeiro - RJ 21,78 5 21,30 4 22,71 6 21,71 6 23,37 5 21,42 12 19,30 12 19,51 15 2,25% 9 1,70%

5 Recife - PE 19,52 6 20,58 6 20,89 10 18,38 17 18,60 4 22,08 7 22,49 7 21,89 26 -5,11% 24 -11,55%

6 Teresina - PI 18,81 9 19,01 7 19,84 9 19,35 7 22,29 18 16,77 10 20,57 4 23,25 19 -1,01% 5 12,16%

7 João Pessoa - PB 18,27 2 24,13 5 21,66 4 24,56 2 27,60 12 18,94 3 25,63 5 22,64 34 -24,30% 13 -3,56%

8 Belo Horizonte - MG 17,98 11 18,07 13 17,49 13 18,00 14 18,91 9 20,00 15 17,36 11 19,84 18 -0,52% 22 -10,11%

9 Florianópolis - SC 17,30 8 19,31 10 18,30 2 28,77 4 25,18 11 18,97 1 29,20 1 33,47 30 -10,41% 17 -8,79%

10 Periferia de Recife - PE 16,63 17 15,48 15 16,99 11 18,38 11 20,42 8 20,13 9 20,63 9 20,88 7 7,37% 26 -17,42%

11 Curitiba - PR 16,52 13 17,05 11 17,98 16 16,36 21 15,22 17 17,25 13 18,53 14 18,43 23 -3,09% 14 -4,25%

12 Belém - PA 16,44 15 16,01 16 16,72 14 17,57 16 18,61 14 18,31 19 15,37 16 17,55 14 2,68% 23 -10,20%

13 Brasília - DF 16,43 14 16,47 14 17,01 19 15,53 18 15,98 20 16,16 16 15,66 19 16,23 17 -0,25% 10 1,69%

14 Fortaleza - CE 16,24 10 18,10 17 15,91 17 16,17 12 19,34 15 17,92 11 20,06 13 18,71 29 -10,30% 19 -9,41%

15 Periferia de Porto Alegre - RS 16,13 18 15,43 18 14,64 20 14,91 20 15,61 19 16,22 21 14,77 18 16,28 12 4,49% 11 -0,61%

16 Salvador - BA 15,57 19 14,87 23 13,97 15 16,57 13 19,02 16 17,29 14 17,79 20 15,63 11 4,70% 21 -9,98%

17 Aracaju - SE 15,44 16 15,63 12 17,86 7 20,77 9 20,79 7 20,60 8 22,25 8 21,70 20 -1,22% 30 -25,06%

18 Cuiabá - MT 13,97 28 10,24 25 13,16 26 11,71 25 12,61 26 12,65 27 11,03 24 12,74 2 36,38% 6 10,42%

19 Goiânia - GO 13,75 25 10,56 20 14,44 25 12,33 27 11,62 29 12,09 18 15,38 30 10,46 3 30,23% 4 13,66%

20 Natal - RN 13,56 12 17,74 8 19,59 12 18,37 10 20,67 10 19,55 22 14,44 15 18,39 33 -23,57% 33 -30,66%

21 São Paulo - SP 13,36 22 13,11 22 14,32 21 13,27 22 14,93 23 14,37 26 11,46 29 11,50 16 1,94% 15 -7,00%

22 Periferia de São Paulo - SP 12,97 21 13,17 21 14,42 23 12,82 23 14,41 27 12,63 24 12,59 26 12,42 21 -1,56% 8 2,68%

23 Porto Velho - RO 12,66 29 9,75 31 11,06 32 7,89 34 5,77 30 10,54 29 10,68 23 14,23 4 29,80% 2 20,16%

24 Maceió - AL 11,63 20 14,69 19 14,49 8 19,80 5 24,82 6 21,03 4 23,61 3 26,23 32 -20,85% 36 -44,73%

25 São Luís - MA 11,53 4 21,88 9 19,28 33 7,32 33 8,78 13 18,78 20 15,14 28 11,86 36 -47,28% 35 -38,58%


27 Rio Branco - AC 10,20 30 9,64 28 11,72 30 9,98 29 10,67 24 13,23 17 15,43 21 14,47 10 5,80% 28 -22,95%

28 Campo Grande - MS 10,12 23 12,46 26 12,94 24 12,36 28 11,23 28 12,62 31 10,07 31 9,85 31 -18,83% 27 -19,82%

29 Manaus - AM 10,05 34 7,29 33 8,30 29 10,01 31 9,32 31 10,28 32 9,38 33 8,95 1 37,79% 12 -2,31%


31 Periferia de Belém - PA 9,64 26 10,40 30 11,08 28 10,69 26 12,08 25 13,07 25 12,46 27 12,05 28 -7,34% 31 -26,22%

32 Periferia de Curitiba - PR 8,76 31 8,27 32 9,50 31 9,85 30 9,34 32 9,72 28 10,72 34 8,91 9 5,95% 20 -9,84%

33 Periferia de Fortaleza - CE 8,50 33 7,81 34 7,11 36 5,04 32 9,26 34 7,32 33 8,91 32 9,03 6 8,79% 3 16,13%

34 Macapá - AP 6,80 35 7,29 27 12,01 27 11,67 15 18,87 33 9,63 34 8,43 17 17,30 27 -6,67% 32 -29,37%

35 Boa Vista - RR 6,06 32 8,04 35 5,44 34 7,03 35 3,98 35 6,97 35 7,39 35 6,69 35 -24,59% 25 -13,05%

36 Palmas - TO 5,68 36 5,33 36 2,53 35 5,85 36 3,16 36 2,99 36 3,44 36 2,53 8 6,62% 1 90,15%

Mapping Poverty, Inequality and the New Middle Class ...€¦ · Mapping Poverty, Inequality and...

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