regional disparities in brazil disparities in brazil: recent trends and future possibilities carlos...

Regional Disparities in Brazil: Recent Trends and Future Possibilities

Carlos R. Azzoni

FEAUSP, NEREUS RSA Global Conference 2014, Fortaleza, 29/04/2014

2

Regional inequality and growth: the role of interregional trade in the Brazilian economy

http://www.ifitweremyhome.com/compare/US/BR

http://www.ifitweremyhome.com/compare/US/BR

Regional concentration

Regional Concentration

1940 2010 1939 2011

Agric. Mining Manuf.

North 45.3 3.9 8.3 2.7 5.6 9.5 16.6 4.6

Northeast 18.3 35.0 27.8 16.9 13.8 16.6 7.0 8.7

Piauí St. 2.9 2.0 1.6 0.9 0.6 0.8 0.1 0.2

Southeast 10.9 44.5 42.1 63.0 54.4 31.7 73.4 59.6

S. Paulo St. 2.9 17.4 21.6 31.3 31.4 12.1 2.5 41.8

South 6.8 13.9 14.4 15.3 16.3 24.5 1.2 21.8

Mid-West 18.9 2.7 7.4 2.1 9.8 17.7 1.7 5.3

Table 1 - Indicators of regional concentration

Share in

Area

Share in Population Share in National GDP

2011

Total

Regional inequality

Regional Inequality

North

Northeast

Piauí St.

Southeast

S. Paulo St.

South

Mid-West

Per Capita GDP respective to the

national average

1939 2010

0.75 0.64

0.48 0.48

0.43 0.36

1.41 1.31

1.8 1.53

1.11 1.15

0.7 1.26*

Manufacturing Value Added/km2

Manufacturing Clusters, 2010

Northeast

Mid-West

Southeast

Market Potential

Knowledge Creation

Network of Scientists

Concentration of Skills

THE DETERMINANTS OF AGGLOMERATION IN BRAZIL: INPUT-OUTPUT, LABOR AND KNOWLEDGE EXTERNALITIES

BY AGUINALDO NOGUEIRA MACIENTE

PH. D. Dissertation, University of Illinois, 2013

Average Population 2010 1A - 19 Milhões 1B – 7,5 Milhões 1C – 3 Milhões 2A - 1,1 Milhões 2B – 426 Mil 2C – 358 Mil 3A - 110 Mil 3B – 76 Mil 4A - 52 Mil 4B – 27 Mil 5 – 12 Mil

SP

RJ, BSB

MAN, BEL, FOR, REC, SAL, BH, CWB, GOI, POA

80 Regional Capitals

“ …a more general set of abilities and skills, including variables such as deductive, inductive, and mathematical reasoning, investigative and analytical skills, as well as oral, written and interpretative skills”

SP

RJ, BSB

MAN, BEL, FOR, REC, SAL, BH, CWB, GOI POA 80 Regional Capitals

maintenance, operation, control, and repair of machines, manual dexterity, hearing and inspecting skills, as well with Holland’s (1997) realistic personalities

medicine and psychology knowledge domains, therapy and service orientation, moral values and integrity requirements and is associated with Holland’s (1997) social personalities

ability to coordinate and manage personnel and material resources, economics and accounting knowledge domains, and are related to Holland’s (1997) enterprising personalities

besides the two knowledge domains that give this factor its name, the Building and Construction and Physics domains, and drafting and visualization skills

Concentration within São Paulo Metropolitan Area

(Over 20 million people)

22

Where people reside

23

Where people work

Department of Economics, University of Sao Paulo

24

Where the money is

One million commuters (15.4% of the labor force)


25

Daily flows ...

Whole population

The rich (>20 Minimum Wages)

Productive efficiency: looking ahead …

Agriculture: 1970-2006

-

0.20

0.40

0.60

0.80

1.00

1.20

Northeast North Mid-West Southeast South

1970

1975

1980

1985

1995

2006

Agriculture: 2006

Manufacturing: average 2000/06

Better than SP Metro Same as SP Metro Worse than SP Metro

National productivity, 1995-2009

1.7

3.3

2.4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Agriculture Industry Service

Tota

l Fa

cto

r P

rod

uct

ivit

y

2.1

0.6

-1.9

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Agriculture Industry Service

Tota

l Fa

cto

r P

rod

uct

ivit

y G

row

th R

ate

(%

)

Levels Growth

1,7

3,3

2,4

2,1

0,6

1,9

Productivity Convergence?

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

-15 -13 -10 -8 -5 -3 0 3 5 8 10

TFP Level (zero=national average)

TFP Growth Rate (%, zero=national average)

High LevelPositive Growth

High LevelNegative Growth

Low LevelNegative Growth

Low LevelPositive GrowthPI

SC

RJ

GO

MG

PR

MS RR

AC

TO

RS

BAPA

PECEMA

RNPB

SEAL

SP

MT

APES

DF

AM

RO

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-15 -13 -10 -8 -5 -3 0 3 5 8 10






Low LevelPositive Growth

PE

PB

RN

AL

SE

PI

CE

PR

PA

DF

RJ

TO

BA

RS

MA

RRAPRO

ES

AM

SP

MT

MG

AC

GO

MS

SC

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-15 -13 -10 -8 -5 -3 0 3 5 8 10







PE

PB

RN

AL

SE

PI

CE

PR

PA

DF

RJ TO

BA

RS

MA

RR

AP

RO

ES

AM

SP

MT

MG

AC

GOMS

SC

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-15 -13 -10 -8 -5 -3 0 3 5 8 10







PE

PBRN

AL SE

PI

CE

PR

PA

DF

RJ

TO

BA

RS

MA

RR

AP

RO

ES

AM

SP

MT

MG

AC

GO

MS

SC

Global

Services Agriculture

Manufacturing

Productivity in Manufacturing So

uth

eas

t MG + + + +

ES + + + + + +

RJ + + + + - + -

SP + + + + + + +

Sou

th PR + - + + + +

SC + + + + + +

RS + - + + + + +

Mid

-Wes

t MS + + - - + -

MT + + - + +

GO + + + + +

DF + + + + + + +

TFP Levels TFP Growth

All

Sectors Agric Manuf Serv.

All

Sectors Agric Manuf Serv

TFP Levels TFP Growth

All

Sectors Agriculture Manufacturing Services

All

Sectors Agriculture Manufacturing Services N

ort

h

RO + - - + - -

AC + + - - + +

AM + + + - + +

RR + + - + +

PA - - - - -

AP + + - - + +

TO + - - + -

No

rth

eas

t

MA - + - - - -

PI - - - - - - -

CE - - - - - -

RN - - - - - - -

PB - - - - - - -

PE - - - - -

AL - - - - - - -

SE - - - - - - -

BA - + + - - -

Concentration evolution: anything new?

Economic Center of Gravity

tmm

m

t kLATLAT ,.

tmm

m

t kLONLON ,.

tBR

tm

tmGDP

GDPk

,

,

,

Average Longitude

Average Latitude

State’s share in

National GDP

);( tt LONLAT

-22.0

-21.8

-21.6

-21.4

-21.2

-21.0

-20.8

-20.6

-20.4

-20.2

-20.0

-46.2 -46.0 -45.8 -45.6 -45.4 -45.2 -45.0 -44.8

La

titu

de

Longitude Centro de Gravidade da Economia Brasileira

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Sul

Leste

Norte

Oeste

Leste

Norte

Oeste

Leste

Norte

Oeste

Leste

Norte

New winds over old mills, or new mills?

Relevant factors

• Macroeconomic stabilization – Inflation: from 30%-80% per month, to 6% per year

• Minimum Wage appreciation – Purchasing power of MW

has tripled since 1995

• Social Programs

• Government influenced productive investments

• Climate change

0

50

100

150

200

250

300

350

19

95

.08

19

96

.03

19

96

.10

19

97

.05

19

97

.12

19

98

.07

19

99

.02

19

99

.09

20

00

.04

20

00

.11

20

01

.06

20

02

.01

20

02

.08

20

03

.03

20

03

.10

20

04

.05

20

04

.12

20

05

.07

20

06

.02

20

06

.09

20

07

.04

20

07

.11

20

08

.06

20

09

.01

20

09

.08

20

10

.03

20

10

.10

20

11

.05

20

11

.12

20

12

.07

Purchasing Capacity of Minimum Wage (BRL)

Lula’s Administration

Recent evolution of productivity

Productivity evolution: Agriculture

1995-2011 2007-2011

Productivity evolution: Manufacturing

1995-2011 2007-2011

Productivity evolution: Services

1995-2011 2007-2011

Productivity evolution: Global

1995-2011 2007-2011

Social Policy as Regional Policy

Raul M. Silveira-Neto, Carlos R. Azzoni

Social Policy as Regional Policy: Market and Nonmarket Factors Determining Regional Inequality

Journal of Regional Science, Vol. 52, Issue 3, pp. 433-450, 2012

Non-spatial government policies and regional income inequality in Brazil

Regional Studies, v. 45, p. 453-461, 2011

Carlos Azzoni et al. Social policies, personal and regional income inequality in Brazil: an I-O analysis

J. Love, W. Baer. (Org.). Brazil Under Lula. Palgrave MacMillan, 2009, p. 243-261.

0

1

2

3

0 1 2 3

All Income Sources

La

bo

r In

co

me

in

Ag

ric

ult

ure

0

1

2

3

0 1 2 3

All Income Sources

La

bo

r In

co

me

in

Ma

nu

factu

rin

g

0

1

2

3

0 1 2 3

All Income Sources

La

bo

r In

co

me

in

th

e T

ert

iaryPe

r ca

pit

a la

bo

r in

com

e/

nat

ion

al p

er c

apit

a la

bo

r in

com

e

Per capita income from all sources/ National per capita income

Tertiary

Primary Secondary

0

1

2

3

0 1 2 3

All Income Sources

La

bo

r In

co

me

in

th

e P

ub

lic S

ecto

r

Public Sector

Labor Income

0 1 2 3

All Income Sources

Re

tire

me

nt

an

d P

en

sio

ns

0 1 2 3

All Income Sources

Pro

pe

rty

Ren

t a

nd

Do

na

tio

ns

Retirement and Pensions Property rents and donations

0 1 2 3

All Income Sources

Inte

rests

an

d D

ivid

en

ds

Interests and Dividends

0 1 2 3

All Income Sources

Bo

lsa F

am

ília

0 1 2 3

All Income Sources

Be

ne

fíc

ios

de

Pre

sta

çã

o C

on

tin

ua

da

Benefícios de Prestação Continuada

Bolsa Família

Results

• The largest part of the reduction in regional inequality in Brazil is related to the dynamics of the market-related labor income – manufacturing and services favor deconcentration.

– Labor income in agriculture, retirement and pensions, and property rents and other sources favored concentration.

• The social programs are responsible for more than 24% of the reduction in inequality, although they account for less than 1.7% of the disposable household income

• Comparing to previous regional development promotion schemes, this social policy is the most effective form of regional policy ever implemented in the country

Results

• Market-related labor income also played a vital role.

– This is shown both by the changes in the shares of different sources and, most importantly, by important concentration changes within the sources.

– This could be the result of regionally explicit polices, such as the ones promoted by individual states in relation to manufacturing

– Thus, it seems that market forces are reinforcing the social policy-related deconcentration effects

Impacts on production


81

We have assessed the economic impacts of the Bolsa Família program using an input-output framework

• Multi-regional input-output framework, for 2002.

• Five regions.

• 21 sectors in each region.

• Full specification of interregional flows.

• Ten income brackets were considered (R$/month): from zero income to 400 (5.3% of total national household income); 400 – 600 (5.4%); 600 – 1,000 (11.5%); 1,000 – 1,200 (5.3%); 1,200 – 1,600 (8.9%); 1,600 – 2,000 (8.3%); 2,000 – 3,000 (13.7%); 3,000 – 4,000 (9.7%); 4,000 – 6,000 (11.9%); and 6,000 and over (19.8%).

• The household expenditure patterns for each income bracket in each region are taken into account.

• Model captures direct, indirect and induced effects.

Department of Economics, University of Sao Paulo 82

How regions are affected

Brazil Northeast Southeast Mid-West North South

Production -0,48% 4,6% -0,82% -9,51% 1,15% 0,51%

Income -1,78% 3,3% -2,01% -16,55% 0,15% -0,39%

1st Poorest 1,71% 5,96% -0.97% -12.77% 1,02% 0.37%

2nd Poorest 0,33% 4,96% -1,13% -13,13% 0.69% 0.21%

3rd Poorest -0,77% 3,60% -1,48% -14.61% 0.45% -0.07%

All others

lose more

All others

gain less

All others

lose more

All others

lose more

All others

gain less

All others

lose more


83

Pro-Poor?

Gini Gini Change Gini Change

North 0.4659 0.4661 0.04% 0.4655 -0.07%

Northeast 0.4988 0.4962 -0.51% 0.4961 -0.54%

Mid-West 0.5353 0.5351 -0.05% 0.5317 -0.67%

Southeast 0.4666 0.4662 -0.08% 0.4661 -0.10%

South 0.4580 0.4579 -0.02% 0.4576 -0.10%

Brazil 0.5280 0.5266 -0.26% 0.5259 -0.39%

Increased

government

expenditure

Constant

government

expenditure

Observed 2002

After Shock

Impacts on income inequality

Regional Aspects of the Economics of Climate

Change in Brazil

Economics of Climate Change in Brazil Modeling Logical Structure

Fipe - Fundação Instituto de Pesquisas Econômicas

85

Departamento de Economia, FEA-USP 86

Spatial CGE system integrated with other models (sequentially or semi-iteractively/soft links)

CGE locus in the integrated system

Scenarios of climate change(A2 e B2)

CGE system

Agricultureand land use

Energy Population

Economic impacts Social impacts


In the first part of the project, two scenarios (baselines) were produced

• Projections of economic variables: macroeconomic, sectoral and regional

• Time horizon: 2050

• Baselines: without GCC (temperature and precipitation)

Modelos do IPCC: HadAM3

RegCM3

Downscaling IPCC

Scenarios

A2, B2

HadRM3 Eta CCS

Regional models

Climatology

1961-90

PROBIO-IPCC Global models used: IPCC TAR (HadAM3)-Version 1

Maps and data of

climate

anomalies

2071-2100, A2, B2

Climatology

regional model

1961-90

Climate anomalies (future-

present), from regional mulimodel

ensemble Time slices 2071-2100,

A2, B2


The land-use model produces estimates of changes in the allocation of land to agriculture, pasture and forestry;…


90

Results: percentage changes in areas Scenario A2 - regions

2010-2040 2040-2070 2070-2100

lavoura pasto lavoura pasto lavoura pasto

Norte -2,4% 17,7% 17,9% 16,7% 44,1% 10,4%

Nordeste -27,6% 28,3% -18,9% 25,1% 31,8% 9,8%

Sudeste 7,0% 4,9% 11,1% 5,9% -7,6% 9,6%

Sul 27,9% -6,0% 30,4% -4,6% 33,4% -16,8%

Centro-

Oeste

-6,4% 8,4% -7,1% 10,2% -12% 9,3%


91

Results: percentage changes in areas Scenario B2 - regions

2010-2040 2040-2070 2070-2100

lavoura pasto lavoura pasto lavoura pasto

Norte 4,0% 13,0% 10,3% 15,5% 24,9% 12,8%

Nordeste -26,6% 25,5% -23,5% 25,1% 12,6% 14,1%

Sudeste 13,6% 3,5% 16,3% 3,7% -20,3% 13,6%

Sul 22,6% -2,7% 27,1% -1,7% 15,9% -8,6%

Centro-

Oeste

-5,1% 8,0% -9,1% 9,6% -15,2% 10,0%


… the agriculture productivity model produces estimates of productivity changes, by different crops;…

These physical changes are then translated into shocks in the spatial CGE system

“Physical” changes

• Changes in the allocation of land to agriculture, pasture and forestry

• Changes in productivity by crops

• Changes in the energy intensity use

Shocks in CGE variables

• Capital-augmenting technical change in agriculture and livestock (regional shocks)

• All-input-augmenting technical change in agriculture (regional shocks)

• Technical changes variables for import/domestic composites related to energy products (sectoral shocks)



Differential results across sectors and regions, and over time

• Economic growth (-)

• Welfare (-)

• Regional concentration (-)

• Regional inequality (-)

• Some sectors and regions may be positively affected

• Impacts are magnified over time

Costs (benefits) of GCC (1)

2008 2035 2050

Percentage difference

SMCG – without GCC

CMCG – with GCC

Costs (benefits) of GCC (2)

2008 2035 2050

SMCG – without GCC

CMCG – with GCC

Costs over time (PV of differences – marginal flows)


Macroeconomic outlook

• Adjusted to present values, discounted at a rate of 1% a

• year, these losses would range between R$ 719 billion and

• R$ 3.6 trillion, which would be equivalent to losing at least an

• entire year of growth over the next 40 years

• The average Brazilian citizen would lose between R$ 534

• (US$ 291) and R$ 1,603 (US$ 874). The present 2008 value of

• the reductions in Brazilian consumption accumulated to 2050

• would range between R$ 6,000 and R$ 18,000, representing

• 60% to 180% of current per capita annual consumption


Regional outlook

• The regions most vulnerable to climate change in Brazil are

• the Amazon and the Northeast.

• In the Amazon, temperatures may increase 7-8°C by 2100, possibly leading to a radical change in the Amazon forest – the

• so-called “savanization”. It is estimated that climate changes

• would bring about a 40% reduction in the forest cover in the

• south-southeast-east region of the Amazon, being substituted

• by a savanna biome.

• In the Northeast, rainfall would tend to drop 2-2.5 mm/day

• by 2100, causing agricultural losses in all states of the region.

• The water deficit would lead to a 25% reduction in pasture for

• slaughter cattle, thus stimulating a return to low-output cattle ranching.


Regional outlook

• The decline in rainfall would affect river flows of the

• Northeast basins, such as the Parnaíba and the East Atlantic,

• important for electric power generation, with flows dropping by

• up to 90% between 2070 and 2100.

• There would be severe losses for agriculture in all states,

• with the exception of the colder South-Southeast states, where

• temperatures would be less severe


Sectoral outlook

• Water resources. The projected results would be alarming for

• certain basins, especially in the Northeast region, with a sharp

• reduction in flows by 2100.

• Electric power. Greater uncertainty in the hydroelectric power

• generation capacity, with firm energy reductions ranging

• between 31.5% and 29.3%. The more pronounced impacts would be felt in the North and Northeast. In the South and Southeast the impacts would be minimal or even positive, but would not offset the losses in the North and Northeast.


Sectoral outlook

• Agriculture and livestock. With the exception of sugarcane,

• reductions in low-risk production areas would affect all

• crops, especially soybeans (-34% to -30%), corn (-15%), and

• coffee (-17% to -18%). Productivity would drop particularly for

• subsistence crops in the Northeast.

• Coastal zone. Under the worst scenario of sea level rise

• and extreme meteorological events, the estimated value of

• infrastructure and properties at risk along the Brazilian coast

• ranges between R$ 136 and R$ 207.5 billion.


103

Spatial distribution of costs of GCC – B2

(in % of the projected GRP without GCC) (1)

Regional costs (benefits) of GCC – B2 (1)


104

-12.0% -10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0% 4.0%

MT

AL

MA

PI

MS

ES

CE

TO

PE

RO

RN

RR

AM

AP

GO

MG

PA

SP

SC

Brasil

AC

DF

RJ

PB

BA

RS

PR

% do PIB EstadualDiscount rate: 1%


105

State costs vs. Capital-city costs – B2

(in % of the projected GRP without GCC) (1)

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

% d

o P

IB E

stad

ual

/Cap

ital

UF Capital

Regional costs (benefitss) of GCC (2)


Taxa de desconto: 1%

What next?

• There is a long way to go until poor regions can be competitive at their present conditions. Better human capital is of course a necessary condition, but a richer entrepreneurship culture is necessary to catalyze the new stimuli thrown at the region by the cash transfers

• The important regional effects of the social policies are sizeable and very welcome.

• They seem to have teamed up well with other programs to attract manufacturing to peripheral regions, and the final results are positive.

• Considering the short-term, the social programs are already the most effective forms of regional policy ever implemented in the country

What next?

• The challenge of turning the short-term effects into sustainable social and regional changes lies on – the maintenance of the programs with their present intensity

and – on the qualification of the human capital to take the next step

out of the social programs.

• Education and health conditionalities are good first steps, but mere increases in access to schooling will probably not do the job.

• Also, it does not seem reasonable to believe that increasing the production of wage goods can pull out a region from stagnation and transform it into a competitive and dynamic area.

Thanks!

[email protected]

Podem fecundar o sistema produtivo das regiões pobres?

Department of Economics, University of Sao Paulo 113

Impactos nas regiões

Brazil Northeast Southeast Mid-West North South

Production -0,48% 4,6% -0,82% -9,51% 1,15% 0,51%

Income -1,78% 3,3% -2,01% -16,55% 0,15% -0,39%

1st Poorest 1,71% 5,96% -0.97% -12.77% 1,02% 0.37%

2nd Poorest 0,33% 4,96% -1,13% -13,13% 0.69% 0.21%

3rd Poorest -0,77% 3,60% -1,48% -14.61% 0.45% -0.07%

All others

lose more

All others

gain less

All others

lose more

All others

lose more

All others

gain less

All others

lose more

Desafios

Obrigado!

What next?

• The challenge of turning the short-term effects into sustainable social and regional changes lies on – the maintenance of the programs with their present intensity

and – on the qualification of the human capital to take the next step

out of the social programs.

• Education and health conditionalities are good first steps, but mere increases in access to schooling will probably not do the job.

• Also, it does not seem reasonable to believe that increasing the production of wage goods can pull out a region from stagnation and transform it into a competitive and dynamic area.

Desigualdade Regional

Indicadores de desigualdade regional

Table 2 - Indicators of regional inequality

Per Capita GDP respective to the national average

1939 2009

North 0.75 0.63

Northeast 0.48 0.48

Piauí St. 0.43 0.33

Southeast 1.41 1.31

S. Paulo St. 1.8 1.55

South 1.11 1.14

Mid-West 0.7 1.32*

* The capital city, Brasília, established in 1961 in the region, presents the

highest per capita income level in the country, 2.98 times the national average.

Accesso à internet (% de pessoas com 10 ou mais anos, 2005)

Posse de telefone celular (% de pessoas com 10 anos ou mais, 2005)

HDI, 2000

//upload.wikimedia.org/wikipedia/commons/4/4e/Idh_municipios_brasil.png

regional disparities in brazil disparities in brazil: recent trends and future possibilities carlos...

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