Poverty Dynamics in Brazil: Patterns, Associated Factors and Policy Challenges

POVERTY DYNAMICS IN

BRAZIL: PATTERNS,

ASSOCIATED FACTORS AND

POLICY CHALLENGES

Lead Authors:

Rogerio Bianchi Santarrosa

Anna Fruttero

Luis F. Lopez Calva

Maria Ana Lugo

With support by: Raul Andres Castaneda, Samantha Lach, Jordan Solomon

ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS

The team is grateful to Melanie Allwine, François

Bourguignon, Francisco Ferreira, Peter Lanjouw Jamele

Rigolini and Shabana Singh who collaborated with the team

and provided important inputs and comments. Maria

Concepcion Steta and Joana Da Silva also provided useful

comments and became a fundamental source of support in

the preparation of the final output. Background work for this

Report was presented at the author‘s workshop in

Washington DC in July 2012, George Washington University

Development Tea Seminar Series, and IPEA-Brasilia.

Economic growth and falling inequality

contributed 56% and 44%, respectively of

the decline in poverty between 2000 and

2010.

LAC achieved impressive gains in shared prosperity in the last 15 years, exceeding its past performance…

For the f irst t ime in 2011, the

middle class exceeds the poor, due

to growth (77%) and improved

income distribution (23%).

2000 2002 2004 2006 2008 2010

0

10

20

30

40

Headcount (%

) Middle Class

Vunerable

Poor

In Brazil, growth accounted for 54% of the

decline in poverty between 2001 and 2011.

While redistribution contributed 46% to

decrease poverty.

… over this period Brazil has experienced steady economic growth and substantial reduction of inequality

Pover ty in Brazi l has decl ined since

the 2003. In 2011, 24.5% of the

population was poor ($4 USD/day,

PPP 2005). By 2008, the middle

class outnumbered the poor.*

2000 2002 2004 2006 2008 2010

22

23

24

25

26

27

28

Per capita GDP (per day, PPP Constant 2005 $)

0.50

0.52

0.54

0.56

0.58

0.60

0.62

Gini Coefficient

GDP per capita/day

Gini

The middle class figure shown above is constructed under the World Bank definition, World Bank (2012) and was constructed

only to inform international comparisons.

Roland1

Slide 4

Roland1 Separate labels vulnerable / middle classRoland Clarke, 3/1/2013

Main Policy Questions

• Brasil sem miseria strategy has set as its goal to eliminate

extreme poverty in Brazil

• There are three main pillars in the strategy:

• “Active search” and income guarantee (reaching those who have been

excluded for different reasons)

• Minimum income guarantee

• Productive inclusion (those who leave poverty must be incorporated to

the productive world)

• Access to services (close existing coverage gaps in basic services)

Some questions arise

• What does it mean to “eliminate” poverty? (transient versus

chronic poverty)

• How to measure chronic poverty?

• What is the right “policy mix” to deal with both aspects of

poverty?

Methodology for Policy Use

• Use synthetic panels to characterize the different mobility

groups

• Use a combination of multidimensional and monetary

measures to distinguish the “chronic” from the “transient”

poor

Characterizing Mobility: Leavers and stayers

Founded optimism: Brazil’s impressive social record• Poverty has decreased through last decade, using both national and

international lines

Founded optimism: Brazil’s impressive social record• This result of a progressive income growth and declining inequality

Growth Incidence Curves for Brazil, 2003Growth Incidence Curves for Brazil, 2003Growth Incidence Curves for Brazil, 2003Growth Incidence Curves for Brazil, 2003----2011201120112011

Brazil and the Bottom 40% growth rate

Growth rate of income of the bottom 40% in LAC,

2000-2011

.

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

Brazil LAC

An

nu

alize

d G

row

th R

ate

An

nu

alize

d G

row

th R

ate

An

nu

alize

d G

row

th R

ate

An

nu

alize

d G

row

th R

ate

Annualized Growth Mean Income Bottom 40% Annualized Growth Mean Income

Who left, who stayed?Poverty dynamics: 2003-2011

• Using Methodology in Lanjouw, et al (2011), Cruces, et al

(2012) to construct synthetic panels from a series of cross

sections (PNAD)

• Lower and upper bounds

• 3 economic groups (following SAE’s Study):

- Poor (income below R$140 per month)

- Vulnerable (R$140 – R$250)

- Middle Class and Upper Class (R$250 - )

Destination: 2011

Poor Poor Poor Poor (0-140 Reais)

VulnerableVulnerableVulnerableVulnerable(140 – 250

Reais)

MiddleMiddleMiddleMiddle Class + Class + Class + Class + (250 Reais +)

Ori

gin

: 2

00

32

00

32

00

32

00

3


14.0% 6.7% 1.9%


Reais)

0.5% 7.0% 11.2%

MiddleMiddleMiddleMiddle Class + Class + Class + Class + (250 Reais+)

0.0% 0.9% 57.8%

TOTALTOTALTOTALTOTAL

2003200320032003

22.6%

18.7%

58.7%

100.0%TOTAL 2011TOTAL 2011TOTAL 2011TOTAL 2011 14.5% 14.6% 70.9%

NB: Results are lower bounds estimates


Destination: 2011



Reais)

MiddleMiddleMiddleMiddle Class + Class + Class + Class + (250 Reais +)

Ori

gin

: 2

00

32

00

32

00

32

00

3


14.0% 6.7% 1.9%


Reais)

0.5% 7.0% 11.2%

MiddleMiddleMiddleMiddle Class + Class + Class + Class + (250 Reais+)

0.0% 0.9% 57.8%

TOTALTOTALTOTALTOTAL

2003200320032003

22.6%

18.7%

58.7%

100.0%TOTAL 2011TOTAL 2011TOTAL 2011TOTAL 2011 14.5% 14.6% 70.9%

CHRONICALLY

POORPOVERTY LEAVERS

POVERTY

ENTRANTS

NB: Results are lower bounds estimates


Profile of poverty leavers

• Exiting poverty in Brazil between 2003 and 2011 is highlycorrelated with educational achievement, even more than inthe previous poverty reduction period of the early 1990s

• Probability to exit poverty in the 2000s is greater inhouseholds headed by women

• Those who manage to get out of poverty systematically showbetter labor market conditions, starting out in the formaleconomy as employees or employers.

• A larger share of people by ethnic groups and regions (urbanand rural) were able to exit poverty, vis-à-vis the 1990s

What does it mean to eliminate poverty?

But... challenges remain

• There is however, an important number of people who remain poorin monetary terms, as well as in terms of access to basic services

• About 4444....7777 percentpercentpercentpercent of the population lives below the officialextreme poverty line of R$70 (Reais) per month (Pnad 2011); theyare about 9999 millionmillionmillionmillion who remain in extreme poverty

• 12121212....4444 percentpercentpercentpercent live below the R$140 official poverty line (Pnad2011). This amounts to more than 24242424 millionmillionmillionmillion Brazilians whom,despite the efforts of social programs, continue to live in poverty

• Eliminating extreme poverty, within the context of the BSMprogram, necessarily entails the identification of the chronicallypoor

Chronic versus transient poverty

But what does it mean to live in chronic poverty?Concept and Measurement

• One way to approach this issue is to use the idea of

“ultra poverty”: persistence of poverty over time, depth

of poverty, and multidimensionality (complexity)

• The typical notion of “chronicity” refers mainly to

persistence. Two approaches:• The components components components components approach tries to distinguish permanent versus

transitory income generation, and compares to a standard

• The spellsspellsspellsspells approach defines it in terms of number of periods in which

the income is below the standard

Using Non-Monetary Dimensions to Approximate Chronic Poverty in Brazil

• Social programs in Brazil, including within the BSM strategy, relyprimarily on income-based indicators to select the beneficiaries

• Given their volatility and issues related to incentives, incomeindicators may be complemented with alternative methodologiesto target social programs in the most efficient and equitable way.

• Multidimensional measures of poverty could be a good instrumentto enhance the incidence of programs.

• Those who are both monetary and multidimensionally poor in oneperiod are systematically –and considerably—more likely to havebeen monetary poor in other periods.

• Association between the complexity (multidimensionality) andpersistence aspects of the ultra poverty concept

Using Non-Monetary Dimensions to Approximate Chronic Poverty in Brazil

• Multidimensional measurement of poverty using a dual

cut-off (Alkire and Foster, 2011)

• The first cut-off, the traditional poverty line “z”, identifies

whether individuals are poor within a given dimension

• The second cutoff, the dimensional one, establishes the

proportion of dimensions “k”, in which an individual

must be identified as poor to be considered multi-

dimensionally poor

Chronicity of Poverty

• The main idea in the estimation of exit from chronic poverty

is that the time spent in poverty (or the duration in poverty)

affects whether an individual will leave poverty in a given

period.

• The longer a person remains in poverty, the less likely it is that she will

exit poverty (this is the poverty trap argument)

• Looking at whether an individual will leave poverty today

depends on an a number of individual factors (level of

education, etc.) but also on the number of years she has

been poor.

Conjectures

• First: people who are both monetary and multidimensionally

poor are more likely to have been poor in previous periods,

compared to those who are monetary but NOT

multidimensionally poor today

• The longer you are poor (both Monetary and MPI) the less

likely you are to escape monetary poverty in the future

Chile – Changes in probabilities

BaseBaseBaseBase modelmodelmodelmodel: the household head

is male, is married, and has

lower-secondary education; in

2002 he was a skilled manual

worker, living in an urban area of

the Metropolitan region; he has

faced shocks between 2001 and

2006; andandandand hehehehe waswaswaswas notnotnotnot incomeincomeincomeincome

poorpoorpoorpoor andandandand multidimensionalmultidimensionalmultidimensionalmultidimensional poorpoorpoorpoor

atatatat thethethethe samesamesamesame timetimetimetime inininin 2001200120012001....

0.489

0.190

0.068

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Income poor & multid. poor in

2001

Income poor but non-multid.

poor in 2001

Non-income poor but multid.

poor in 2001

Marginal effects for being income poor in Marginal effects for being income poor in Marginal effects for being income poor in Marginal effects for being income poor in

2006200620062006

0.061

0.128

0.230

0.533

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Base Non-income poor but

multid. poor in 2001

Income poor but non-


Income poor & multid. poor

in 2001

0.067

0.169

0.471

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50


poor in 2001


poor in 2001


2001

Magnitude of changes in probability Magnitude of changes in probability Magnitude of changes in probability Magnitude of changes in probability Probabilities of being incomeProbabilities of being incomeProbabilities of being incomeProbabilities of being income----poor in 2006poor in 2006poor in 2006poor in 2006

0.115

0.264

0.354

0.494

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Base Non-income poor but


Income poor but non-


Income poor & multid. poor

in 2002

Mexico – Changes in probabilities

BaseBaseBaseBase modelmodelmodelmodel: the household head

is male, is married, and has

lower-secondary education; in

2002 he was a skilled manual

worker, living in an urban area of

the Western region; he has faced

shocks between 2002 and

2005; andandandand hehehehe waswaswaswas notnotnotnot incomeincomeincomeincome

poorpoorpoorpoor andandandand multidimensionalmultidimensionalmultidimensionalmultidimensional poorpoorpoorpoor

atatatat thethethethe samesamesamesame timetimetimetime inininin 2002200220022002....

Marginal effects for being income poor in Marginal effects for being income poor in Marginal effects for being income poor in Marginal effects for being income poor in

20052005200520050.426

0.258

0.143

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45


2002


poor in 2002


poor in 2002

Probabilities of being incomeProbabilities of being incomeProbabilities of being incomeProbabilities of being income----poor in 2005poor in 2005poor in 2005poor in 2005 Magnitude of changes in probability Magnitude of changes in probability Magnitude of changes in probability Magnitude of changes in probability

0.149

0.239

0.379

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40


poor in 2002


poor in 2002


2002

Multidimensional and income poverty

• Chronic poverty can be identified in the absence of panel data using a multidimensional approach to poverty

• Use of synthetic panel over two periods, from 1999 to 2011

• Results suggest that people who were not only income poor, but also multi-dimensionally poor in the initial period had a significantly lower probability to emerge from monetary poverty.


0

3238

6476

012345678

Non-monetary poor but deprived

8.2%Better off

65.1%

$R70

$R140

1999199919991999

Transiently poor

11.9%

Chronic poor

14.7%

Inco

me

po

or

Multi-dimensionally poor

Ho

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ld p

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Number of deprivationsNumber of deprivationsNumber of deprivationsNumber of deprivations


0

3238

6476

012345678

Non-monetary poor but deprived

4.1%Better off

83.5%

$R70

$R140

2011201120112011

Transiently poor

9.1%

Chronic poor

3.3%

Inco

me

po

or

Multi-dimensionally poor

Ho

use

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ld p

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a in

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Number of deprivationsNumber of deprivationsNumber of deprivationsNumber of deprivations

MPI and multidimensional targeting vis-à-vis income alone

• Groups that are reached using income and multidimensional poverty status (MPI) thresholds show:• more severe levels of deprivations and possess on average less assets

• significantly lower level of education (two years of schooling on average), substantively higher illiteracy rates (~50 percent) and lower enrollment rates for children

• While transient poverty may be largely associated with temporary unemployment, chronic poverty—identified through the multidimensional measures—is related to lower productivity and lower wages

• Using MPI criteria to fine-tune identification leads to a higher concentration of target groups in rural areas, where the level of deprivations is higher.

Conclusion

• Social indicators have shown remarkable progress in Brazil

during the last decade; nevertheless, many individuals remain

who have not benefited from Brazil’s rapid development

• This study looks at ways to better characterize the different

types of poverty, with existing data, in order to select the

different instruments to reach them effectively

• The transient poor will require fundamentally policies related to

productivity and income-generation capacity

Technology

Poverty Dynamics in Brazil: Patterns, Associated Factors and Policy Challenges