Lecture 28: Cash Transfers - USP · 2. Structural Regional Policy Variables - Per Capita...
Transcript of Lecture 28: Cash Transfers - USP · 2. Structural Regional Policy Variables - Per Capita...
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Prof. Eduardo A. Haddad
Lecture 28: Cash Transfers
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The realm of regional policy
Lula’s regional policy
Pro-vote?
Pro-growth?
Pro-poor?
Outline
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General indicators of regional concentration in Brazil
Table 1 - General indicators of regional concentration in Brazil
Share of
national
territory (%)
1940 2000 1939 2004 1939 2004
North 45.3 3.5 7.6 2.7 5.3 0.75 0.67
Northeast 18.3 35.0 28.1 16.9 14.1 0.48 0.51
Piaui State 2.9 2.0 1.7 0.9 0.5 0.43 0.30
Southeast 10.9 44.5 42.6 63.0 54.9 1.41 1.29
São Paulo State 2.9 17.5 21.8 31.3 30.9 1.80 1.41
South 6.8 13.9 14.8 15.3 18.2 1.11 1.24
Center-West 18.9 3.1 6.8* 2.1 7.5 0.70 1.07
Share of national
population (%)
Share of
national GDP
(%)
Per capita income
in relation to
national average
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The analysis of different indicators of production (growth) and welfare (development) in Brazil...
Exploratory analysis of spatial distribution of growth and human
development indicators in Brazil:
Gross Regional Product – GRP
“Índice FIRJAN de Desenvolvimento Municipal” – IFDM
GRP measures the total amount of final goods and services
produced within the limits of a geographical area (country, state,
municipality), by residents and non-residents.
The IFDM, created by Sistema Firjan to monitor the evolution of
Brazilian municipalities, considers (with the same weights) the three
main dimensions of human development: employment and income;
education, and health.
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... reveals the existence of a persistent regional inequality in terms of per capita GRP...
Relative position of per capita GRP with relation to national per capita GDP -- 2008
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... and human development in its various dimensions
IFDM – 2007 IFDM – Component “employment and income” – 2007
IFDM – Component “education” – 2007 IFDM – Component “health” – 2007
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In extreme situations, it is clearer the regional dualism in in the country and the “northeast issue”
Municiaplities with per capita GRP less than 30% below the national average - 2008
Municiaplities with per capita GRP more than 100% above the national average - 2008
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Regional policy encompasses two distinct dimensions
The realm of regional policy
Physical capital
Human capital
Geographic targeted social policies (transfers)
Fundamental concepts
Sustainability
Endogeneity
Stakeholders participation
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Where did it stand in the first term? (second as well!)
Physical capital
Human capital
Transfers
Endogeneity
Sustainability
Participation
The realm of regional policy Key concepts
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The realm of regional policy
Lula’s regional policy
Pro-vote?
Pro-growth?
Pro-poor?
Outline
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Nothing much has been done in the first four years of Lula’s administration
Nowadays, the regional policy carried out by the central
government consists of isolated subsidies and industrial
incentives to growth centers, in addition to constitutional
transfers to less developed regions and rural areas.
In terms of proper regional policy, central government relied
only on constitutional intergovernmental transfers through
regional funds – FNE, FNO, FCO – and rural pensions.
Key ingredient: lack of coordination!
However, the central government has been engaged in an
effort to design and implement social compensatory policies
with a strong spatial dimension.
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The Bolsa Família program has a strong spatial dimension
The pro-poor “Bolsa Família Program” is a program that provides direct income transfers to poor (with per capita income between BRL 60,01 and BRL 120,00) and extreme poor households (with per capita income below BRL 60,00). Given the geographical distribution of poor households in the country, targeting benefits to the poor reflects an implicit concern with regional disparities in the country. Even though it cannot be considered an explicit strategy of geographic targeting to reduce poverty, it may achieve the goal of classical regional policies – namely, the reduction of regional disparities – through direct income transfers to poor households, which happen to be concentrated in poorer regions. However, this remains to be tested.
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Spatial distribution of poor households
Region Number of Poor Households %
North 1,574,094 0.0917
Northeast 7,140,519 0.4158
South 2,006,596 0.1169
Southeast 5,342,975 0.3111
Mid-West 1,107,909 0.0645
Total 17,172,093 1.0000
Households eligible for benefits from Bolsa Família, 2000
Source: Demographic Census, 2000
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The realm of regional policy
Lula’s regional policy
Pro-vote?
Pro-growth?
Pro-poor?
Outline
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What did the ballots tell us?
We evaluate the main determinants of Lula’s voting in the first
round of the 2006 presidential elections (data by municipality)
Lula’s voting (% of Lula’s voting in relation to total voting)
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Formal tests suggest that Lula’s voting was not randomly distributed in space
Moran Scatterplot and Global Moran’s I Statistics for Lula’s Performance in the
First Round of the 2006 Presidential Elections Moran Significance Map for Lula’s Voting
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Paradoxical result (?)
Does Lula’s performance reflect his efforts in the first mandate
to fight regional inequality or is there something else behind
this seemingly paradoxical result, which shows that a
government without any concrete regional policy action
achieved its best evaluation in the regions that were left
behind?
Need to investigate the main determinants of Lula’s
performance in the 2006 elections!
Tool: spatial econometric models
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The model
The dependent variable is the percentage of Lula’s voting in the first
round of the presidential elections.
We considered four groups of covariates in our models:
1. Spatial Structure Variables - Human Development Index; Gini
Coefficient; Per Capita GRP (all for year 2000)
2. Structural Regional Policy Variables - Per Capita Constitutional
Transfers; Per Capita Rural Pension Payments (both in 2006)
3. Social Policy Variables - Per Capita Income from Bolsa Família,
in 2006; Number of Households with Per Capita Income below R$
120,00, in 2000
4. Economic and Political Variables - Dummy for Mayor affiliated
to PSDB; Share of Agriculture in GDP, in 2002
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The results
Noteworthy is the robustness of the social policy variables. Municipalities with higher per capita transfers through the “Bolsa Família Program” and with the potential for its consolidation in the future, presented a positive evaluation of the Lula’s first mandate translated into greater proportion of votes.
OLS Spatial Lag Spatial Error
W_Lula - (+) -
Constant (+) (+) (+)
HDI (-) n.s. (-)
Gini (+) n.s. (+)
GDP (per capita) (-) (-) (-)
Constitutional transfers (per capita) (-) (-) (-)
Bolsa Familia income (per capita) (+) (+) (+)
Rural pensions (per capita) (-) (-) (-)
Number of poor households (+) (+) (+)
Dummy_PSDB (-) (-) n.s.
Share_Agriculture (-) (-) n.s.
Lambda - - (+)
CoefficientsModel
Qualitative estimation results (dependent variable: % Lula’s voting)
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The realm of regional policy
Lula’s regional policy
Pro-vote? YES!
Pro-growth?
Pro-poor?
Outline
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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$/month): from zero income to 400 (5.3% of total national household income); 400 – 600 (5.4%); 600 – 1,000 (11.5%); 1,000 – 1,200 (5.3%); 1,200 – 1,600 (8.9%); 1,600 – 2,000 (8.3%); 2,000 – 3,000 (13.7%); 3,000 – 4,000 (9.7%); 4,000 – 6,000 (11.9%); and 6,000 and over (19.8%). The household expenditure patterns for each income bracket in each region are taken into account. Model captures direct, indirect and induced effects.
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Patterns of consumption
0,0
0,1
0,2
0,3
0,4
1 2 3 4 5 6 7 8 9 10
Income Classes
Manufactured goods
Manufactured Food
Rent
Transportation
0,0
0,1
0,2
0,3
1 2 3 4 5 6 7 8 9 10
Income Classes
Services to Households
Services
Trade
Communication
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What were the amounts involved in each region?
2002 2003 2004 2005 2006 Lula's(1) (2) Administration
R$ 1,000 of 2002
Mid-West 130,797 219,383 226,801 300,167 376,739 1,123,090
North 119,168 242,595 482,206 618,950 805,087 2,148,839
Northeast 1,169,922 1,676,519 3,111,165 3,623,624 4,281,900 12,693,208
South 231,267 414,757 525,039 692,920 750,044 2,382,759
Southeast 756,846 891,746 1,247,910 1,720,518 1,964,509 5,824,682
Total 2,408,000 3,445,000 5,593,121 6,956,179 8,178,279 24,172,579
Regional shares
Mid-West 5.4% 6.4% 4.1% 4.3% 4.6% 4.6%
North 4.9% 7.0% 8.6% 8.9% 9.8% 8.9%
Northeast 48.6% 48.7% 55.6% 52.1% 52.4% 52.5%
South 9.6% 12.0% 9.4% 10.0% 9.2% 9.9%
Southeast 31.4% 25.9% 22.3% 24.7% 24.0% 24.1%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
(2) PNAD 2003, special segment on social programs
(1) Based on "interests, profits, dividends and others" received by individuals with total earnings below R$ 120 in the Norttheast, R$ 130 in the North, and
R$ 140 in the other three regions
(*) Untill 2003 payments to auxílio-gás, bolsa alimentação, bolsa escola and cartão alimentação were summed. These separate programs were unified in
2004 under the Bolsa-Família program. Official data from 2004 on.
Government transfers to families by region - Bolsa Família (*)
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Simulation strategy
1. Extra money was introduced in the region as an absolute
increase in government expenditure.
2. Extra money had to come from reduced government current
expenditures.
The size of the shock simulated is of R$ 24.172 billions,
which represents 0.45 % of national GDP, 0.82% of national
disposable income, and 13.4% for the poorest income
bracket.
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Not pro-growth, but pro-regional equity!
North Northeast Mid-West Southeast South Brazil North Northeast Mid-West Southeast South Brazil
Agriculture 5.31% 17.1% 4.21% 4.40% 4.65% 6.26% 3.21% 14.1% -0.27% 1.34% 2.14% 3.19%
Mining 1.53% 4.7% 1.46% 1.91% 1.10% 2.13% 0.32% 2.2% -1.63% 0.21% 0.11% 0.40%
Metal 1.20% 2.3% 1.45% 1.35% 1.64% 1.45% 0.08% 1.3% -1.68% 0.00% 0.22% 0.12%
Machinery 1.54% 6.0% 2.46% 1.52% 1.20% 1.56% 0.40% 4.1% -8.39% 0.05% 0.26% 0.19%
Vehicles 1.06% 4.6% 1.32% 1.49% 1.53% 1.53% 0.01% 2.3% -4.59% -0.78% -0.40% -0.62%
Wood & Furniture 3.97% 9.0% 2.90% 2.27% 2.48% 2.75% 1.36% 5.4% -8.19% -0.95% 0.22% -0.32%
Chemicals 4.10% 5.8% 3.68% 3.20% 3.06% 3.62% 1.21% 3.5% -5.78% 0.05% 0.45% 0.60%
Textiles 4.89% 7.4% 4.34% 4.19% 2.97% 4.32% 2.37% 4.9% -7.35% 0.45% 0.87% 1.10%
Manfactured Food 8.22% 20.9% 4.25% 4.63% 5.34% 6.62% 5.20% 17.3% -1.11% 1.69% 2.93% 3.41%
Other Manufacturing 1.68% 8.2% 2.36% 2.20% 2.27% 2.43% -0.46% 4.4% -8.52% -1.30% -0.30% -0.80%
Public Utilities 5.47% 9.4% 2.95% 3.06% 3.70% 4.27% 2.77% 6.9% -11.04% -0.52% 1.16% 0.16%
Construction 0.20% 0.4% 0.17% 0.20% 0.24% 0.23% 0.06% 0.2% -1.03% -0.08% 0.04% -0.09%
Commerce 4.31% 7.6% 1.89% 1.95% 2.65% 2.92% 1.29% 4.9% -8.61% -0.92% 0.20% -0.60%
Transportation 7.46% 14.4% 3.00% 2.60% 2.97% 4.66% 4.72% 11.5% -8.79% -0.43% 0.91% 1.23%
Communication 5.17% 6.9% 2.01% 2.17% 2.81% 3.06% 2.26% 4.3% -12.31% -1.11% 0.33% -0.92%
Financial Services 2.69% 3.5% 0.51% 0.72% 0.93% 1.02% 0.92% 2.1% -3.68% -0.42% 0.07% -0.41%
Services to Families 3.34% 6.8% 1.34% 1.69% 2.38% 2.65% 0.97% 4.1% -17.37% -1.90% -0.30% -1.80%
Services to Business 3.14% 6.1% 2.07% 1.00% 2.31% 1.65% -0.40% 2.1% -22.74% -1.25% -0.70% -2.02%
Rent 11.85% 14.0% 5.59% 3.36% 4.23% 4.95% 7.57% 10.6% -20.32% -0.02% 1.88% 0.74%
Public Administration 0.10% 0.2% 0.03% 0.15% 0.16% 0.15% -2.94% -3.4% -21.77% -7.24% -4.24% -8.16%
Non-mercantile Services 5.61% 7.7% 1.02% 1.49% 2.61% 2.55% 1.21% 4.3% -18.29% -1.46% 0.19% -1.75%
All Sectors 3.35% 7.2% 2.15% 2.13% 2.77% 2.96% 1.15% 4.6% -9.51% -0.82% 0.51% -0.48%
Increased government expenditure Constant government expenditure
Impacts on production, by sector and region
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The realm of regional policy
Lula’s regional policy
Pro-vote? YES!
Pro-growth? NO!
Pro-poor?
Outline
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Pro-poor? Yes,...
Impacts on household income by region
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... and pro-income equity
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
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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
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In summary
Pro-vote? YES!
Pro-growth? NO!
Pro-regional equity? YES!
Pro-poor? YES!
Pro-income equity? YES!
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But how about long-run considerations?
Total
Poverty
Infrastructure
Education
Transfers
Urban
poverty
Education
Infrastructure
Transfers
Rural poverty
Infrastructure
Education
Transfers
Ranking
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Challenge: to promote development in depressed areas
What is an economically depressed area?
Low rates of economic growth
Lack of labor absorption (high rates of unemployment,
underemployment and disguised unemployment)
High poverty rates and social problems
Strong socioeconomic and intra-regional (between rural
and urban zones) unbalances
Economic and social infrastructure in bad conditions
High degree of dependence on government
transfers, both for residents and local governments
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Where are they?
IFDM – Component “employment and income” –
Northeast and Southeast, 2007
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What happens to the possibilities of endogenous growth of a society in which more than one third of its members depend upon compensatory social policies?
“No Brasil, desde a implantação dos direitos sociais previstos
na Constituição Federal de 1988, tem-se mais de 11 milhões
de famílias no programa Bolsa-Família, mais de 7 milhões de
benefícios da Previdência Social e mais de 5 milhões de idosos
e deficientes na Lei Orgânica da Assistência Social (LOAS). Aos
programas sociais do Governo Federal, somam-se aqueles de
menor escala coordenados e implementados por governos
Estaduais e Municipais do País. Não é difícil, pois, chegar a
um número superior a 70 milhões de brasileiros que
sobrevivem, direta ou indiretamente, sob a cobertura
das políticas sociais compensatórias, recorrentes do ponto
de vista fiscal e inflexíveis do ponto de vista político.”
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Typology of products that will lead growth in Brazilian micro-regions, 2007-2030