Economic Growth and Poverty Dynamics in Latin America
Maximo Torero
Director – MTID
International Food Policy Research Institute
m.torero@cgiar.org
Globalization, Macroeconomic Imbalances and South America’s Potential to Be the World’s Food Basket ICAE/IAAE Pre‐Congress Workshop Foz do Iguaçu, Brazil, August 18, 2012 Brazil’s inequality in the last 30 years
Gini Index, per capita household income
Over the last decade, inequality has fallen in 13
out 18 LAC countries for this data is available
Change in the Gini coefficient (points), CIRCA 2000-2009
The cost of high inequality
High inequality may matter for four sets of reasons: 1. It makes (some) people unhappy 2. Political or power inequalities may corrupt institutions 3. When capital markets are imperfect, wealth inequality may harm future growth 4. For a given growth rate, higher inequality slows down poverty reduction. The cost of high inequality
The cost of high inequality
The cost of high inequality
The cost of high inequality
The cost of high inequality
Let’s take Peru as an Example
What has happened in terms of growth?
In the past 60 years Peru has experienced:
• First, a ‘purely’ liberal period of economic
growth (1951-1970)
• Second, an interventionist period of economic
downturn and civil strain (1971-1990)
• Third, a period of progressively inclusive
liberal economic growth (1991-2010)
-5
-10
-15
* Base Year = 1994
Source: Peruvian Central Bank
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
1963
1961
1959
1957
1955
1953
1951
% Change of Real GDP
Peru: GDP Growth
Peru: GDP Growth 1951-2009
15
10
5
0
Source: Peruvian Central Bank
2008
2010
2006
2004
2000
2002
1998
1994
1996
1992
1990
1986
1988
1984
1982
1978
1980
1976
1972
1974
1970
1968
1964
1966
1962
1960
1956
1958
1954
1950
1952
Peru: Real GDP per capita 1950-2008
(in constant 1994 soles)
8,000
7,000
6,000
5,000
4,000
3,000
2,000
70
7,000
65
6,500
60
6,000
55
5,500
50
5,000
45
4,500
40
4,000
35
3,500
30
Poverty Rate (%)
7,500
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Per capita GDP (constant Soles of 1994)
Poverty Rate and per capita GDP, 1970-2010
Per capita GDP
Poverty Rate
Source: Per capita GDP from Peruvian Central Bank. Poverty rates from Escobal, Saavedra and Torero 1998 (1971/1972
rate estimated from ENCA 1972; 1985/1986, 1994 and 1996 from Peruvian LSMS) and National Statistics Institute (1999,
2001-2010).
Accelerated growth, relative to other
countries in the region
GDP: Average Annual Growth Rate
(USD, Constant 2000 prices)
1991‐2000
2001‐2010
Argentina
4.5
4.3
Brazil
2.5
3.6
Chile
6.4
3.7
Colombia
2.7
4.1
Mexico
3.5
1.8
Peru
4.0
5.7
Latin America & Caribbean
3.2
3.3
Source: World Bank.
… and even when compared to other regions
GDP Growth Rate by Region: 2000‐2010
12
Peru
10
LAC
Annual Growth Rate (%)
8
6
4
2
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
‐2
World
‐4
‐6
Year
Asia del Este & Pacifico
East Asia & Pacific
Source: World Bank
Europa & Asia Central
Europe
Latin America
América Latina & Caribe
Mundo &
& Central
Asia
Caribbean
Perú
Components of GDP Growth
(percentage points)
TFP
Source: Peruvian Central Bank
Capital
Labor
Determinants of growth in Peru in the
past two decades
• Country-level longitudinal time-series studies: Vega
Centeno (1989, 1997)
• “Heterodox” studies: Carranza et.al (2005), Hausmann
& Klinger (2009)
• Studies using multi-country panel data, with more robust
estimates that explicitly quantify the contribution of each
variable to growth in national GDP per capita: Loayza et
al. (2008), Loayza (2008).
Sources of per capita GDP growth 1980-1990
(growth rates)
Actual Rate
Growth Rate
Projected Rate
Conditional Convergence
Cyclical reversion
Structural reforms
Macro stabilization
External conditions
Source: Loayza (2008)
Source: Iván Rivera (2011). ¿Puede el Perú llegar a ser desarrollado en una generación? Oportunidades y obstáculos para lograrlo. Economía Vol. XXXIV, N° 67, semestre enerojunio 2011, pp. 163-200 / ISSN 0254-4415
Sources of per capita GDP growth 1990‐2005 (growth rates)
Actual Rate
Growth Rate
Projected Rate
Conditional Convergence
Cyclical reversion
Structural reforms
Macro stabilization
External conditions
Source: Loayza (2008)
Source: Iván Rivera (2011). ¿Puede el Perú llegar a ser desarrollado en una generación? Oportunidades y obstáculos para lograrlo. Economía Vol. XXXIV, N° 67, semestre enerojunio 2011, pp. 163-200 / ISSN 0254-4415
Notable improvements in macroeconomic
stability and openness to trade
Inflation rate and trade openness: 1961-2010
(Annual averages)
1961‐1970
1971‐1980
1981‐1990
1991‐2000
2001‐2010
Inflation
9.4
32.0
1223.6
60.1
2.4
Openness
27.4
24.9
22.7
30.4
38.6
Note: Trade openness is the sum of exports plus imports of goods and services as a
percentage of GDP (in new soles at constant 1994 prices)
Source: Peruvian Central Bank.
The “Peruvian Miracle”?
Examples of local media coverage…
El Comercio. Front Page, 07/02/2010. El Comercio. Front Page, 07/02/2010. Caretas (Political
Magazine). Front
Page, 07/17/2008
22
Examples of international media coverage…
The Economist. 05/25/2009
Financial Times, Special Report, 09/22/2010
23
However, there is some
other, less encouraging
evidence at the country
level….
Comparative labor flexibility
The Challenge of Flexibility
Labor market flexibility indicators (0=most flexible, 100=most rigid)
Peru
Uruguay
Colombia
Index of rigidity
of employment
Ease of firing
index
Chile
Latin America & Caribbean
East Asia
Source: Doing Business and IPE
Difficulty of firing
index
Investment gap in infrastructure
Sector
2008 Gap
2008
Gap
(Millions of US
Dollars)
%
Transport
Airports
Ports
Railways
Road Networks
13961
571
3600
2415
7375
37.0
Sanitation
Drinking water
Sewerage
Wastewater treatment
6306
2667
2101
1538
16.7
Electricity
Generation
Transmission
Coverage
8326
5183
1072
2071
22.0
Natural Gas
3721
9.9
Telecommunications
Telephone (landlines)
Telephone (mobile)
5446
1344
4102
14.4
37760
100.0
Total
Source: CADE 2010
(US$ Millions)
Changes in inequality (Gini) in LAC
from improved infrastructure
Improving to the level of LAC Improving to the median level in Leader (Costa Rica)
East Asia (South Korea)
Stock
Quality
Total
Stock
Quality
Total
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
‐0.03
‐0.08
‐0.03
‐0.03
‐0.04
‐
‐0.01
‐0.01
‐0.02
0.00
‐0.02
‐
‐0.03
‐0.09
‐0.06
‐0.03
‐0.06
‐
‐0.05
‐0.10
‐0.05
‐0.05
‐0.06
‐0.02
‐0.02
‐0.02
‐0.03
‐0.01
‐0.03
‐0.01
‐0.06
‐0.12
‐0.09
‐0.06
‐0.09
‐0.03
Dominican Rep.
Ecuador
Guatemala
Honduras
Mexico
Nicaragua
Panama
Peru
El Salvador
Uruguay
Venezuela
‐0.03
‐0.04
‐0.07
‐0.07
‐0.03
‐0.07
‐0.03
‐0.06
‐0.03
‐0.02
‐0.02
0.00
‐0.02
‐0.01
‐0.02
0.00
‐0.02
0.00
‐0.01
‐0.01
‐0.01
‐0.01
‐0.03
‐0.06
‐0.08
‐0.09
‐0.03
‐0.10
‐0.03
‐0.07
‐0.04
‐0.02
‐0.03
‐0.05
‐0.06
‐0.09
‐0.09
‐0.05
‐0.09
‐0.05
‐0.08
‐0.06
‐0.04
‐0.04
‐0.01
‐0.03
‐0.02
‐0.03
‐0.01
‐0.03
‐0.01
‐0.02
‐0.02
‐0.02
‐0.02
‐0.06
‐0.09
‐0.11
‐0.12
‐0.06
‐0.13
‐0.10
‐0.10
‐0.07
‐0.05
‐0.06
Source: Calderon & Serven 2004
Changes in the income distribution
(Simulated)
0.006
Agua
+&
Water
Electicity
electricidad
density
densidad
0.005
0.004
Agua +,
Water
electricidad
Electicity
&+
desagüe
Sewage
0.003
All
Todos los
activos
Sólo
agua
Water
only
0.002
0.001
Noactivos
action
Sin
0.000
0
100
200
300
400
ingreso per
pc del
hogar
simulado
Household income
capita
(simulated)
Source: Escobal and Torero, 2004.
500
600
• Inequality has partially decreased (measured through the Gini coefficient). • However, these reductions have been underwhelming relative to the economy’s strong performance.
Gini Coefficient, 1981-2006
58
56
54
52
50
48
46
44
42
Source: Jaramillo and Saavedra (2011)
Source: Milanovic (2010) 2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
40
30
And, overall, it remains a highly unequal
country
Per capita monthly income by decile and area
2007
2009
3,000
Metro Lima
2,500
2,500
Real per capita monthly income
Real per capita monthly income
3,000
Other urban
Rural
2,000
1,500
1,000
500
0
1
2
3
4
5
6
Decile
7
8
9
Metro Lima
Other urban
2,000
Rural
1,500
1,000
500
10
Source: Peruvian National Statistics Institute, based on the National Household Survey
0
1
2
3
4
5
Decile
6
7
8
9
10
Education of the poor has not improved, despite
improved growth
Education levels of the poor aged 15+ (%): 2001-2010
Primary
Source: INEI.
Secondary
Higher
Exports continue to be dominated by raw
materials…
Goods exports, 1961-2010
(Percentage of total exports at current prices)
1961-1970
1971-1980
1981-1990
1991-2000
2001-2010
Traditional
93.1
86.6
74.1
69.6
73.7
- Mining
43.2
48.0
45.4
46.0
56.3
Non-traditional
6.9
13.7
25.9
30.5
26.3
100.0
100.0
100.0
100.0
100.0
Total
Source: BCRP.
PERU: Global Competitiveness Index
(Percentile, Relative location)
Institutions 96/129
Infrastructure 88/139
Innovation 110/139
Note: The percentile (to the left of the country ranking) summarizes the relative position of the country, and corrects for the varying number of countries
in the ICG each year.
Source: Global Competitiveness Report (various edictions)
What about the situation within
the country?
Growth and Poverty Dynamics in Peru
Real expenditures per capita
Regions
Urban Coast
Rural Coast
Urban Highlands
Rural Highlands
Urban Jungle
Rural Jungle
Metropolitan Lima
Urban
Rural
Peru
Annual Growth
1985-1994
Annual Growth
1994-2007
1981
1993
2007
S/. 410.0
S/. 375.4
S/. 575.5
S/. 451.3
S/. 650.9
S/. 357.5
S/. 556.7
S/. 519.9
S/. 426.0
S/. 312.8
S/. 318.5
S/. 411.4
S/. 310.3
S/. 409.0
S/. 271.1
S/. 390.6
S/. 372.0
S/. 306.1
S/. 452.9
S/. 350.6
S/. 402.3
S/. 230.5
S/. 337.1
S/. 298.9
S/. 574.8
S/. 493.7
S/. 260.7
-3.0%
-1.8%
-3.7%
-4.1%
-5.0%
-3.0%
-3.9%
-3.7%
-3.6%
2.9%
0.7%
-0.2%
-2.3%
-1.5%
0.8%
3.0%
2.2%
-1.2%
S/. 481.6
S/. 348.5
S/. 429.0
-3.5%
1.6%
Poverty rates
Regions
Urban Coast
Rural Coast
Urban Highlands
Rural Highlands
Urban Jungle
Rural Jungle
Metropolitan Lima
Urban
Rural
Peru
Changes in Poverty Changes in Poverty
1985-1994
1994-2007
1981
1993
2007
35.4%
42.2%
25.0%
33.1%
19.1%
47.2%
21.9%
26.4%
36.5%
52.0%
53.8%
34.4%
51.6%
34.5%
60.1%
38.9%
41.8%
53.2%
22.3%
39.7%
34.7%
69.7%
44.2%
50.8%
11.9%
21.0%
61.9%
16.7%
11.6%
9.5%
18.5%
15.4%
12.9%
17.0%
15.4%
16.7%
-29.8%
-14.1%
0.2%
18.1%
9.7%
-9.3%
-27.0%
-20.8%
8.6%
30.5%
45.9%
32.4%
15.4%
-13.5%
Source: Escobal, J y Ponce, Carment (2011). Spatial Polarization of Wellbeing. in Peru, Third Global Conference on Economic Geography 2011,
Space, Economy and Environment, June 28 - July 2, 2011, COEX, Seoul, Korea
Inequality Dynamics
Inequality (Gini)
Regiones
Urban Coast
Rural Coast
Urban Highlands
Rural Highlands
Urban Jungle
Rural Jungle
Metropolitan Lima
Urban
Rural
Peru
1981
1993
2007
0.37
0.39
0.42
0.40
0.40
0.41
0.39
0.40
0.40
0.40
0.37
0.38
0.36
0.36
0.36
0.34
0.37
0.37
0.37
0.37
0.32
0.32
0.32
0.36
0.32
0.34
0.34
0.35
0.33
0.37
Source: Escobal, J y Ponce, Carment (2011). Spatial Polarization of Wellbeing. in Peru, Third Global Conference on
Economic Geography 2011, Space, Economy and Environment, June 28 - July 2, 2011, COEX, Seoul, Korea
What happened to poverty?
Mapping growth and poverty dynamics Growth 1993-2007
Changes in Poverty 1993-2007
Source: Escobal, J y Ponce, Carment (2011). Spatial Polarization of Wellbeing. in Peru, Third Global Conference on
Economic Geography 2011, Space, Economy and Environment, June 28 - July 2, 2011, COEX, Seoul, Korea
Growth and poverty dynamics: disaggregating
beyond the regional level
Growth and Poverty Change 1993-2007
-60
Poverty Change (% points)
-40
-20
0
20
40
(at the provincial level)
-5.0
-3.0
-1.0
1.0
Per Capita Growth (per year)
3.0
5.0
Note: Peru is composed of 25 regions (Departmentos) 195 Provinces and 1800 districts
Source: Escobal, J y Ponce, Carment (2011). Spatial Polarization of Wellbeing. in Peru, Third Global Conference on
Economic Geography 2011, Space, Economy and Environment, June 28 - July 2, 2011, COEX, Seoul, Korea
Sharp inequality levels within the country
Poverty Incidence, 2010
And huge pockets of
poverty persist in
departments with high
levels of development
Cusco: 631,000 people in poverty
Cajamarca :737,000 people in poverty
Lambayeque: 427,000 people in poverty
Lima y Callao: 1,400,000 people in poverty
The situation must change
• The Highlands region has the highest level of
poverty and extreme poverty
• The government can continue with its current
policy of transfers and social safety nets.
• Or the government can prepare and execute
a development strategy that makes a
coordinated attack on the structural causes of
poverty in these areas.
A methodology for prioritizing
investment that captures
heterogeneity
The concept of (stochastic) profit frontier
• This approach is based on a simple economic concept: the Production Possibility Frontier (PPF).
• All the possible production combinations are found within the PPF.
• Outside of the boundary are combinations which are not achieveable under current conditions
• The efficient use of resources is along the boundary.
Milk
production
Production
Possibility
Frontier
C
Corn
production
Accessibility
Altitude
Water bodies
Roads
Land use
Advantages of Micro-Region Typology
Productive projects differentiated to
meet local needs and problems
Conditional Cash Transfers and
Nutritional Programs
The inclusion of
socioeconomic
characteristics and access in
the analysis allows for the
identification of bottlenecks in
areas of high potential but
low or medium efficiency
What are the principal differences
between high and low efficiency
households in the area?
Typology
Diagnostic
from
Poverty
map
High potential and low
average efficiency
High poverty areas
Low potential and low
average efficiency
Productive and Efficiency
potential based on market,
socioeconomic, bio-physical
and access characteristics.
High poverty areas
47
Advantages of a micro‐region
typology: classification
Micro-Regions
Critical, lacking agricultural potential
Medium priority, no agricultural opportunities
Low priority
High priority
Poverty
High
Medium
Low
High
Potential
Low
Low
Low
Medium-High
Efficiency
High-Medium-Low
High-Medium-Low
High-Medium-Low
High-Medium-Low
Medium priority, with agricultural opportunities
Medium
Medium-High
Medium-Low
Low
Low
Medium-High
Medium-High
Medium-low
High
Low priority, with agricultural opportunities
High performance
Estimation Methodology
INSUMOS PARA LA ESTIMACION
Estimation inputs
Step 1:PASO 1: ESTIMACION Estimation
(NIVEL DEL (Household
Level) HOGAR) Potential
Potencial: Precios de Prices
of products (P) and
productos (P) y insumos (W), inputs
(W),
profits reported by
beneficios reportados por el household (π).
hogar (π). Efficiency Land, value of
Eficienciasocioeconomic
: tierra, valor de activities,
los activos, características characteristics
(Z),
biophysical
conditions (G),
socioeconómicas (Z), market
access (A).
condiciones biofísicas (G), ESTIMACION Estimation
Econometría Econometric
Model
Modelo de of the
fronteras stochastic
estocásticas de Profit frontier
beneficios
OUTPUT DE LA ESTIMACIÓN Estimation output
Pesos asignados a los Weights assigned to
insumos de acuerdo a inputs following
teoría económica y economic theory and
empirical
evidence
evidencia empírica
PASO 2: Step 2:
PREDICCIÓN Prediction
(NIVEL (Regional
REGIONAL)
Level)
acceso a mercado (A).
RESULTADO FINAL Final result
RESULTADO DE LA PREDICCION Prediction result
Potencial productive a Potencial Region level
productive y productive
potential
and Pesos
eficiencia a efficiency
nivel regional Productive potential at the
nivel regional; eficiencia regional
level;
de acuerdo a las Efficiency according to
características socioeconomic
characteristics,
biophysical
socioeconómicas, conditions, market access
condiciones biofísicas, within the area
acceso a mercado dentro del área INSUMOS PARA LA PREDICCION
Prediction
inputs
Resultado de la estimación Estimation results (weights)
(pesos) Boundary
Product prices (P) and
Frontera: Precios de productos inputs (W)
(P) y insumos (W). Efficiency: Land, value of
Eficiencia: tierra, valor de activities, socioeconomic
characteristics
(Z), biophysical
activos características conditions (G), market access
socioeconomicas (Z), (A).
condiciones biofísicas (G), acceso a mercado (A). Estimated
Cost of
Access to
Markets
(soles per kg)
Agricultural
Profit
Frontier
Efficiency in
agricultural
profits
Poverty
Map
Constructing the
typology: a
combination of
potential,
efficiency, and
poverty
Recap (1)…
Targeting Criteria
based on Efficiency
Data
Estimated
cost of
Market
Access
Geo Layers
PPF:
Agricultural
Profit Frontier
Input, Output, Profits
Group 1
Available datasets:
Group 1
Group 2
.7
.1
.8
Density
.2
Cumulative Density
.9
.3
Group 2
0
Land characteristics,
biophysical conditions,
socioeconomic
characteristics, assets,
market access, etc.
1
Variable Z
.4
Variable X
4
6
8
values X
10
12
0
2
4
6
Values Z
8
10
Efficiency in
Agricultural
Profits
Recap (2)…
Efficiency Allocation
Criteria
MULTIPLE TARGETING
DIMENSIONS
Equity Allocation
Criterion
Typology
combines all
these criteria
Recap (3)…
Recall the initial objective….
Micro-Regions
Low potential and low
average efficiency
Critical, lacking agricultural potential
Medium priority, no agricultural opportunities
Low priority
High priority
High potential and low
average efficiency
Medium priority, with agricultural opportunities
Low priority, with agricultural opportunities
High performance
57
Recap (4): Grouping diverse criteria into seven microregions…
Recap (5)… How does this translate into policies?
High potential and low
average efficiency
What are the principal differences
between high and low efficiency
households in the area?
Productive projects differentiated to
meet local needs and problems
Recap (6)… How does this translate into policies?
Low potential and low
average efficiency
Conditional Cash Transfers and
Nutritional Programs
Critical areas:
• Areas with high poverty,
but low potential for
agricultural development
due to scarce potential
& efficiency
• Most of these zones are
found in the highest
parts of the Highlands.
• Policies in the area
should aim to provide
short-term assistance
High priority areas:
• Areas with high poverty
and low efficiency, but
whose agricultural
potential (high or medium)
has prospects for
economic development
• Examples of these areas
are the Highland areas of
Piura, Ancash, Huánuco,
Huancavelica and
Ayacucho
• Policies in the area should
identify and tackle
bottlenecks which hamper
improved use of local
resources
High performance
areas:
• Areas with low
poverty, high
potential and
efficiency
• Owing to their
good performance,
the experiences of
these regions
should be studied
to assess their
replicability.
High
Performance
Areas (Enlarged):
• This close up
allows us to see
examples of
such areas: the
valleys of the
Chillón (Canta)
and Mantaro
(Junín) rivers.
Low priority areas
with agricultural
potential:
• Micro-regions with low
levels of poverty, high
potential, and low or
medium agricultural
efficiency.
• Despite significant
untapped agricultural
potential, low poverty
rates indicate that
there are other
activities in these
areas that can be
competitive.
Low priority areas with
agricultural potential
(Enlarged):
• Areas such as Santa
Eulalia, Matucana,
Lunahuana, Yauyos,
and Sayán in Lima are
examples of these
zones.
• These areas are also
characterized by their
high accessibility (along
the roads linking the
coastal region with the
highlands)
Using the typology
to identify bottlenecks
• Areas of high potential and efficiency should be studied to
find the key factors behind their better performance.
• In areas of low efficiency and potential, bottlenecks should be
identified:
– Reductions of transaction costs
– Access to optimal productive technologies through access to
human capital and relevant technical assistance
– Strategies to diversify income
– Investments in infrastructure (accounting for their
complementarities)
Using the typology
to identify bottlenecks
• In low potential areas the bottlenecks that prevent expansion
to the productive frontier should be identified:
– The introduction of new technologies to the area
– Problems in input and goods markets
– Access to more dynamic urban or international markets
– Land management and soil quality features
– Natural risks (e.g. weather variability and strategies to
mitigate risk such as insurance).
Using the typology
to identify bottlenecks
• In critical areas short term assistance should be
provided
– Social networks
– Conditional transfer programs
– School nutrition programs
Can be applied to other settings?
Guatemala
Cost of Market
Access
Efficiency in
Agricultural Profits
Agricultural Profit
Frontier
Poverty Map
Guatemala:
Seven-Class Typology
With
agricultural
potential
Without
agricultural
potential
Conclusions
• Inclusive growth is a necessary and imperative goal for
Peru and in LAC
• Agriculture and agribusinesses have to play a central
role to achieve this
• We are currently in a worldwide recession, so we must
be very careful that we do not create distortions. These
distortions might thwart the sectors that are promoting
growth despite the global crisis.
• Public policy must be differentiated and applied
efficiently through a geographic targeting
• Public investments must be prioritized to increase
inclusion and capture existing heterogeneity
• Do not miss opportunities for synergies between different
policies