Transcript Document

Poverty Analysis Macroeconomic
Simulator (PAMS) and PSIA with an
application to Burkina Faso
Jan Walliser
Senior Economist
The World Bank
Outline of the Presentation
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Introduction
PAMS: Inputs and Outputs
A brief tour of PAMS
A Set of Policy Experiments
Introduction: why “macro”PSIA?
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Changes in the macro framework such
as the fiscal, inflation and exchange rate
targets? How do they affect the poor?
Exogenous shocks such as trade shocks,
capital flows volatility, changes in foreign
aid and foreign payment crises? How can
policy mitigate these effects on the poor?
Introduction: why “macro” PSIA?
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Improving public expenditure
targeting? How can public
expenditure be better targeted?
Structural reforms such as trade
policy, privatization, agricultural
liberalization? How are the poor
affected?
Modeling Implications and Challenges
 Maintain simplicity of macroeconomic
consistency frameworks (e.g., RMSMXs or other country-based models)
 Link macro-consistency frameworks
directly with household survey data
The Logic of PAMS
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Three Recursive Layers Consistent with
Incidence Approach
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Macro-framework: GDP, national accounts,
taxes & government spending, BOP, prices
Labor model breaking down population by
skill level and economic sectors using
categories from HHS
Model to simulate income changes by
group, allowing calculation of poverty
incidence and inter-group inequality
Top-down HHL "micro-simulation" approach
General Structure : 3 Layers
Layer 1: Macro
Macroeconomic Model
Macro Accounting (RMSM-X), CGE (123), Econometric
Sectoral Disaggregation, Factor Markets  Linkage Aggregate Var
For k representative groups of households
Layer 2: Meso
yk , Lk , wk , Pk
Household Survey (HHS), i individual households, Macro "consistent" changes
in real household incomes and change in the distribution of welfare
yi  wi Li  Ei  R(wi Li  Ei ; Ai )/ P(Ci ; p)
Layer 3: Micro
yi  yi , 1  yi , Li  f (Y ), wi  g (Y , A)
(yi) with poverty line, z,  indicator of poverty Pi for each household i
and indicators of within-group inequality (e.g., Gini, etc.)
Limitations
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Not all policy challenges covered
PAMS best suited to simulate poverty and
distributional implications of:
 PRSP-PRGF macro baseline scenarios
 Sensitivity analysis along the base case
 Sectoral growth scenarios
 Average tax burden (standard incidence
analysis)
 Average social transfer
PAMS: Inputs and Outputs
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Micro input
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Macro input
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Micro-Macro Linkage
PAMS: Micro Input
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Household Survey Data
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Expenditure or income
Size of household
Household weight in population
Data arranged by socioeconomic groups
of representative households
PAMS: Macro Input
Macro framework from any macro
consistent model (IMF macro
projections, World Bank’s RMSM-X
model, other domestic macro
models)
Aggregate variables (GDP, BOP,
fiscal accounts, monetary
accounts, inflation)
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PAMS: Micro-Macro
Linkages
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Labor market module breaks down the
economy into sectors: rural/urban,
formal/informal, tradable/non-tradable
Labor supply is driven by exogenous factors
Labor demand is demand is broken down by
sector, skill level and location and depends
on sector demand and real wages
Labor model produces wage income by
representative households of SEG and
location based on income aggregates,
group-specific tax and transfer variables
PAMS: Micro-Macro
Dynamics
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Base year as starting point
Simulation of macro variables/population
Simulation labor demand and supply,
wages and incomes by groups
Simulation of changes in HH-level income
data to calculate poverty indicators
assuming unchanged intra-group
distribution of incomes
PAMS: Outputs
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1. Standard macroeconomic
Indicators
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2. Standard poverty and inequality
indicators (P0, P1, P2, Gini, etc.)
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3. Poverty decompositions:
Growth, inequality and population
effects with respect to P1 and P2
PAMS: Outputs
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4. Pro-poor growth indicators
 Pro-poor growth index (Kakwani and
Pernia, 2000)
 Growth Incidence Curve (Ravallion and
Chen, 2003)
 Poverty Equivalent Growth Rate
(Kakwani and Son, 2003)
PAMS
Macro-Framework
PAMS
House H.
Survey
DEBT
Results
RMSM-X
MEMAU
Int.
PAMS
Meso
Assum
Micro
Simulation with PAMS
Update
Macro
Update Earning
& Trans. Module
Pov. & Ineq
Simul. Scen.
Household survey
Pov. & Ineq
Baseline Scen.
Iteration
Process
Country Applications
PAMS: Implementation Process
Identification
Country Specific Application
1/
Operationalization 2/
Country
Calibration6/
National
Team
Training 3/
Official
Delivery
Follow up
Activities
5
15
3*5=15
N.D
N.D
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Region
contact
Definition
TOR
Processing
HHS
Set
Interface
Pov. M.
N.D 5/
N.D
15
5
5
Burkina Faso
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X
X
X
Mauritania
X
X
X
Cameroon
X
Djibouti
X
Ethiopia
X
Albania
X
X
X
Indonesia
X
X
X
Rwanda
Mali
Benin
Guinea
X
X
X
X
X
X
Timing (days)4/
Dissemination
and Validation
Set
Update the
Interface
Program
Macro M.
X
X
1 The PAMS development phase started in October 2001 and its first application to a pilot country was October 2002.
2
At this stage, the National Team becomes part of PAMS Community of Practice (the network puts together countries applying PAMS as well as the WB Region).
3
A course delivered in 3 Modules. The first is the "Introduction to PAMS", the second is "Modeling with PAMS". The training is delivered by means of Face-to-Face and Videoconferenc
the third is the "Advanced Training on PAMS and delivery."
4
This is a rough estimate of the average number of days required to complete the task.
5
Not defined.
6
Includes data consistency check as well.
PAMS: Burkina Faso
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1994, 1998, 2003 HHS
Longstanding macroeconomic
Program with IMF
HIPC CP in 2000 (original) and
enhanced (2002, with topping up)
Growth rates averaging 5 percent
Largely rural population
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PAMS: Burkina Faso
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Poverty rates (1998) of 45 percent
based on national poverty line
(which is below $1/day)
Cotton as major cash crop – 50-60
percent of exports, and significant
growth of cotton production
Cereal production stabilized due to
promotion of small-scale irrigation
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PAMS development
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Work started before 2003 HHS in context of
PRSP
Interest in having better handle on poverty
projections using macro-growth projections
Home-grown excel-based macro-model (IAP)
with technical assistance of GTZ
Collaboration on PAMS based on 2003 HHS
PAMS model linked to IAP output tables
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PAMS development
 PAMS model linked to IAP output tables
with support from local GTZ adviser and
team
 Close collaboration with macro
forecasting division in Ministry of
Economy and Development
 (Political) challenge: integration of 2003
HHS because of weaknesses in data
analysis
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SEGs and Poverty, 1998-2003
Share of Population
Share of Poor
Poverty Headcount
1998
2003
1998
2003
1998
2003
Rural area.
Urban area
86.3
13.7
79.5
20.5
94.1
5.4
91.0
9.0
62.2
21.1
52.7
20.9
Public sector (Urban)
Agricultural tradable (Rural)
Other agricultural non-tradable (Rural)
Family helpers and others (Rural)
Non labor force (Rural)
Private formal tradable (Urban)
Private formal non-tradable (Urban)
Informal (Urban)
Unemployed (Urban)
4.1
16.8
65.3
0.6
3.6
1.0
1.9
5.6
1.1
3.6
18.3
59.6
0.7
1.0
0.8
2.6
7.4
6.0
0.7
16.4
74.6
0.3
3.3
0.1
1.2
2.3
1.0
0.3
18.6
71.0
0.6
0.8
0.1
0.9
3.4
4.3
9.1
53.1
61.8
30.3
50.4
8.1
33.3
22.8
47.8
3.4
47.1
55.3
42.0
38.8
7.7
15.7
21.5
33.1
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Macro baseline scenario
Selected macro
indicators
Real GDP growth 1/
Primary sector 1/
Secondary sector 1/
Tertiary sector 1/
Fiscal revenue 2/
Public expenditure 2/
Exports of goods 1/
CPI (percentage change)
2003
Act.
2004
Proj.
2005
Proj.
2006
Proj.
2007
Proj.
8.0
10.8
10.4
5.5
11.3
22.0
10.7
2.1
4.8
1.8
6.3
6.1
12.0
22.5
15.8
2.2
5.3
4.5
6.7
5.3
12.5
22.7
16.4
2.2
5.2
4.5
6.6
6.8
13.0
22.9
8.5
2.0
5.2
4.5
6.6
6.8
13.5
23.6
6.3
2.0
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Poverty baseline scenario
Poverty Incidence
National
Rural
Urban
Demographic structure
Annual growth rate 3/
National
Rural
Urban
Share of population
Rural
Urban
2003
Act.
2004
Proj.
2005
Proj.
2006
Proj.
2007
Proj.
46.4
53.1
20.5
44.1
51.4
19.7
42.4
49.6
17.9
40.3
47.6
16.0
39.2
46.6
15.4
5.1
4.6
7.8
2.4
1.9
4.3
2.4
1.9
4.3
2.4
1.9
4.2
2.4
1.9
4.2
79.5
20.5
79.1
20.9
78.8
21.2
78.4
21.6
78.0
22.0
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Inequality-growth tradeoff
2003
2004
2005
2006
2007
Poverty Gap
Growth elasticity
Inequality elasticity
Inequality/Growth Tradeoff
-2.0
2.8
1.4
-2.1
3.1
1.5
-2.1
3.3
1.6
-2.2
3.6
1.6
-2.2
3.8
1.7
Square Poverty Gap
Growth elasticity
Inequality elasticity
Inequality/Growth Tradeoff
-2.5
4.7
1.9
-2.4
5.0
2.1
-2.4
5.3
2.2
-2.4
5.6
2.3
-2.3
5.8
2.5
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20-percent decline in cotton
prices
2. 0%
1. 5%
1. 0%
0. 5%
0. 0%
-0. 5%
-1. 0%
-1. 5%
-2. 0%
-2. 5%
2003
2004
2005
Gini-Total
2006
2007
P0
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20 percent decline in cotton
volume and cotton price
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
-3.0%
-4.0%
-5.0%
-6.0%
2003
2004
2005
Gini-Total
2006
2007
P0
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Increased primary sector
contribution to growth
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
-3.0%
-4.0%
-5.0%
2003
2004
2005
2006
2007
2008
2009
Gini-Total
2010
2011
2012
2013
2014
P0
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Lessons learned
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Strong payoffs of building a close early
collaboration with the government forecasting
team
Close collaboration with the local GTZ
technical assistance crucial
Close involvement of World Bank country
office staff essential
Need to make greater allowance for the
collection and analysis of poverty data when
embarking on PAMS modeling
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