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![Page 1: Jan Walliser Senior Economist The World Bank Poverty Analysis Macroeconomic Simulator (PAMS) and PSIA with an application to Burkina Faso.](https://reader030.fdocuments.net/reader030/viewer/2022032708/56649e585503460f94b51c8a/html5/thumbnails/1.jpg)
Jan WalliserSenior EconomistThe World Bank
Poverty Analysis Macroeconomic Simulator (PAMS) and PSIA with an application to Burkina Faso
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Outline of the Presentation Introduction PAMS: Inputs and Outputs A brief tour of PAMS A Set of Policy Experiments
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Introduction: why “macro”PSIA? 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?
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Introduction: why “macro” PSIA?
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?
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Modeling Implications and Challenges
Maintain simplicity of macroeconomic consistency frameworks (e.g., RMSM-Xs or other country-based models)
Link macro-consistency frameworks directly with household survey data
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The Logic of PAMS Three Recursive Layers Consistent
with Incidence Approach 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
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Household Survey (HHS), i individual households, Macro "consistent" changes in real household incomes and change in the distribution of welfare
(yi) with poverty line, z, indicator of poverty Pi for each household i and indicators of within-group inequality (e.g., Gini, etc.)
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Sectoral Disaggregation, Factor Markets Linkage Aggregate VarFor k representative groups of households
kkkk PwLy ,,,
Macroeconomic ModelMacro Accounting (RMSM-X), CGE (123), Econometric
Top-down HHL "micro-simulation" approach General Structure : 3 Layers
Layer 1: Macro
Layer 2: Meso
Layer 3: Micro
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Limitations 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
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PAMS: Inputs and Outputs
Micro input
Macro input
Micro-Macro Linkage
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PAMS: Micro Input
Household Survey Data Expenditure or income Size of household Household weight in population
Data arranged by socioeconomic groups of representative households
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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
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
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PAMS: Micro-Macro Dynamics
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
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PAMS: Outputs 1. Standard macroeconomic
Indicators
2. Standard poverty and inequality indicators (P0, P1, P2, Gini, etc.)
3. Poverty decompositions: Growth, inequality and population effects with respect to P1 and P2
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PAMS: Outputs 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)
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PAMS
PAMSMacro-FrameworkHouse H.
Survey
RMSM-X
MEMAU
DEBTResults
Assum
Int. PAMS Meso Micro
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Simulation with PAMS
Update
Macro
Update
Macro
Household survey Household survey
Update Earning
& Trans. Module
Update Earning
& Trans. Module
Pov. & Ineq
Baseline Scen.
Pov. & Ineq
Baseline Scen.
Pov. & Ineq
Simul. Scen.
Pov. & Ineq
Simul. Scen.
Iteration
Process
Iteration
Process
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Country Applications
Region contact
Definition TOR
Processing HHS
Set Interface Pov. M.
Set Interface Macro M.
Update the Program
Calibration6/
National Team
Training 3/
Official Delivery
Follow up Activities
Timing (days)4/ N.D 5/ N.D 15 5 5 5 15 3*5=15 N.D N.D
Burkina Faso X X X X X X X X X X
Mauritania X X X X X X X X X X
Cameroon X
Djibouti X
Ethiopia X
Albania X X X
Indonesia X X X
Rwanda X X X X X X X X
Mali XBenin X XGuinea 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 Videoconferences.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.
Dissemination and Validation
PAMS: Implementation Process 1/
Country
Country Specific ApplicationIdentification Operationalization 2/
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PAMS: Burkina Faso
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
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 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. 86.3 79.5 94.1 91.0 62.2 52.7
Urban area 13.7 20.5 5.4 9.0 21.1 20.9
Public sector (Urban) 4.1 3.6 0.7 0.3 9.1 3.4
Agricultural tradable (Rural) 16.8 18.3 16.4 18.6 53.1 47.1
Other agricultural non-tradable (Rural) 65.3 59.6 74.6 71.0 61.8 55.3
Family helpers and others (Rural) 0.6 0.7 0.3 0.6 30.3 42.0
Non labor force (Rural) 3.6 1.0 3.3 0.8 50.4 38.8
Private formal tradable (Urban) 1.0 0.8 0.1 0.1 8.1 7.7
Private formal non-tradable (Urban) 1.9 2.6 1.2 0.9 33.3 15.7
Informal (Urban) 5.6 7.4 2.3 3.4 22.8 21.5
Unemployed (Urban) 1.1 6.0 1.0 4.3 47.8 33.1
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Macro baseline scenario
2003 Act.
2004 Proj.
2005 Proj.
2006 Proj.
2007 Proj.
Selected macro indicators
Real GDP growth 1/ 8.0 4.8 5.3 5.2 5.2 Primary sector 1/ 10.8 1.8 4.5 4.5 4.5 Secondary sector 1/ 10.4 6.3 6.7 6.6 6.6 Tertiary sector 1/ 5.5 6.1 5.3 6.8 6.8 Fiscal revenue 2/ 11.3 12.0 12.5 13.0 13.5 Public expenditure 2/ 22.0 22.5 22.7 22.9 23.6 Exports of goods 1/ 10.7 15.8 16.4 8.5 6.3 CPI (percentage change) 2.1 2.2 2.2 2.0 2.0
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Poverty baseline scenario
2003 Act.
2004 Proj.
2005 Proj.
2006 Proj.
2007 Proj.
Poverty Incidence National 46.4 44.1 42.4 40.3 39.2 Rural 53.1 51.4 49.6 47.6 46.6 Urban 20.5 19.7 17.9 16.0 15.4
Demographic structure Annual growth rate 3/ National 5.1 2.4 2.4 2.4 2.4 Rural 4.6 1.9 1.9 1.9 1.9 Urban 7.8 4.3 4.3 4.2 4.2
Share of population Rural 79.5 79.1 78.8 78.4 78.0 Urban 20.5 20.9 21.2 21.6 22.0
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Inequality-growth tradeoff
2003
2004
2005
2006
2007
Poverty Gap
Growth elasticity -2.0 -2.1 -2.1 -2.2 -2.2
Inequality elasticity 2.8 3.1 3.3 3.6 3.8
Inequality/Growth Tradeoff 1.4 1.5 1.6 1.6 1.7
Square Poverty Gap
Growth elasticity -2.5 -2.4 -2.4 -2.4 -2.3
Inequality elasticity 4.7 5.0 5.3 5.6 5.8
Inequality/Growth Tradeoff 1.9 2.1 2.2 2.3 2.5
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20-percent decline in cotton prices
-2.5%
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
2003 2004 2005 2006 2007
Gini-Total P0
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20 percent decline in cotton volume and cotton price
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
2003 2004 2005 2006 2007
Gini-Total 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 2010 2011 2012 2013 2014
Gini-Total P0
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Lessons learned 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