Jan WalliserSenior EconomistThe World Bank

Poverty Analysis Macroeconomic Simulator (PAMS) and PSIA with an application to Burkina Faso

Outline of the Presentation Introduction PAMS: Inputs and Outputs A brief tour of PAMS A Set of Policy Experiments

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?

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?

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

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

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.)

),(),(,

);(/);(

1, AYgwYfLyyy

pCPAELwRELwy

iiiii

iiiiiiiii

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

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

PAMS: Inputs and Outputs

Micro input

Macro input

Micro-Macro Linkage

PAMS: Micro Input

Household Survey Data 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)

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

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

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

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)

PAMS

PAMSMacro-FrameworkHouse H.

Survey

RMSM-X

MEMAU

DEBTResults

Assum

Int. PAMS Meso Micro

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

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/

19

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

20

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

21

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

22

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

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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