Economy-Wide Simulations of MDG Strategies: Approach and

Lessons from EthiopiaHans Lofgren

Carolina Diaz-BonillaDECPG

World Bank

Presentation prepared for AFRCE and AFR PREM Breakfast Seminar, World Bank, October 18, 2005

1. Introduction

• For SSA, achieving the MDGs is a difficult challenge. • Key policy questions for MDG strategies:

– What is the required expansion of public spending (level, composition)?

– What are the effects on the labor market, foreign trade and exchange rates? (absorptive capacity?)

– What are the roles of synergies between different MDGs? – How does growth in private incomes interact with public

spending?– How do the effects depend on the mix between domestic and

foreign financing?– What is the impact of back- and front-loading public service

expansion?– If all MDGs cannot be met, what are the trade-offs between

human development (HD) and infrastructure?

1. Introduction

• In many SSA countries, the pursuit of MDGs major economic shock (macro, sectors, labor market, foreign aid).

• Our approach: MAMS (Maquette for MDG Simulations) – an extended, dynamic-recursive computable general equilibrium (CGE) model designed for MDG analysis

• Pilot study of Ethiopia.

1. Introduction

• Rationale for approach:– Partial equilibrium analysis is insufficient –

needs to be complemented by an economywide perspective

– Agents do not act with perfect foresight.– Extensions required given that standard

CGE models do not capture the output side of government spending.

1. Introduction

• Contents of presentation:2. Model structure3. Data and resource requirements4. Illustrative findings for Ethiopia5. Lessons for other SSA countries6. Next steps

2. Model structure• Ancestry: IFPRI standard model (Lofgren, Harris

and Robinson); dynamic-recursive version.• Most features are familiar from other open-

economy, dynamic-recursive CGE models:– Optimizing producers and consumers.– Supply-demand balance in factor and commodity

markets (with flexible prices clearing most markets)– Expenditures = receipts for the three macro balances:

government, savings-investment, rest of world– Imperfect transformation/substitutability in trade.– Updating of factor and population stocks and TFP;

endogenous/exogenous mix.

2. Model structure• MAMS treatment of government:

– It purchases government services, disaggregated by function. – Government services produced using labor, intermediate inputs,

and capital.– Government services enter MDG/HD functions and influence

factor productivity. – Education influences size and composition of labor force.– Additional spending items: interest payments, domestic

transfers.– Alternative sources of government receipts: taxes, domestic

borrowing, foreign borrowing, and foreign grants.

MDG/HD module• Functional forms across MDGs:

– Top: MDG(-related) indicator =logistic(intermediate variable)

– Bottom: intermediate variable =CE (gov services, other arguments)

[CE = constant-elasticity]• Logistic function

– calibrated to replicate base values under base conditions and to achieve MDG under conditions identified by sector studies.

– diminishing marginal returns to increases in intermediate variable (and its determinants)

Table. Determinants of MDG achievements

MDGServiceDelivery

Per-cap cons’on

Wageincentives

Public infra- structure

Other MDGs

1 X

2 X X X X 4

4 X X X 7a,7b

5 X X X 7a,7b

7a X X X

7b X X X

Education• Disaggregated by cycle. • Model tracks evolution of enrollment in each

cycle = old students that continue/repeat + entering graduates from earlier cycle + new entrants to school system.

• Endogenous student behavior: shares of relevant totals that graduate, continue, repeat, drop out; selected shares sum to unity.

• Within each cycle and between cycles, student behavior determined by the above logistic-CE structure (for arguments, see MDG2 in Table)

Labor

• In each year, labor by level of educational achievement defined as the sum of:– Remaining stocks from last year– New entrants among graduates and dropouts– Net entrants from outside the school system

Poverty and Inequality

• Alternative approaches to poverty and inequality analysis: – aggregate poverty elasticity– representative household– microsimulation (integrated, top-down)

Other stocks and productivity• Updating of (non-labor) factor stocks:

– private and government capital– non-capital factors with exogenous growth

• Updating of debt stocks:– foreign (incl. possible debt relief)– domestic government

• TFP (by production activity) as a function of– changes in public infrastructure services– changes in openness (trade share in GDP)– exogenous trend

Typical Ethiopia macro closures• Government balance:

– Cleared by foreign grants– Given values: domestic and foreign borrowing, tax rates,

government demand (consumption and investment), interest• Private savings-investment balance:

– Cleared by private investment– Given values for funding sources: sum of private savings (net of

lending to government), FDI, and private foreign borrowing (both exogenous)

• RoW balance: – Cleared by real exchange rate (influencing exports and imports)– Given values: foreign borrowing, FDI, migrant remittances, and

foreign grants

3. Data and resource requirements

• Data Requirements– Social Accounting Matrix– Core Data– Non-Core Data– Technical Data

• Resource Requirements

Social Accounting Matrix (SAM)

• Country database– Required for every country application

• How to begin?– Determine Base year

• By data availability and context• As recent as possible, but avoid unstable year

(drought, war, etc)– Create Macro SAM– Disaggregate into Micro SAM

• Various sources and levels of disaggregation

Social Accounting MatrixMacro SAM

• Construct up-to-date Macro SAM:– National Accounts Data– Balance of Payments– Government Budget

• May include the following accounts:– activity, commodity, factor, household, government,

rest-of-world, different tax accounts, domestic interest payments, foreign interest payments, private investment, public investment.

• The RMSM-X model includes the data required.

Social Accounting MatrixMicro SAM

• Disaggregation of the government and MDG-related accounts:– Education – Health – Water & Sanitation – Public Infrastructure – Other Government Services.

• Need information on: – Recurrent Costs (intermediates and factor payments) – Investment Costs

Micro SAM (cont)

• Activities-Commodities: MDG/government– Disaggregation should permit a distinction between the

major determinants of the achievement of each MDG• For MDGs covered by the analysis• In effect in the areas of education, health, and water-sanitation

– Educational coverage has to be comprehensive• Must cover the targeted primary school cycle and some

aggregation of other schooling.

– The schooling system is linked to the labor market: more disaggregation permits more detailed links between the labor market and educational MDGs

Micro SAM (cont)• Disaggregation of non-government/non-MDG-

related accounts – Ex: activities/commodities, factors, households– Not required – But disaggregation would enrich the analysis

• Analyze the MDGs in the context of a broader set of issues (e.g. trade)

• With deeper insights about poverty (MDG1)– Only feasible if:

• Relatively recent SAM exists, OR• Major data collection effort is to be undertaken

• SAM-balancing program may be used to reconcile data from different sources and years.

Micro SAM (cont)• Factors

– Disaggregation is flexible– Require at least one non-government/MDG capital type.

• National accounts do not typically assign value-added to government capital

– Labor disaggregated by the highest cycle achieved.

• Institutions and related accounts (taxes, interest payments)– Model assumes single accounts for RoW and

government– Households: one or more.– One or more enterprises may or may not be singled out.

Micro SAM (cont)• Required accounts for savings-investment

– savings and capital (investment-financing) accounts by institution

– one account per investment type (private and different public)

• If private sector is involved in MDG-related health and education services, private activities/commodities and related capital returns and investment financing could be singled out.

Core Data

• Essential for every country application• Covers the MDGs, education, population,

labor, and selected government data (complementing the SAM).

• Education data disaggregated by cycle. – Must distinguish the primary cycle

corresponding to MDG 2.

Core Data (cont)

• Includes more detailed data related to different MDGs and the labor market; ex:– Levels of service delivery to meet MDGs– Initial stocks of students by cycle– Initial stocks of labor by educational level– Student behavioral patterns (ex: graduation

rates)– Labor use by activity (private and public)

Non-Core Data

• Country-specific data collection effort is not essential.

• Primarily future projections and some detailed data that are based on assessments.

• Example:– Projected annual growth in macro aggregates, labor

stocks (by type and household).– Assumed paths for exogenous parameters

(borrowing, transfers)

Technical Data

• Elasticities in production, trade, consumption, and in the different MDG functions.– Difficult to find country-specific estimates.

• Disaggregation depends on the SAM disaggregation– Country-specific data collection effort is not essential.

• Essential to identify technical studies that have generated or used this type of data (e.g. various other models of the economy and/or MDG sectors).

Data Sources• Public Expenditure Review (PER)• Country Economic Memoranda (CEM)• Development Policy Review (DPR)• World Development Indicators (WDI)

– Labor stocks; Value-added in Ag/Ind/Srv; Pop• Country-specific research papers• Specialized sector studies

– Health, education, water-sanitation, public infrastructure

– Often undertaken in context of MDG studies.

Other Resource Requirements• People

– Person knowledgeable about GAMS and CGE models.

– Sectoral experts.• Time

– 3 months for a country case study (?) (benefiting from initial investment in Ethiopia model)

4. Illustrative findings for Ethiopia

• Evolution over Time:• Net Primary School Completion Rate

(MDG 2; %)• Wages of Workers with Secondary-School

Education (ET Birr)• Foreign Aid Per Capita (US$)

• Trade-Offs between Human Development (HD) and Poverty

Evolution over Time for MDG 2Net Primary School Completion Rate (%)

(By Simulation)

0

20

40

60

80

100

120

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

basemdg-base mdg-infcutmdg-hdcut

Note: 2015 target for MDG 2 = 100%

Evolution over Time for WagesWorkers with Secondary-School Education

(By Simulation)

Note: Wages are shown in Ethiopian Birr

1800

1900200021002200

2300

240025002600

27002800

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

basemdg-base mdg-infcutmdg-hdcut

Foreign Aid Per Capita (US$)By Simulation

0

10

20

30

40

50

60

70

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

basemdg-base mdg-infcutmdg-hdcut

Trade-offs between Human Development (HD) and Poverty

80

85

90

95

100

70 80 90 100

100%

90%

80%

75%

PV of Aid:

Shar

e of

HD

Tar

get (

%)

Share of Poverty Target (%)

5. Lessons for other SSA countries

• Initial investment has been made new applications need less resources.

• Model disaggregation and related data needs are flexible and country-specific.

• At least one team member must have expertise in GAMS-based CGE modeling

5. Lessons for other SSA countries

• MAMS is a research tool, requiring development of a sector/micro-based database and a process of model fine-tuning/validation.

• Government may prefer to use simpler macro frameworks for their strategy documents (political spending figures; implicit/different micro foundations)

• MAMS findings provide background and can be used for Bank dialogue on the design of strategies for MDGs and poverty reduction.

6. Next Steps

• Applications to additional countries under way (especially SSA and Latin America)

• Streamlining of modeling framework• Training• Further development of documentation• User-friendly interface.

References

• Lofgren, Hans, Rebecca Lee Harris, and Sherman Robinson, with assistance from Moataz El-Said and Marcelle Thomas. 2002. A Standard Computable General Equilibrium (CGE) Model in GAMS. Microcomputers in Policy Research, Vol. 5. Washington, D.C.: IFPRI (www. ifpri.org/pubs/microcom/micro5.htm)

• Lofgren, Hans. 2004. MAMS: An Economywide Model for Analysis of MDG Country Strategies. Mimeo. November. Washington, D.C.: World Bank.

• Lofgren, Hans and Carolina Diaz-Bonilla. 2005. “Economywide Simulations of Ethiopian MDG Strategies,” DECPG, World Bank, July.

References• Robinson, Sherman and Hans Lofgren. 2005. “Macro

Models and Poverty Analysis: Theoretical Tensions and Empirical Practice,” Development Policy Review, Volume 23, Number 3, May.

• Sundberg, Mark, and Hans Lofgren. 2005. Absorptive Capacity and Achieving the MDGs: The Case of Ethiopia. World Bank. Mimeo.