Economy-Wide Simulations of MDG Strategies: Approach and Lessons from Ethiopia Hans Lofgren Carolina Diaz-Bonilla DECPG 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 structure 3.Data and resource requirements 4.Illustrative findings for Ethiopia 5.Lessons for other SSA countries 6.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 MDG Service Delivery Per-cap cons’on Wage incentives Public infra- structure Other MDGs 1X 2XXXX4 4XXX7a,7b 5XXX 7aXXX 7bXXX

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 Matrix Macro 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 Matrix Micro 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 2 Net Primary School Completion Rate (%) (By Simulation) Note: 2015 target for MDG 2 = 100%

Evolution over Time for Wages Workers with Secondary-School Education (By Simulation) Note: Wages are shown in Ethiopian Birr

Foreign Aid Per Capita (US$) By Simulation

Trade-offs between Human Development (HD) and Poverty

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 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 MAMS: An Economywide Model for Analysis of MDG Country Strategies. Mimeo. November. Washington, D.C.: World Bank. Lofgren, Hans and Carolina Diaz-Bonilla “Economywide Simulations of Ethiopian MDG Strategies,” DECPG, World Bank, July.

References Robinson, Sherman and Hans Lofgren “Macro Models and Poverty Analysis: Theoretical Tensions and Empirical Practice,” Development Policy Review, Volume 23, Number 3, May. Sundberg, Mark, and Hans Lofgren Absorptive Capacity and Achieving the MDGs: The Case of Ethiopia. World Bank. Mimeo.