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Analyzing MDG Strategies. Hans Lofgren Carolina Diaz-Bonilla DECPG World Bank Presentation prepared for the Workshop “Experiences with EPIAM

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Analyzing mdg strategies

Analyzing MDG Strategies

Hans Lofgren

Carolina Diaz-Bonilla

DECPG

World Bank

Presentation prepared for the Workshop “Experiences with EPIAM

in Bangladesh, Cameroun, Ghana, the Philippines, and Nepal”, organized by the New Rules for Global Finance Coalition and held at IMF Headquarters, Washington, DC, March 15, 2006


1 introduction and background
1. Introduction and Background

  • At the UN Millennium Summit of 2000, the world’s leaders agreed on a set of goals and targets for 2015:

    • Halving poverty and hunger rates (relative to the 1990 rates)

    • Achieving universal primary education

    • Eliminating gender disparity in education

    • Reducing by two thirds the under-five child mortality rate (relative to the 1990 rate) ….


1 introduction and background1
1. Introduction and Background

….

  • Reducing by three quarters the maternal mortality rate (relative to the 1990 rates)

  • Reversing the spread of HIV/AIDS, malaria and other major diseases

  • Halving the population shares without sustainable access to safe water and improved sanitation.

  • Developing a global partnership for development


Introduction and background
Introduction and Background

  • As in other areas of PSIA, the choice of method for MDG strategy analysis depends on

    • the questions posed

    • data availability

    • resources (people, skills, time)

  • Questions often posed in country-level MDG strategy analysis:

    • Is it feasible to achieve the MDGs?

    • How much does it cost?

    • Under alternative foreign aid constraints, what are the trade-offs between different objectives (MDGs and others)?


Introduction and background1
Introduction and Background

  • The questions posed are very challenging -- the analyst needs to understand the functions determining MDG achievements. For example:

    Primary school completion rate =

    f(government education services per student, per-capita income, infrastructure, health of age cohort)

  • The analysis of poverty reduction strategies poses similar challenges.

  • Given the tall order, findings should be viewed as indicative.


Introduction and background2
Introduction and Background

  • This presentation will discuss:

    • Alternative methods for MDG strategy analysis:

      • Bottom-up sectoral costing methods

      • Economywide modeling methods

    • An application of (2) to Ethiopia


2 bottom up costing
2. Bottom-up costing

  • Stylized analytical steps:

    • For each MDG, determine needed “physical” inputs: investments; labor (at different skill levels); intermediate inputs

    • Compute costs of providing inputs using projected or current prices, wages, and exchange rates.

    • Assign costs to different agents (government, private sector, NGOs, …)


2 bottom up costing1
2. Bottom-up costing

  • Main advantage of this method:

    • Concrete and micro-based

    • Not very skill intensive.

  • Problems with Step 1:

    • Physical input needs by sector or MDG are not well-defined – different combinations of the determinants can achieve the objective;

    • MDG-specific inputs cannot be defined since some (all?) inputs contribute, directly or indirectly, to more than one MDG.

    • Marginal returns to inputs may vary depending on the value for the MDG indicator.


2 bottom up costing2
2. Bottom-up costing

  • Problems with Step 2:

    • Difficult to project costs – prices, wages, exchange rates change over time (cf. Dutch Disease effects; labor market constraints)

    • Cost-effectiveness of alternative policy combinations depends on cost structure.

  • Problems with Step 3:

    • Need for domestic government revenue influences MDG achievement (by reducing resources in private hands)

    • Need for aid depends on the exchange rate.


3 economywide modeling
3. Economywide Modeling

  • Rationale for economywide approach: In most low-income countries,

    • the pursuit of MDGs leads to major economic shock (macro, sectors, labor market, foreign aid)

    • sector-by-sector approach (partial equilibrium) analysis is not sufficient on its own

  • Problem with typical economywide models: They do not capture the output side of government spending.

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


4 structure of mams
4. Structure of MAMS

  • 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.


4 structure of mams1
4. Structure of MAMS

  • Distinguishing features of the government in MAMS:

    • It purchases government services, disaggregated into functions relevant to MDG analysis.

    • 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.

  • Like other CGE models, MAMS provides a full account of government expenditures (incl. interest payments, domestic transfers) and receipts (taxes, domestic borrowing, foreign borrowing, and foreign grants).


Mdg hd module
MDG/HD module

  • Nested functions for MDG analysis:

    • Top: MDG indicator =

      logistic or exponential fn (intermediate variable)

    • Bottom: intermediate variable =

      CE fn (gov services, other arguments)

      [where CE = constant-elasticity]

  • The nested MDG functions

    • are calibrated to:

      • replicate base values and elasticities under base conditions

      • achieve MDGs under conditions identified by sector studies

      • upper and lower bounds

    • have diminishing marginal returns to increases in bottom-level determinants



Education
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 nested- function structure

    • for arguments, see MDG2 in Table


Labor stocks
Labor stocks

  • 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


Other stocks and productivity
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 capital stocks

    • changes in openness (trade share in GDP)

    • exogenous trend


Poverty and inequality
Poverty and Inequality

  • Alternative approaches to poverty and inequality analysis:

    • aggregate poverty elasticity

    • representative household

    • microsimulation (integrated, top-down)


5 data for mams
5. Data for MAMS

  • Social Accounting Matrix with

    • Government consumption and investment disaggregated by MDG-related functions (main education cycles, health, water & sanitation, other public infrastructure, other government services)

    • Labor disaggregated by educational achievement

    • Otherwise highly flexible disaggregation


5 data for mams1
5. Data for MAMS

  • Base-year physical quantities

    • enrollment by educational cycle

    • labor by educational level

    • labor use by activity (private and public)

    • population

  • Base-year rates for MDGs and education

    • MDGs 2, 4, 5, 7

    • Student behavior (ex: graduation rates)


5 data for mams2
5. Data for MAMS

  • Elasticities in production, trade, consumption, and in the different MDG and education functions.

  • Sector studies linking MDG and educational achievements to specific values for the determinants (in the bottom level of the nest).


5 data for mams3
5. Data for MAMS

  • Typical data sources

    • Country statistical publications

    • World Bank country studies and data bases:

      • Public Expenditure Review (PER)

      • Country Economic Memoranda (CEM)

      • Development Policy Review (DPR)

      • World Development Indicators (WDI)

    • MDG-relevant studies including country-specific sector-focused needs assessments


6 illustrative analysis of ethiopia
6. Illustrative analysis of 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


6 illustrative analysis of ethiopia1
6. Illustrative analysis of Ethiopia

  • Scenarios

    • Base: business as usual

    • MDG-base: all MDGs achieved with unlimited foreign grants

    • MDG-infcut: constrained foreign grants; reduced spending on MDG- and growth-enhancing infrastructure

    • MDG-hdcut: constrained foreign grants; reduced spending on health, primary education, and water-sanitation


Evolution over time for mdg 2 net primary school completion rate by simulation
Evolution over Time for MDG 2Net 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
Evolution over Time for WagesWorkers with Secondary-School Education(By Simulation)

Note: Wages are shown in Ethiopian Birr




7 concluding remarks
7. Concluding remarks

  • MDG strategy analysis is a serious challenge to economic analysis – need for analysis using a variety of approaches (sectoral, econometric, economywide modeling).

  • Sector-by-sector, bottom-up costing analysis provides insights about input needs but is less convincing as a tool for comprehensive costing.

  • Economywide modeling can address the shortcomings of bottom-up costing, albeit at the cost of being more data and resource-intensive.


7 concluding remarks1
7. Concluding remarks

  • Activities under way as part of the MAMS-based research program:

    • Applications to countries in SSA and Latin America.

    • Streamlining of modeling framework

    • Training

    • Further development of documentation

    • User-friendly interface


References
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 and Carolina Diaz-Bonilla. 2006. MAMS: An Economywide Model for Analysis of MDG Country Strategies. Mimeo. Washington, D.C.: World Bank.

  • Lofgren, Hans and Carolina Diaz-Bonilla. 2005. Economywide Simulations of Ethiopian MDG Strategies. Washington, D.C.: World Bank.


References1
References

  • Reddy, Sanjay, and Antoine Heuty. 2004. Achieving the MDGs: A Critique and a Strategy. Mimeo. UNDP.

  • United Nations Millennium Project. 2005. Investing in Development: A Practical Plan to Achieve the Millennium Development Goals.


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