Analyzing MDG Strategies

Hans LofgrenCarolina Diaz-Bonilla

DECPGWorld Bank

Presentation prepared for the Workshop “Experiences with EPIAMin 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

• At the UN Millennium Summit of 2000, the world’s leaders agreed on a set of goals and targets for 2015: 1. Halving poverty and hunger rates (relative to

the 1990 rates)

2. Achieving universal primary education

3. Eliminating gender disparity in education

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

1. Introduction and Background

….5. Reducing by three quarters the maternal

mortality rate (relative to the 1990 rates)6. Reversing the spread of HIV/AIDS, malaria

and other major diseases7. Halving the population shares without

sustainable access to safe water and improved sanitation.

8. Developing a global partnership for development

1. 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:

1. Is it feasible to achieve the MDGs?2. How much does it cost? 3. Under alternative foreign aid constraints, what are

the trade-offs between different objectives (MDGs and others)?

1. 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.

1. Introduction and Background

• This presentation will discuss:– Alternative methods for MDG strategy

analysis:1. Bottom-up sectoral costing methods

2. Economywide modeling methods

– An application of (2) to Ethiopia

2. Bottom-up costing

• Stylized analytical steps:1. For each MDG, determine needed “physical”

inputs: investments; labor (at different skill levels); intermediate inputs

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

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

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 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

• 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

• 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 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

• 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

Table. Determinants of MDG achievements

MDG

Service

Delivery

Per-cap cons’on

Wage

incentives

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 nested- function structure – for arguments, see MDG2 in Table

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

• 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

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

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 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 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 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

• 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 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 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

base

mdg-base mdg-infcut

mdg-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

1900

2000

2100

2200

2300

2400

2500

2600

2700

2800

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

base

mdg-base

mdg-infcut

mdg-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

base

mdg-base

mdg-infcut

mdg-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:

Sha

re o

f HD

Tar

get (

%)

Share of Poverty Target (%)

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 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

• 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.

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.