1. Achieving the MDGs in Kenya – a need for additional aid flows?*
Jörgen Levin
Jane Kiringai
Work in Progress
Presentation at School of Economics,
University of Nairobi, April 15, 2008
*Part of this power-point presentation is based on Lofgren, Diaz-Bonilla and Timmer (2007), Presentation for the Public Finance Analysis and Management Core Course, PREM Learning Week, April 27, 2007

2. Progress in achieving the MDGs
While there has been progress towards the Millennium Development Goals (MDGs) at the global level there are vast differences across and within regions and countries
Most of sub-Saharan Africa faces significant challenges in meeting the MDGs
A basic question is whether low-income countries can implement MDG programs and effectively ‘absorb’ much higher levels of aid and efficiently use them for the purpose of achieving the MDGs.

3. The Kenyan situation
Kenya has ascribed to the Millennium Declaration and is instituting measures to achieve Millennium Development Goals (MDGs).
A needs assessment study has been conducted and according to the report, Kenya requires a total of about US$ 61 billion during to realize the MDGs.
A MDGs status report on Kenya indicates that significant progress has been made towards achieving the goal of universal primary education. However, the Government will need to scale-up its efforts substantially beyond the current momentum, if the other goals are to be realised by 2015.

4. Policy issues
The policy issue we discuss is whether the 2007 budget strategy proposed by the Government would achieve the MDGs.
We also discuss the impact of additional external resources.
The paper is organised as follows:
In chapter two we discuss some recent macroeconomic events.
In the third chapter the MDGs are discussed in terms of progress and costing.
The fourth chapter explain the model and the data used in the study.
In the fifth section we present and discuss different policy scenarios and the impact of additional financial resources on the achievement of MDGs.
The final section concludes.

5. Outline of presentation
Issues in MDG strategy analysis – what an analytical framework should consider
The Structure of MAMS
Data for MAMS
Kenya-MAMS simulations

6. Issues in MDG strategy analysis
A framework for analysis of MDG strategies should consider the following factors:
Synergies between different MDGs
Role of non-government service providers
Demand-side conditions (incentives, infrastructure, incomes)
Role of economic growth
Macro consequences of increased government spending under different financing scenarios
Diminishing marginal returns (in terms of MDG indicators) to services and other determinants.
Unit service costs depend on efficiency and input prices (e.g. wages)

7. Issues in MDG strategy analysis
A simple first approach establishes feasible strategies and evaluate costs in an fixed-coefficient fixed-price framework
Such a framework does not consider important factors influencing the design of MDG strategies  it is limited and possibly misleading
Prices and exchange rates change;
Difficult to assign inputs to specific sectors/MDGs;
Synergies / externalities among MDGs can be important;
Changing private income / spending affects attainment;
Possible trade-offs between MDGs linked to levels and composition of government spending and revenues.

8. Model Structure
MAMS may be described as an extended, dynamic-recursive computable general equilibrium (CGE) model designed for MDG analysis.
MAMS is complementary to and draws extensively on sector and econometric research on MDGs.
Motivation behind the design of MAMS:
An economywide, flexible-price model is required.
Standard CGE models provide a good starting point
But Standard CGE approach must be complemented by a satisfactory representation of 'social sectors'.
Problem with typical economywide models: They do not capture the output side of government spending.

9. General Features
Many features are familiar from other open-economy, CGE models:
Computable  solvable numerically
General  economy-wide
Equilibrium 
optimizing agents have found their best solutions subject to their budget constraints
quantities demanded = quantities supplied in factor and commodity markets
macroeconomic balance
Dynamic-recursive  the solution in any time period depend on current and past periods, not the future.
In a dynamic-recursive model, decisions in this period depend on a set of parameters and past and current variable levels, but not on future variable values. As a result, it can (but doesn’t have to) be solved one year at a time.
The familiar features include:
complete account of payments/receipts for agents and at the macro level;
[mostly] price-clearing markets;
imperfect substitutability/ transformability in foreign trade)

10. MDGs
MAMS covers MDGs 1 (poverty), 2 (primary school completion), 4 (under-five mortality rate), 5 (maternal mortality rate), 7a (water access), and 7b (sanitation access).
The main originality of MAMS compared to standard CGE models is the inclusion of (MDG-related) social services and their impact on the rest of the economy.
Social services may be produced by the government and the private sector.

11. Government
Government services are produced using labor, intermediate inputs, and capital
Government consumption is classified by function: social services (education, health, water-sanitation), infrastructure and “other government”.
Government spending is split into
Recurrent: consumption, transfers, interest
Capital
Government spending is financed by taxes, domestic borrowing, “money printing”, foreign borrowing, and foreign grants.
Model tracks government domestic and foreign debt stocks (including foreign debt relief) and related interest payments.
The relevant functions: education, health, ….
The need for capital in government service production is normally not recognized in economic models.
National accounts rarely ascribe rent to government capital; standard profit-maximizing behavior (MVP = MC) is not feasible.

12. MDG “production”
Together with other determinants, government social services determine the "production" of MDGs.
MDGs are modeled as being “produced” by a combination of factors or determinants (table following) using a (reduced) functional form that permits:
Imposition of limits (maximum or minimum) given by logic or country experiences
Replication of base-year values and elasticities
Calibration of a reference time path for achieving MDGs
Diminishing marginal returns to the inputs
Two-level function:
Constant-elasticity function at the bottom: Z = f(X)
Logistic function at the top: M(DG) = g(Z)

13. Determinants of MDG outcomes
Service
per capita or student
Consump-tion per
Capita
Wage
incen-tives
Public infra- structure
Other MDGs
2–Primary schooling X 4
4-Under-five
mortality
2,5,7a,7b
5-Maternal mortality
2,4,7a,7b
7a-Water
7b-Sanitation
Wage incentives are defined as the ratio between two current wage rates: w1/w2:
w1 = the wage the student would earn if he/she stayed in school long enough to climb one notch up in the wage-skill-educational level hierarchy;
w2 = the wage the student would earn if he/she dropped out of school immediately;
If [w1/w2] goes up, you are encouraged to stay in school longer ….

14. Modeling education in MAMS
Service measured per student in each teaching cycle (primary, secondary, tertiary).
Model tracks evolution of enrollment in each cycle
Educational outcomes as functions of a set of determinants: for each cycle, rates of entry, pass, repeat, and drop out; between cycles, share that continues
MDG 2 (net primary completion rate) computed as product of 1st grade entry rate and primary cycle pass rates for the relevant series of years.
Enrollment in any cycle in any year = [old students that continue/repeat] + [entering graduates from earlier cycle] + [new entrants to school system].
Logistic functions used to model student behavior within and between cycles:
shares of relevant totals that enter 1st grade; in each grade, pass and continue, repeat, or drop out; and continue from one cycle to next
selected shares sum to unity;
If you have a 4-year primary cycle and MDG2 for 2015 is defined as the product of the 1st grade entry rate in 2012 and the passing rates in years 2012, 2013, 2014 and In order to achieve 100% (everyone in the right age cohort completing a full cycle of primary schooling by 2015), then all these shares have to be 1(00%).

15. Flexible modeling framework
MAMS has evolved from an Ethiopia-specific pilot version to one that is more widely applicable, and may include:
multiple sectors
multiple households
wide range of taxes
NGO + private MDG/HD services
special-case sectors (resource-based export sectors, regulated utilities)
MAMS can also be used as an simple two-sector (government – private) framework for dynamic macro analysis.
MAMS works with standard approaches to poverty and inequality analysis:
aggregate poverty elasticity
representative household
microsimulation (integrated, top-down)

16. Typical Simulations and Indicators
MAMS scenarios relevant to public-finance analysis may differ in terms of:
level and composition of government spending;
financing of government spending (different types of taxes, domestic borrowing, money printing)
government efficiency
Outcome indicators of interest include the evolution of:
Private and government consumption and investment, exports, imports, value-added, taxes; all indicators may be national totals are disaggregated
Domestic and foreign debt stocks
MDG indicators (poverty, non-poverty MDGs)

17. Country cases MAMS is being applied in numerous countries:
19 in Latin America and the Caribbean (in collaboration with the UNDP and UNDESA)
7 in Sub-Saharan Africa (Kenya, Uganda, Tanzania, Ghana, Madagascar, Malawi and Ethiopia)
In Ethiopia (the pilot country), MAMS has been extensively used by the World Bank and the government in the analysis of MDG and Poverty Reduction Strategies, as well as independent studies on demography, labor market, and aid/budget policy.

18. Data Basic data needs are similar to other CGE models:
Social Accounting Matrix (SAM); factor and population stocks; shares and elasticities in trade, production, and consumption
Data (and model) disaggregation highly flexible outside the government and the labor market
Data requirements specific to MAMS:
In SAM: government consumption and investment disaggregated by MDG-related functions; labor disaggregated by educational achievement;
Education parameters: stocks of students by educational cycle; student behavioral patterns (ex: rates of passing, repetition, dropout); population data with some disaggregation by age;
MDG data: base-year indicators; elasticities; service expansion required to reach MDGs (MDG scenarios)
Other worksheets
Ex: debt, foreign debt relief, growth rates
Education parameters: stocks of students, behavioral shares, number of years per cycle, etc.
Note: all data inputted into excel

19. MDG Values for Kenya

20. Kenya Data Base
Modified Kenya 2003 SAM (Thurlow, Kiringai and Wanjala, IFPRI 2006)
Public MDG sectors:
Primary education
Secondary education
Tertiary education
Health
Water and sanitation
Infrastructure
Public non-MDG sectors:
Other government
Private “non-MDG” sectors (MDG 1 only):
Agriculture
Industry
Services
Private MDG sectors:

21. MAMS Kenya findings Key results:
Baseline scenario there is some progress across all MDGs but not sufficient to reach the targets
Foreign grants would be the preferred option and the amount of resources is not extremely high.
Financing options:
To achieve the MDGS:
Foreign aid as a share of GDP has to increase five-fold compared to 2003
Domestic borrowing: Domestic debt as a share of GDP increases from 24.2% to 99.5%.
Foreign borrowing: External debt as a share of GDP increases from 28.8% to 83.6%.
Direct taxes as a share of GDP increases from 19.8% to 24.5%

22. MAMS Kenya findings

23. MAMS Kenya findings

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27. MAMS Kenya findings

28. MAMS Kenya findings

29. Future work Data issues: Applications:
Improve database (update SAM, labour market, household etc.)
Poverty methodology (include representative household groups and/or micro-simulation module in the model)
Government data
Applications:
Regional analysis – allocation of government spending and MDGs at regional level
Allocation of government expenditures – reallocation from MDG sectors to public administration
Trade-offs between spending on HD and INFRA
Vision 2030