Transcript MDG Strategy Analysis: Methods and an Agenda for Future Work

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
Progress in achieving the MDGs
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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.
The Kenyan situation
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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 2005-2015 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.
Policy issues
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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:
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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.
Outline of presentation
1. Issues in MDG strategy analysis – what an
analytical framework should consider
2. The Structure of MAMS
3. Data for MAMS
4. Kenya-MAMS simulations
Issues in MDG strategy analysis
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A framework for analysis of MDG strategies should
consider the following factors:
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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)
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
Model Structure
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MAMS may be described as an extended, dynamicrecursive computable general equilibrium (CGE)
model designed for MDG analysis.
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MAMS is complementary to and draws extensively
on sector and econometric research on MDGs.
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Motivation behind the design of MAMS:
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An economywide, flexible-price model is required.
– Standard CGE models provide a good starting point
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But Standard CGE approach must be complemented by a
satisfactory representation of 'social sectors'.
General Features
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Many features are familiar from other open-economy, CGE
models:
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Computable  solvable numerically
General  economy-wide
Equilibrium 
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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.
MDGs
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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).
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The main originality of MAMS compared to
standard CGE models is the inclusion of (MDGrelated) social services and their impact on the rest
of the economy.
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Social services may be produced by the government
and the private sector.
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.
MDG “production”
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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
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Two-level function:
1. Constant-elasticity function at the bottom: Z = f(X)
2. Logistic function at the top: M(DG) = g(Z)
Determinants of MDG outcomes
Service
per capita or
student
Consumption per
Capita
Wage
incentives
Public infrastructure
Other MDGs
2–Primary
schooling
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X
X
X
4
4-Under-five
mortality
X
X
X
2,5,7a,7b
5-Maternal
mortality
X
X
X
2,4,7a,7b
7a-Water
X
X
X
7b-Sanitation
X
X
X
MDG
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.
Flexible modeling framework
• MAMS has evolved from an Ethiopia-specific pilot version to one that is
more widely applicable, and may include:
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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)
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)
Country cases
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MAMS is being applied in numerous countries:
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19 in Latin America and the Caribbean (in collaboration
with the UNDP and UNDESA)
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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.
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 MDGrelated 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
MDG Values for Kenya
MDG 1
MDG 2
MDG 4
MDG 5
MDG 7a
MDG 7b
poverty rate (%)
primary (first cycle) school net completion rate (%)
under 5 mortality rate (per 1000 live births)
maternal mortality (per 100 000 live births)
access to safe water (%)
access to basic sanitation (%)
1990
49
63
99
590
48
84
2003
55
68
115
414
49
86
2015
24.5
100
33
147
74
92
Kenya Data Base
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Modified Kenya 2003 SAM (Thurlow, Kiringai and Wanjala, IFPRI 2006)
Public MDG sectors:
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Primary education
Secondary education
Tertiary education
Health
Water and sanitation
Infrastructure
Public non-MDG sectors:
– Other government
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Private “non-MDG” sectors (MDG 1 only):
– Agriculture
– Industry
– Services
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Private MDG sectors:
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Primary education
Secondary education
Tertiary education
Health
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%
MAMS Kenya findings
Real GDP at market prices (% annual average growth 20032015)
6.1
6
5.9
5.8
Serie1
5.7
5.6
5.5
5.4
base
mdg-fg
mdg-tax
mdg-fb
mdg-db
MAMS Kenya findings
Poverty (headcount ratio)
70
60
50
base
mdg-fg
%
40
mdg-tax
30
mdg-db
mdg-fb
20
10
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
MAMS Kenya findings
Net Primary School Completion Rate (%)
120
100
%
80
base
60
fg
40
20
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
MAMS Kenya findings
Economy-wide real Wages ('000 Ksh)
800
700
600
n-base
n-mdg-fg
n-mdg-tax
500
n-mdg-db
s-base
s-mdg-fg
400
s-mdg-tax
s-mdg-db
t-base
300
t-mdg-fg
t-mdg-tax
t-mdg-db
200
100
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
MAMS Kenya findings
Workers with no education ('000 Ksh)
6
5
4
n-base
n-mdg-fg
3
n-mdg-tax
n-mdg-db
2
1
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
MAMS Kenya findings
Foreign aid per capita required to reach to
reach all MDGs
90
80
2003 USD
70
60
50
base total
40
mdg-fg total
30
20
10
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
MAMS Kenya findings
Real exchange rate index
1.2
1
0.8
2003=1
base
mdg-fg
0.6
mdg-tax
mdg-db
0.4
0.2
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Future work
• Data issues:
– 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