Planning for Sustainable
Development
VCC/CGSD EXTRACTIVE INDUSTRIES AND SUSTAINABLE
DEVELOPMENT EXECUTIVE TRAINING PROGRAM
JUNE 16, 2014
PROF. GORDON MCCORD
CENTER ON GLOBALIZATION AND SUSTAINABLE
DEVELOPMENT
GM2101@COLUMBIA.EDU
Today’s Plan
2
 9:00am – 11:00am: Using a Differential
Diagnosis to Inform Sustainable Economic Policy
 11:00am – 11:15am: Coffee Break
 11:15am – 1:00pm: Designing and implementing a
public investment program
Suggested Further Reading
3
Why a Differential Diagnosis
4
 Like the human body, society is a complex system
 Interconnected subsystems
 Implies that many things can go wrong
 More importantly, possibility of cascading failures
 Problems require a differential diagnosis
 One symptom can have multiple causes



Not every fever requires budget cuts
Need to identify underlying cause
Checklist starting with most likely causes
 Context and setting is important
 Monitoring and evaluation crucial
Basic Idea of a Differential Diagnosis
5
 Audience
 Ministries of Development, Planning, Education, Public
Works, Health, Finance
 Questions to Answer
 What is holding back sustained reduction in poverty?
 Which national programs work? Which don’t?
 What is missing?
 Specificity
 Empirically-based analysis
 What are the typologies of poverty in the country?
Typology of Poverty
6
 Who are the poor? Where are they?
 Urban or rural? Widespread or pockets?
 Geographic isolation?
 Social exclusion?
 Access to services in education, health, transportation?
 Endogenous cultural barriers?


Labor force participation
Fertility behavior
 Sources of income?
 Targeting of existing national programs
 Missing national programs
 Role of NGOs, development partners, private sector?
Differential Diagnosis Checklist
(see Jeffrey Sachs’ End of Poverty Chapter 3)
7
Where are the Poor? Rates vs. Counts
8
9
Targeting is enormously
important for policy
direction… how do Jordan’s
poverty pockets match the
poverty rate & count maps?
10
Where is the Infrastructure?
11
12
Differential Diagnosis for Jordan
13
 What are the primary risk factors for poverty?
 What do the data say?

DHS & HEIS household surveys
 Unemployed women (1.34x)
 > 5 children (RR: 4.25x)
 < secondary education (4.82x)
 Non-Jordanian (1.82x)
 Amman (0.49x)
 Mafraq (1.54x)
 Rural?
Differential Diagnosis Checklist
14
Differential Diagnosis Checklist
15
Differential Diagnosis Checklist
16
Differential Diagnosis Checklist
17
-5
0
2
10
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5
ba
ija
Az
er
15
n
But Governance Doesn’t Explain Everything…
18
Corruption and Economic Growth
4
6
8
Corruption Perceptions Index (2003)
10
On Governance Failures
19
 Corruption can be frustrating, but is it a cause or a
symptom of poverty?


Petty corruption (e.g. traffic police)
Corruption that hinders private sector growth

Permits, ports, customs
 Remember good management costs money, so good
governance might be a chicken-and-egg problem
 Include in your differential diagnosis only
governance failures that are truly pernicious, as
opposed to symptoms of poverty
Differential Diagnosis Checklist
20
21
22
23
Differential Diagnosis Checklist
24
Synergy of Integrated Interventions
25
Agriculture
Water
Economic
Growth
Poverty Reduction
Health
Education
Gender
Equity
Core Interventions
26
 Agriculture and Environment (staple yields, diversification,
commercialization, landscape management)
 Primary Health System (CHWs, clinical care, receiving hospital, safe
childbirth, integrated management of childhood disease, homebased malaria control, control of NTDs, HIV, TB, etc.)
 Education (access, school meals, de-worming, hygiene, curriculum)
 Infrastructure (water and sanitation, roads, connectivity, electricity,
cook stoves, other)
 Business Development
 Gender empowerment
27
 Empirical Regularities in Development
 Review some theory on poverty traps
 Millennium Development Goals
 Making International Development System Coherent
28
Diffusion of Industrial Revolution
29
30
31
Structural Features within Countries
32
Conclusion: both geography and institutions affect distribution of poverty,
across and within countries
Basic Mechanics of Capital Accumulation
33
Poverty Trap
34
The Role of ODA in Breaking the Poverty Trap
35
Private & Public Investments in Capital
36
Private sector requires all these forms of capital, which requires public investment!
37
Table 1: Comparative Indicators
GDP per capita
PPP (2001)
Tropical Sub-Saharan Africa
South Asia
Latin America
East Asia and the Pacific
Middle East and North Africa
1095
2597
7358
5915
5281
Per capita growth Life expectancy at birth
(1980-2000)
(2001)
-1.1%
3.3%
0.5%
6.4%
0.9%
Note: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in text
Source: World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.
46.0
62.6
70.6
70.2
68.4
Under 5 mortality rate,
per 1000 live births
(2001)
Population Growth
(annual %) (2001)
172.5
95.3
32.7
38.3
49.8
2.3
1.7
1.4
0.8
2.0
Table 3: Governance Indicators Analysis
Dependent variable:
Independent variables
Tropical Sub-Saharan Africa dummy
Corruption Perception Index 2003,
Transparency International
1980-2000 Growth of GDP per capita
-3.27
-3.06
-2.67
-3.40
-3.37
(-6.46)
(-6.43)
(-6.02)
(-6.93)
(-6.35)
0.83
(5.19)
38
-0.96
2001 Index of Economic Freedom
(-2.72)
2000 Average Kaufman, Kraay, ZoidoLobaton indicators
1.89
(5.87)
1.57
1982-1997 Average ICRG Indicators
(5.28)
0.41
1982 Average ICRG Indicators
log( GDP pc PPP in 1980)
R-squared
N
(3.84)
-2.06
-1.65
-1.74
-1.97
-1.77
(-6.73)
(-5.84)
(-6.80)
(-6.72)
(-5.53)
0.58
59
0.45
70
0.58
77
0.59
64
0.54
51
Notes: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in text
t-statistics are indicated in parentheses, all coefficients are significant
All regressions are ordinary least-squares and include a constant term (not reported)
Unreported regressions using more restrictive constant-dollar (PPP) GDP data from Maddison (2001) provided
similar results
Regressions do not include high-income and ex-soviet countries
The Corruption Perception Index relates to perceptions of the degree of corruption as seen by business people,
academics and risk analysts, and ranges between 10 (highly clean) and 0 (highly corrupt); the Index of Economic
Freedom ranges from 1 to 5, where 5 indicates greatest level of government interference in the economy and least
economic freedom; the Kaufmann, Kraay, Zoido-Lobaton indicators are six governance indicators measured in
units ranging from about -2.5 to 2.5, with higher values corresponding to better governance outcomes; the ICRG
indicators include the average of six governance indicators from 1992-1997, where higher scores reflect better
governance (ranging from 1 to 6).
Sources: Corruption Perception Index from Transparency International. 2004. Global Corruption Report 2004. Pluto
Press, London; Index of Economic Freedom from Miles, Marc, E. Feulner, M.A. O'Grady, "2004 Index of Economic
Freedom," Heritage Foundation and the Wall Street Journal; Average Kaufmann, Kraay, Zoido-Lobaton from
Kaufmann, D., A. Kraay, and P. Zoido-Lobaton, "Governance Matters II--Updated Indicators for 2000/01," World
Bank Policy Research Department Working Paper No. 2722, Washington D.C., 2002; Average ICRG indicators from
ICRG (International Country Risk Guide). 2004. available at http://www.prsgroup.com/icrg/icrg.html, GDP data from
World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.
Table 4: Structural Features of Sub-Saharan Africa
Geography
% Population within 100 km of coast
% Population in tropical ecozones
% Population in sub-humid and arid ecozones
% Percent population living at low density
Health
Malaria ecology
Infant mortality rate
Under-5 mortality rate
Total fertility rate
Agriculture
Irrigated land (% of agricultural land)
Cereal yield (kg per hectare)
Fertilizer consumption (100 grams per hectare of
arable land)
Infrastructure
Paved Roads (km per 1000 people)
Traditional Fuel Use (% of total energy use)
Tropical Sub-Saharan
Africa
Rest of developing
world average
Statistical
Significance
24.9
62.0
81.9
45.2
66.3
34.9
38.7
26.5
***
***
***
***
13.0
107.6
176.9
5.4
2.5
43.7
58.1
3.4
***
**
***
**
0.5
1102.0
10.6
2364.6
***
***
95.0
1606.2
***
<0.1
76.3
4.2
29.2
***
39
Notes: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in text
Averages are not weighted by population
For Geography variables, third column refers to statistical significance of the difference in the two averages
For other variables, third column refers to statistical significance of 33-country sample dummy in the following regression:
structural variable = a + b1(log GDP per capita PPP) + b2(dummy for 33-country sample)
Tobit regression used when structural variable is a percentage, otherwise OLS used taking the log of the dependent variable
*** 1% significance, ** 5% significance, * 10% significance
High-income and ex-soviet countries excluded throughout
Sources: Calculated from World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.; Center for International
Earth Science Information Network (CIESIN), Columbia University, 2002. National Aggregates of Geospatial Data: Population, Landscape
and Climate Estimates (PLACE), Palisades, NY: CIESIN, Columbia University. Available at:
http://sedac.ciesin.columbia.edu/plue/nagd/place.; Kiszewski, Anthony, Andrew Mellinger, Pia Malaney, Andrew Spielman, Sonia Ehrlich,
Jeffrey D. Sachs. (forthcoming) "A Global Index of the Stability of Malaria Transmission Based on the Intrinsic Properties of Anopheline
Mosquito Vectors." American Journal of Tropical Medicine and Hygiene, forthcoming.
40
Table 5: Agricultural Technology and Productivity
Modern Variety (MV) diffusion (% of area planted to
modern varieties)
Sub-Saharan Africa
Asia
Latin America
Middle East and North Africa
1970
1
13
8
4
1980
4
43
23
13
1990
13
63
39
29
1998
27
82
52
58
CGI contribution to Cereal yield (kg per
yield growth
hectare)
% annual growth in % annual growth in
cereal yield (kg per food production per
hectare)
capita
1960-1998
2000
1980-2000
1980-2000
0.280
0.884
0.658
0.688
1111.6
3662.4
2809.2
2659.9
0.7
2.3
1.9
1.2
0.0
2.3
0.9
1.0
Note: "Sub-Saharan Africa" refers to all countries in columns 1-5 and in columns 6-8 refers to 33-country sample defined in text.
Sources: Columns 1-5 from Evenson, R.E. and D. Gollin. 2003. Crop Variety Improvement and its Effect on Productivity . FAO.
Columns 6-7 from World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.
Column 8 from FAOSTAT, available at http://apps.fao.org/default.jsp
Table 8: Savings rates
Gross national saving 1980- Adjusted gross saving 19802001 (% of GNI)
2001 (% of GNI)
Tropical Sub-Saharan Africa
South Asia
Latin America
East Asia and the Pacific
Middle East and North Africa
41
11.1
20.0
18.7
35.1
23.5
3.0 (1.0*)
18.7
15.7
29.3
9.1
Notes: Adjusted net savings are equal to net national savings plus education expenditure and minus energy
depletion, mineral depletion, and net forest depletion.
"Tropical Sub-Saharan Africa" refers to 33-country sample defined in text.
* We used nutrient depletion indicators and fertilizer prices to calculate Tropical Sub-Saharan Africa's soil
depletion to be around 2% of GDP, which would reduce adjusted gross saving to 1.5%
Sources: World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.; Soil
nutrient depletion calculated with 1999 Sub-Saharan Africa nutrient balance midpoint of 60-100 NPK kg/ha
from (Henao, J. & Baanante, C. 1999. Nutrient depletion in the agricultural soils of Africa. 2020 Brief 62,
October 1999. Washington, DC, IFPRI. (http://www.cgiar.org/ifpri/2020/briefs)) using 1982-1984 SubSaharan Africa N-P-K depletion ratio of 22-2.5-15 from (Stoorvogel, J. J., E. M. A. Smaling, and B. H.
Janssen. 1993. Calculating soil nutrient balances in Africa at different scales. Fertilizer Research. No. 35:
227-335). GDP figures taken from World Bank. 2003a. World Development Indicators 2003. World Bank,
Washington D.C.; prices taken from African Agricultural Market Information Network accessed at
http://www.afamin.net/regionalenglish/reg_mis_en.asp on 8 March 2004.
Inter-Sectorial Problems & Solutions
42
43
Synergy of Integrated Interventions
44
Agriculture
Water
Economic
Growth
Poverty Reduction
Health
Education
Gender
Equity
45
Break!
Implementing Goal-Oriented Development
46
 Motivation

Poverty traps require capital and technological push


(where governance is adequate!)
Agreed-upon international financing framework
 Measure indicators and trend at baseline
 Identify “on-track” and “off-track” indicators
 Conduct needs assessment and costing



Households
Model increased domestic resource mobilization
Foreign assistance
 Calculate foreign assistance gap


Is it within means/obligations of donors?
Monterrey Consensus (2002)
47
Table 13: Progress towards achieving the MDGs in Ghana, Tanzania and Uganda
Ghana
Indicator
Earliest
Most recent MDG
Status
Earliest
Tanzania
Most recent
MDG
Status
Earliest
Uganda
Most recent
MDG
Status
Proportion below national poverty line
31.4% (1992)
Prevalence of Child Malnutrition (weight for
age)
27.3% (1994)
24.9% (1999)
Off track
28.9% (1992)
29.4% (1999)
Off track
25.5% (1995)
23.0% (2000)
On track
Primary net enrollment rate
56.8% (1998)
58.3% (2000)
Off track
51.4% (1990)
46.7% (2000)
Off track
87.3% (1997) 109.5% (2000)
On track
Ratio girls/boys in primary and secondary
education
85.8% (1998)
88.2% (2000)
Off track
96,8% (1990)
98.9% (2000)
Off track
88.3% (1998)
88.9% (2000)
Off track
Under-five mortality rate (per 1000)
126 (1990)
100 (2001)
Off track
163 (1990)
165 (2001)
Off track
165 (1990)
124 (2001)
Off track
% with access to improved water supply
53% (1990)
73% (2000)
On track
38% (1990)
68% (2000)
On track
45% (1990)
52% (2000)
Off track
% with access to improved sanitation
61% (1990)
72% (2000)
On track
84% (1990)
90% (2000)
On track
51.1% (1991)
Source: World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.
55.0% (1993)
79% (2000)
Graphically…
48
Needs Assessments
49
50
Issues to Consider
51
 Capital vs. Recurring Costs
 Total vs. Incremental Resources
 Marginal vs. Average Costs

Disaggregate target populations if possible
 Synergies

Especially in the health sector
 Financing

Appropriate fees (e.g. lifeline tarrifs)

Int’l Consensus: no fees for basic healthcare and primary education
 Absorptive Capacity Constraints


Investment opportunities
Distinct from bad governance
52
Estimating
Financing
Gaps
53
Table 15: Estimated ODA Requirements for Ghana, Tanzania and Uganda to Achieve the MDGs
Country
ODA p.c. in
2001 ($)
Estimated % of
Implied
MP estimate of Implied total
Implied
Average
current ODA
current p.c.
ODA p.c.
ODA
current
estimated
Minimum
estimated as
ODA going
required to
required
shortfall in GDP p.c. over estimated ODA
going to MDGs to MDGs ($) meet MDGs ($)
p.c. ($)
ODA p.c. ($) 2004 - 2015
as % GDP
(a)
(b)
(c)=(a)*(b)
(d)
(e)=(c)+(d)
(f)=(e)-(a)
(g)
(h)=(e)/(g)
Ghana
29
50
14.5
50.0
64.5
35.5
313
0.21
Tanzania
41
50
20.5
62.0
82.5
41.5
362
0.23
Uganda
43
50
21.5
48.0
69.5
26.5
340
0.20
Sources: Simon, David. 2003. "Official Development Assistance and the Millennium Development Goals." A report prepared for the
Millennium Project Secretariat. (Authors' calculations)
54
55
56
Table 16: Estimated Budget Support to Sub-Saharan Africa
From Bilaterals
ODA Disbursements to Sub-Saharan Africa, 2002
Grants
Gross Loans
Gross ODA
Subtract:
Technical Cooperation
Development Food Aid
Emergency Aid
Debt Forgiveness Grants
Support to NGOs
Sub-total Gross ODA paid into gov't budgets
Principal Repayments actually made
Interest Payments
Estimated Budget Support
Budget Support as % of Total Support
Source: Brian Hammond, DAC, OECD, personal correspondence
Note: Supports to NGOs based on DAC estimates
"Sub-Saharan Africa" refers to all Sub-Saharan African countries.
From Multilaterals
Total ODA
11,532.51
779.15
12,311.66
3,241.36
5,418.75
8,660.11
14,773.87
6,197.90
20,971.77
-3,344.13
-362.85
-1,272.15
-2,961.45
-437.11
3,933.97
-541.23
-96.87
3,295.87
27%
-487.17
-108.39
-652.47
-286.86
-3,831.30
-471.24
-1,924.62
-3,248.31
-437.11
11,059.19
-2,094.58
-579.83
8,384.78
40%
7,125.22
-1,553.35
-482.96
5,088.91
59%
Incoherence in International System
57
 Bilateral Donors
 USAID, GTZ (Germany), JICA (Japan), DfID (UK)
 Global Multilaterals
 World Bank, Global Fund to Fight AIDS, TB and Malaria
 Regional Multilaterals
 Asian Development Bank, Inter-American Development Bank
 Foundations
 Gates
 UN System (not big donors, but they house sector knowledge)
 FAO, IFAD, WFP, WHO, UNICEF, UNFPA, UNDP, UNEP, UN-HABITAT
 IMF & Finance Ministries Set Macroeconomic Framework &
Budgets!



Are they oriented to meet the MDGs?
Goal-oriented planning vs. “do the best you can with what you have”
Cutting Ghana down from $75 to $6
MDG-Based PRS
58
 Every country has a national poverty reduction strategy



“PRSP” serves as basis for World Bank & IMF programs
Country-driven, results-oriented, comprehensive, partnershiporiented, and based on longer-term perspective
Macro framework, ODA, budgetary ceilings set independent of needs
 Make them goal-oriented & work backwards from goals


Helps transparency & accountability
Makes planning practical and clarifies tradeoffs
 3-5 year MDG-based PRSs should be within a 10-year
framework for action


Public investment & budgetary frameworks
Especially for public sector management strategies which take time to
implement
 Local communities & NGOs involved in service delivery &
oversight
MDG-Based Poverty Reduction Strategy
59
Transparent, Integrated & Consultative Process
60
 MDG-based PRSs developed in an open process
 Domestic & foreign stakeholders
 MDG strategy group chaired by national government
Bilateral & multilateral donors
 UN agencies
 Provincial & local authorities
 Domestic civil society leaders (including women’s orgs)



Thematic working groups
Designate MDG coordinator (in MoP or MoE) integrates work
MDG-Based System
61




Differential Diagnosis
Investment Plan
Financial Plan
Donor Plan




Magnitude
Timing
Predictability
Harmonization
 Public Management Plan
 Decentralization
 Training
 Information Technologies
 Measurable Benchmarks
 Audits
 Monitoring & Evaluation
Quick Wins
62
 High visibility & high impact to catalyze change, e.g.:
 Agroforestry techniques to triple yields in nitrogen-depleted soil
 Fertilizer distribution programs
 Anti-retroviral drugs for HIV+ population
 Long-lasting insecticide treated bednets to fight malaria
 Eliminate user fees for uniforms, schools, clinics
 Free locally-sourced school meals programs
 Free annual deworming campaigns
Example: Malaria & CHW Program
63
Health Burden
64
 Chronic vs. Infectious Disease
Example: Malaria
65
Cerebral Malaria
66
Malaria’s Burden on Society
67
 > 1 million deaths, up to 1 billion cases
 Children



School absenteeism, cognitive detriment of parasitaemia
Reduced education attainment, literacy (Barreca 2007, Lucas 2009)
Reduced adult productivity (Bleakley 2010)
 Adults




Reduced productivity due to anemia
Labor absenteeism
Cost of child replacement
Within-country displacement
 Demography

Child mortality delays demographic transition (McCord, 2011)
 Macro Effects via FDI, Tourism

Cross country evidence on income levels (Cartsensen and Gundlach) and
growth (Sachs)
Malaria & Economic Growth
68
Malaria Control
69
 Components of a Fully-Deployed Program
Mass distribution of bed nets
 Mass availability of ACTs
 Mass training of community health workers
 Improved rapid diagnostics
 Indoor Residual Spraying
 Coordination with other “neglected diseases” such as
eukaryotic infections (LF, oncho, ascaris, hookworm . . .)

Calculating Costs…
70
African Population Distribution
71
Year
15
20
14
20
13
20
12
20
11
20
10
20
09
20
08
20
07
20
06
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
20
2005 US$ (millions)
72 Costs (all Africa)
Malaria Intervention
LLINs
IRS
Microscopy
RDT
CHW
Human Resources
ACTs
Other drugs
Severe Malaria
IEC
Monitoring & Evaluation
Global Overhead Costs
73
Table 2: Cost Estimates:
Totals (millions of US$)
LLINs
IRS
CHW
Microscopy
Human Resources
RDT
ACTs
Other drugs
Severe Malaria
IEC
Monitoring & Evaluation
Global Overhead Costs
Total
Total per total population (dollars)
Total per person at risk (dollars)
2006
700
223
645
28
677
40
139
26
54
7
178
272
2,989
$3.28
$4.45
2007
770
187
660
28
693
33
214
13
45
7
186
284
3,119
$3.35
$4.54
2008
854
191
676
29
709
25
270
0
34
7
196
299
3,290
$3.46
$4.67
2009
58
195
692
30
726
26
276
0
35
8
143
219
2,407
$2.48
$3.34
2010
199
200
708
30
743
26
283
0
36
8
156
239
2,628
$2.65
$3.56
2011
760
204
724
31
760
27
289
0
36
8
199
304
3,343
$3.30
$4.43
2012
831
209
741
32
778
27
296
0
37
8
207
317
3,483
$3.37
$4.51
2013
916
213
758
33
796
28
303
0
38
8
216
331
3,640
$3.45
$4.61
2014
121
218
776
33
814
29
310
0
39
9
164
251
2,762
$2.56
$3.42
2015
Average
263
547
222
206
793
717
34
31
832
753
29
29
317
270
0
4
40
39
9
8
178
182
272
279
2,987
3,065
$2.72
$3.06
$3.62
$4.11
Basic Arithmetic of Extreme Poverty
74
 Per capita income of $300 (or less)
 Government revenues of 15% of GNP or less
 Therefore, Government Revenues of $45 per capita per year, to
cover: President and parliament, public administration, military,
police, roads, power, rail, ports, water and sanitation, primary
and secondary education, higher education, environmental
management, climate change adaptation, courts and judicial
system, and HEALTH.
 In the high-income countries, per capita income is $40,000 and
government revenues are around 40% of GNP, meaning $16,000
per capita per year.
75
Intervention Strategies
76
• Deploying Scalable, Replicable Proven
Interventions
• Combining Health Sector and Non-Health Sector
Interventions
• Combining Prevention and Treatment
• Empowering Households
77
Commission on
Macroeconomics & Health
found that 87% of the
world’s poor do not live
the most highly
constrained institutional
settings (p. 71)
Success of Vertical Programs
78
 Useful when systems are weak
 Effective for
 Malaria (national campaigns, specialized knowledge)
 TB (DOTS)
 HIV/AIDS
Human Resource Challenge
79
 Training
 Coverage in Rural Areas
 Salaries and Brain Drain
 Community Health Worker Strategy
80
81
82
Reasons for Optimism
83
Aid for Health and Population
10
9
8
7
6
5
ODA (2006 USD billions)
4
3
2
1
0
Concluding Thoughts
84
 Development planning & financing is a little
incoherent
 Goal-oriented planning built on solid
interdisciplinary science, followed by needs
assessments, financial frameworks and gap analysis
ensures coherence
Concluding Thoughts
85
During the diagnosis, look for the deep answers
-
Beware of the “easy” explanations
-
Corruption
Cultural Problem (e.g. laziness)
Talk to everyone
-
Community members
NGOs
Other implementing bodies (e.g. microlenders)
Government (education, health, infrastructure, tax policy,
subsidies)
UN Agencies (expertise, implementation)
Concluding Thoughts
86
The span of relevant knowledge is vast!
-
-
-
Economics
Epidemiology
Public Health
Public Health
Business
Political Sci.
- Agronomy
- Education
- Law
- Finance
- Engineering
- History
Few people (if any) have all the skills
Form good teams to inform your diagnosis and
your work!
Concluding Thoughts
87
Partner with academics in your country & abroad
-
Analytical capacity
Fresh, impartial viewpoint
New methods