Planning for Sustainable Development

VCC/CGSD EXTRACTIVE INDUSTRIES AND SUSTAINABLE DEVELOPMENT EXECUTIVE TRAINING PROGRAM

JUNE 16 , 2014

PROF. GORDON MCCORDCENTER ON GLOBALIZATION AND SUSTAINABLE

[email protected]

Planning for Sustainable Development

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Today’s Plan

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

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Suggested Further Reading

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Why a Differential Diagnosis

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

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Basic Idea of a Differential Diagnosis

Audience Ministries of Development, Planning, Education,

Public Works, Health, FinanceQuestions to Answer

What is holding back sustained reduction in poverty? Which national programs work? Which don’t? What is missing?

SpecificityEmpirically-based analysisWhat are the typologies of poverty in the

country?

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Typology of Poverty

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 programsMissing national programsRole of NGOs, development partners, private sector?

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Differential Diagnosis Checklist(see Jeffrey Sachs’ End of Poverty Chapter 3)

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Where are the Poor? Rates vs. Counts

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Targeting is enormously important for policy direction… how do Jordan’s poverty pockets match the poverty rate & count maps?

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Where is the Infrastructure?

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Differential Diagnosis for Jordan

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?

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Differential Diagnosis Checklist

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But Governance Doesn’t Explain Everything…

Azerba

ijan

Belarus

Chile

China

Domini

can R

epub

lic

Ecuad

or

El Salv

ador

Estonia

Ethiop

ia

Jorda

nMor

occoPeru

Vietna

m

-50

510

15G

row

th in

Inco

me

per C

apita

(19

99-2

009)

2 4 6 8 10Corruption Perceptions Index (2003)

Corruption and Economic Growth

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On Governance Failures

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, customsRemember 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

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Synergy of Integrated Interventions

Poverty Reduction

WaterAgriculture Economi

c Growth

HealthEducation

Gender Equity

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Core Interventions

Agriculture and Environment (staple yields, diversification, commercialization, landscape management)

Primary Health System (CHWs, clinical care, receiving hospital, safe childbirth, integrated management of childhood disease, home-based 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

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Empirical Regularities in Development Review some theory on poverty traps Millennium Development Goals Making International Development System

Coherent

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Diffusion of Industrial Revolution

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Structural Features within Countries

Conclusion: both geography and institutions affect distribution of poverty, across and within countries

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Basic Mechanics of Capital Accumulation

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Poverty Trap

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The Role of ODA in Breaking the Poverty Trap

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Private & Public Investments in Capital

Private sector requires all these forms of capital, which requires public investment!

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Table 1: Comparative Indicators

GDP per capita PPP (2001)

Per capita growth (1980-2000)

Life expectancy at birth (2001)

Under 5 mortality rate, per 1000 live births

(2001)

Population Growth (annual %) (2001)

Tropical Sub-Saharan Africa 1095 -1.1% 46.0 172.5 2.3South Asia 2597 3.3% 62.6 95.3 1.7Latin America 7358 0.5% 70.6 32.7 1.4East Asia and the Pacific 5915 6.4% 70.2 38.3 0.8Middle East and North Africa 5281 0.9% 68.4 49.8 2.0

Note: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in textSource: World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.

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Table 3: Governance Indicators AnalysisDependent variable:

Independent variables-3.27 -3.06 -2.67 -3.40 -3.37(-6.46) (-6.43) (-6.02) (-6.93) (-6.35)0.83(5.19)

-0.96(-2.72)

1.89(5.87)

1.57(5.28)

0.41(3.84)

-2.06 -1.65 -1.74 -1.97 -1.77(-6.73) (-5.84) (-6.80) (-6.72) (-5.53)

R-squared 0.58 0.45 0.58 0.59 0.54N 59 70 77 64 51

Notes: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in textt-statistics are indicated in parentheses, all coefficients are significantAll regressions are ordinary least-squares and include a constant term (not reported)

Regressions do not include high-income and ex-soviet countries

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.

1982-1997 Average ICRG Indicators

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).

1982 Average ICRG Indicators

2000 Average Kaufman, Kraay, Zoido-Lobaton indicators

log( GDP pc PPP in 1980)

Unreported regressions using more restrictive constant-dollar (PPP) GDP data from Maddison (2001) provided similar results

1980-2000 Growth of GDP per capita

Tropical Sub-Saharan Africa dummy

Corruption Perception Index 2003, Transparency International

2001 Index of Economic Freedom

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Table 4: Structural Features of Sub-Saharan AfricaTropical Sub-Saharan

AfricaRest of developing

world averageStatistical

SignificanceGeography

% Population within 100 km of coast 24.9 66.3 ***% Population in tropical ecozones 62.0 34.9 ***% Population in sub-humid and arid ecozones 81.9 38.7 ***% Percent population living at low density 45.2 26.5 ***

HealthMalaria ecology 13.0 2.5 ***Infant mortality rate 107.6 43.7 **Under-5 mortality rate 176.9 58.1 ***Total fertility rate 5.4 3.4 **

AgricultureIrrigated land (% of agricultural land) 0.5 10.6 ***Cereal yield (kg per hectare) 1102.0 2364.6 ***Fertilizer consumption (100 grams per hectare of arable land) 95.0 1606.2 ***

InfrastructurePaved Roads (km per 1000 people) <0.1 4.2Traditional Fuel Use (% of total energy use) 76.3 29.2 ***Notes: "Tropical Sub-Saharan Africa" refers to 33-country sample defined in textAverages are not weighted by populationFor Geography variables, third column refers to statistical significance of the difference in the two averagesFor 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% significanceHigh-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.

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Table 5: Agricultural Technology and Productivity

CGI contribution to yield growth

Cereal yield (kg per hectare)

% annual growth in cereal yield (kg per

hectare)

% annual growth in food production per

capita

1970 1980 1990 1998 1960-1998 2000 1980-2000 1980-2000Sub-Saharan Africa 1 4 13 27 0.280 1111.6 0.7 0.0Asia 13 43 63 82 0.884 3662.4 2.3 2.3Latin America 8 23 39 52 0.658 2809.2 1.9 0.9Middle East and North Africa 4 13 29 58 0.688 2659.9 1.2 1.0

Column 8 from FAOSTAT, available at http://apps.fao.org/default.jsp

Modern Variety (MV) diffusion (% of area planted to modern varieties)

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.

Columns 6-7 from World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.Sources: Columns 1-5 from Evenson, R.E. and D. Gollin. 2003. Crop Variety Improvement and its Effect on Productivity . FAO.

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Table 8: Savings rates

Gross national saving 1980-2001 (% of GNI)

Adjusted gross saving 1980-2001 (% of GNI)

Tropical Sub-Saharan Africa 11.1 3.0 (1.0*)South Asia 20.0 18.7Latin America 18.7 15.7East Asia and the Pacific 35.1 29.3Middle East and North Africa 23.5 9.1

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 Sub-Saharan 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.

Notes: Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, and net forest depletion.

* 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%

"Tropical Sub-Saharan Africa" refers to 33-country sample defined in text.

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Inter-Sectorial Problems & Solutions

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Synergy of Integrated Interventions

Poverty Reduction

WaterAgriculture Economi

c Growth

HealthEducation

Gender Equity

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Break!

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Implementing Goal-Oriented Development

Motivation Poverty traps require capital and technological push

(where governance is adequate!) Agreed-upon international financing framework

Measure indicators and trend at baselineIdentify “on-track” and “off-track” indicatorsConduct 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)

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Table 13: Progress towards achieving the MDGs in Ghana, Tanzania and Uganda

Indicator Earliest Most recent MDG Status

Earliest Most recent MDG Status

Earliest Most recent MDG Status

Proportion below national poverty line 31.4% (1992) 51.1% (1991) 55.0% (1993)

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 79% (2000)

Source: World Bank. 2003a. World Development Indicators 2003. World Bank, Washington D.C.

Ghana Tanzania Uganda

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Graphically…

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Needs Assessments

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Issues to Consider

Capital vs. Recurring CostsTotal vs. Incremental ResourcesMarginal vs. Average Costs

Disaggregate target populations if possibleSynergies

Especially in the health sectorFinancing

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

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Estimating

Financing Gaps

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Table 15: Estimated ODA Requirements for Ghana, Tanzania and Uganda to Achieve the MDGs

CountryODA p.c. in

2001 ($)

Estimated % of current ODA estimated as

going to MDGs

Implied current p.c. ODA going to MDGs ($)

MP estimate of ODA p.c.

required to meet MDGs ($)

Implied total ODA

required p.c. ($)

Implied current

shortfall in ODA p.c. ($)

Average estimated

GDP p.c. over 2004 - 2015

Minimum estimated ODA

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 0.21Tanzania 41 50 20.5 62.0 82.5 41.5 362 0.23Uganda 43 50 21.5 48.0 69.5 26.5 340 0.20Sources: Simon, David. 2003. "Official Development Assistance and the Millennium Development Goals." A report prepared for the Millennium Project Secretariat. (Authors' calculations)

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From Bilaterals From Multilaterals Total ODAODA Disbursements to Sub-Saharan Africa, 2002Grants 11,532.51 3,241.36 14,773.87Gross Loans 779.15 5,418.75 6,197.90Gross ODA 12,311.66 8,660.11 20,971.77Subtract:

Technical Cooperation -3,344.13 -487.17 -3,831.30 Development Food Aid -362.85 -108.39 -471.24 Emergency Aid -1,272.15 -652.47 -1,924.62 Debt Forgiveness Grants -2,961.45 -286.86 -3,248.31 Support to NGOs -437.11 -437.11

Sub-total Gross ODA paid into gov't budgets 3,933.97 7,125.22 11,059.19Principal Repayments actually made -541.23 -1,553.35 -2,094.58Interest Payments -96.87 -482.96 -579.83Estimated Budget Support 3,295.87 5,088.91 8,384.78Budget Support as % of Total Support 27% 59% 40%Source: Brian Hammond, DAC, OECD, personal correspondenceNote: Supports to NGOs based on DAC estimates

"Sub-Saharan Africa" refers to all Sub-Saharan African countries.

Table 16: Estimated Budget Support to Sub-Saharan Africa

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Incoherence in International System

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

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MDG-Based PRS

Every country has a national poverty reduction strategy “PRSP” serves as basis for World Bank & IMF programs Country-driven, results-oriented, comprehensive, partnership-oriented,

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

implementLocal communities & NGOs involved in service delivery &

oversight

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MDG-Based Poverty Reduction Strategy

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Transparent, Integrated & Consultative Process

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

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MDG-Based System

Differential Diagnosis Investment Plan Financial Plan Donor Plan

Magnitude Timing Predictability Harmonization

Public Management Plan Decentralization Training Information Technologies Measurable Benchmarks Audits Monitoring & Evaluation

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Quick Wins

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

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Example: Malaria & CHW Program

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Health Burden

Chronic vs. Infectious Disease

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Example: Malaria

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Cerebral Malaria

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Malaria’s Burden on Society

> 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)

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Malaria & Economic Growth

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Malaria Control

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 . . .)

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Calculating Costs…

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African Population Distribution

72Malaria Intervention Costs (all Africa)

0500

1,0001,5002,0002,5003,0003,5004,000

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Year

2005

US$

(mill

ions

)

LLINsIRSMicroscopyRDTCHWHuman ResourcesACTsOther drugsSevere MalariaIECMonitoring & EvaluationGlobal Overhead Costs

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Table 2: Cost Estimates:Totals (millions of US$) 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 AverageLLINs 700 770 854 58 199 760 831 916 121 263 547IRS 223 187 191 195 200 204 209 213 218 222 206CHW 645 660 676 692 708 724 741 758 776 793 717Microscopy 28 28 29 30 30 31 32 33 33 34 31Human Resources 677 693 709 726 743 760 778 796 814 832 753RDT 40 33 25 26 26 27 27 28 29 29 29ACTs 139 214 270 276 283 289 296 303 310 317 270Other drugs 26 13 0 0 0 0 0 0 0 0 4Severe Malaria 54 45 34 35 36 36 37 38 39 40 39IEC 7 7 7 8 8 8 8 8 9 9 8Monitoring & Evaluation 178 186 196 143 156 199 207 216 164 178 182Global Overhead Costs 272 284 299 219 239 304 317 331 251 272 279

Total 2,989 3,119 3,290 2,407 2,628 3,343 3,483 3,640 2,762 2,987 3,065Total per total population (dollars) $3.28 $3.35 $3.46 $2.48 $2.65 $3.30 $3.37 $3.45 $2.56 $2.72 $3.06Total per person at risk (dollars) $4.45 $4.54 $4.67 $3.34 $3.56 $4.43 $4.51 $4.61 $3.42 $3.62 $4.11

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Basic Arithmetic of Extreme Poverty

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.

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Intervention Strategies

• Deploying Scalable, Replicable Proven Interventions

• Combining Health Sector and Non-Health Sector Interventions

• Combining Prevention and Treatment

• Empowering Households

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Commission on Macroeconomics & Health found that 87% of the world’s poor do not live the most highly constrained institutional settings (p. 71)

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Success of Vertical Programs

Useful when systems are weakEffective for

Malaria (national campaigns, specialized knowledge) TB (DOTS) HIV/AIDS

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Human Resource Challenge

TrainingCoverage in Rural AreasSalaries and Brain Drain

Community Health Worker Strategy

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Reasons for Optimism

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

0

1

2

3

4

5

6

7

8

9

10

Aid for Health and Population

ODA (2006 USD billions)

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Concluding Thoughts

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

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Concluding Thoughts

- 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)

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Concluding Thoughts

- The span of relevant knowledge is vast!- Economics - Agronomy- Epidemiology - Education- Public Health - Law- Public Health - Finance- Business - Engineering- Political Sci. - History

- Few people (if any) have all the skills- Form good teams to inform your diagnosis

and your work!

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Concluding Thoughts

- Partner with academics in your country & abroad

- Analytical capacity- Fresh, impartial viewpoint- New methods

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Planning for Sustainable Development