One Researcher's Viewpoint on Policy Issues Relating to What have we - PowerPoint PPT Presentation

One Researcher's Viewpoint on Policy Issues Relating to ‘What have we learned, and what’s next?’ on the MDGs Jere R. Behrman University of Pennsylvania, Philadelphia, PA USA Policy Conference on Reaching the MDGs (12 June), 6th General Poverty & Economic Policy (PEP) Research Network Meeting Sheraton Lima Hotel, Paseo de la Republica 170, Lima, Peru 9-16 June 2007 University of Pennsylvania Behrman 1

Millennium Development Goals (MDGs) • MDG 1: Eradicate extreme poverty and hunger • MDG 2: Achieve universal primary education • MDG 3: Promote gender equality and empower women • MDG 4: Reduce child mortality • MDG 5: Improve maternal health • MDG 6: Combat HIV/AIDS, malaria and other diseases • MDG 7: Ensure environmental sustainability • MDG 8: Develop a global partnership for development Established in 2000 with targets for 2015, monitoring; Lustig provides more details. University of Pennsylvania Behrman 2

• Also considerable & increasing research on these & related issues in developing world. • Question arises, what have we learned from this research that is germane to policies related to MDGs? • This paper gives perspective of one development economics researcher with experience in Africa, Asia and Latin America on this question, with emphasis on MDG1-7 (Lustig on MDG 8 as well). • Some might wish that a research perspective would result in set of magic bullets – “Do this. Do not do that” – but world too complicated & information too limited to provide such a simple list. • But hopefully following six general points constitute a perspective that helpful for policy considerations. University of Pennsylvania Behrman 3

1. Essential to place research and policy implications within framework for basic policy motives: (1) efficiency and (2) distribution • Efficiency: social = private rates of return; otherwise can improve welfare of all or of some at no cost to others. • Distribution: e.g. lessen poverty • Tradeoffs (opportunity costs of resources) versus win-win (e.g., educational spillovers on technology adoption; improving markets for capital, insurance, information with particular benefits for poorest) University of Pennsylvania Behrman 4

2. Important to assess policy options in terms of their relative economic costs • Policy hierarchy (e.g., MDG 4 child mortality targets could be obtained with many policies, but costs differ) • Economic costs, public and private costs (distortion costs), not governmental budgetary costs (includes transfers); Economic benefit-cost ratios. • Copenhagen Consensus: 8 leading economists (4 Nobel Laureates) & later UN ambassadors prioritized policies: 1 civil conflicts; 2 climate change (MDG7); 3 communicable diseases (MDG6); 4 education (MDG2); 5 financial stability; 6 governance; 7 hunger & malnutrition (MDG1); 8 migration; 9 trade reform; 10 water & sanitation (MDG6, 7). • 4-5 projects in each, B-C ratios by experts. Challenging, but informative. E.G. CC7 hunger & malnutrition (MDG1) University of Pennsylvania Behrman 5

Example: Present discounted value of shifting one LBW infant to non-LBW status in low-income country, 5% discount rate PDV % of column Reduced infant mortality $93 16 Reduced neonatal and $80 14 infant/child illness costs Increased physical $99 17 productivity Increased cognitive ability $239 41 Reduced costs of chronic $23 4 diseases Intergenerational benefits $45 8 Sum of PDV $580 University of Pennsylvania Behrman 6

Sensitivity of benefits to averting low birthweights to changes in discount rate 1% 3% 5% 10% Reduced infant mortality $96 $95 93 88 Reduced illness costs 81 81 80 78 Gains from increased 351 249 99 28 physical productivity Gains from increased 846 600 239 69 cognitive ability Reductions in costs of 239 132 23 1 chronic diseases Intergenerational benefits 422 219 45 7 Sum of PDV 2037 1378 580 273 % of 5% discount rate 351% 170% 100% 47% University of Pennsylvania Behrman 7

Benefit-Cost Ratios for these Opportunities University of Pennsylvania Behrman 8

University of Pennsylvania Behrman 9

3. Policies usually have unintended or indirect effects: • Increase resources for some & change incentives for behaviors for individuals and families and other entities, including service providers (e.g., in health and education) and governmental bureaucrats (e.g., rents from policy-created restrictions, patronage). • E.g., Nutrition programs targeted towards children but families redistribute • E.g., High administrative & logistic costs of in-kind programs • E.g., Poorly targeted • Such concerns behind Conditional Cash Transfer programs (e.g., Mexican PROGRESA) University of Pennsylvania Behrman 10

4. Policies likely to be more effective the more closely targeted to the real objective: • Often policies targeted to intermediate, not ultimate objectives. • E.g., MDG 2 and 3 on school enrollments & gender equality. • Schooling attainment or learning of real interest. • Enrollment & attendance targets create incentives for schools to over-report, but not to assure learning. • School enrollment often lower for girls; therefore concern about disadvantaging girls. But in some cases (e.g., Malawi, Mexico), boys fail & repeat or drop out & re-enter school more & have higher enrollment, but lower attainment. Therefore PROGRESA higher scholarship for girls incentive to increase gender gap in school attainment. University of Pennsylvania Behrman 11

• Positive example: teacher absence perceived problem that limits student learning. –Recent policy evaluation experiment in rural India: schools provided cameras with unalterable time/date mechanisms; teacher bonuses depending on teachers present -- increased teacher presence & student tests. • Related but different example: MDG 4-6 emphasis on traditional health problems of developing countries – communicable, maternal, perinatal and nutritional conditions (CMPNC). But non-communicable diseases (NCD) are larger & predicted growing share of health problems. So focus on CMPNC may divert attention from more important health problems. University of Pennsylvania Behrman 12

Chart 7A. % Composition of DALYs Projected for Three Major GBD/WHO Categories for All Developing Countries 60% 54% 50% 50% 46% 41% 40% 37% 32% 30% 20% 14% 13% 13% 10% 0% 2005 2015 2030 CMPNC NCD Injuries University of Pennsylvania Behrman 13

Table 3. Ranking of Top Causes among Projected DALYs for All Developing Countries and for Low-Income Developing Countries Ranking of Top Percentage Shares of Total DALYS for Leading Projected Conditions Causes Causes Ranked for All Aggregate Low- Developing Countries in Tripartite All Income 2005 Category Developing Developing Low-Income Developing of Causes Countries Countries All Developing Countries Countries 2005 2030 2005 2030 2005 2015 2030 2005 2015 2030 Neuropsychiatric conditions NCD 1 2 1 1 12.2% 13.0% 13.3% 11.4% 11.7% 11.8% Cardiovascular diseases NCD 3 4 2 3 9.9% 10.2% 10.9% 10.2% 10.2% 10.5% Unintentional injuries Injuries 2 1 3 4 9.3% 9.5% 9.5% 11.3% 11.5% 11.8% Perinatal conditions CMPNC 4 9 4 9 6.7% 5.6% 4.1% 7.0% 5.9% 4.3% HIV/AIDS CMPNC 6 3 5 2 6.0% 8.2% 11.0% 5.9% 8.6% 11.2% Respiratory infections CMPNC 5 10 6 10 6.3% 4.7% 3.1% 6.2% 4.8% 3.3% Sense organ diseases NCD 9 6 7 5 5.0% 6.0% 7.3% 4.5% 5.1% 6.0% Malignant neoplasms NCD 8 8 8 7 4.5% 5.1% 5.8% 4.6% 5.1% 5.8% Respiratory diseases NCD 11 5 9 6 3.8% 4.7% 6.1% 4.0% 5.0% 6.4% Diarrheal diseases CMPNC 10 12 10 13 4.3% 3.2% 2.1% 4.3% 3.2% 2.2% Intentional injuries Injuries 7 7 11 8 3.5% 3.8% 4.1% 4.9% 5.3% 6.0% University of Pennsylvania Behrman 14

5. Policy effectiveness depends on context so not necessarily transferable • Effectiveness depends on market, policy, cultural environment: –Improved child nutrients depends on infectious disease environment –Increasing textbooks depends on teacher quality –increased mothers’ schooling impact on child schooling depends on labor markets for women (e.g., US vs. rural India) • Therefore not so simple as blindly emulating specific “best practice” policies. University of Pennsylvania Behrman 15

6. Likely considerable gains to collecting good information & undertaking good systematic analysis of policies • Many determinants of outcomes of interest (e.g., maternal health - MDG 5, water quality - MDG 7); some are not easily observable (e.g., innate ability, health, motivation for MDG1-6; soil & water qualities for MDG7). • Therefore associations between some policy & some outcome not likely to reveal policy impact; individuals (or other entities) exposed to policy not likely to be same as those who are not wrt unobserved characteristics. • E.g., for MDG 2 & 3, those who attend school or attend better schools likely to differ from those who do not with respect to ability, motivation and family background. University of Pennsylvania Behrman 16