Key Preliminary Messages from KfW University of Göttingen research project on synergies and interdependencies in MDGs

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1 Key Preliminary Messages from KfW University of Göttingen research project on synergies and interdependencies in s Prepared by Stephan Klasen, Simon Lange, and Marica LoBue. Serious Problems with individual indicators which hampers measuring levels and trends: Apart from data gaps and measurement error in the data for many countris, there are no reliable data for maternal mortality, the FAO data used to measure undernourishment are not valid and reliable, there are biases in the underweight indicator (which biases both levels and trends, see Klasen, 008; Misselhorn 00). Lastly, the revisions of the global absolute poverty numbers ($.5 a day) have generated a lot of uncertainty on poverty levels, and are based on deeply problematic procedures. Serious consideration should be given to replacing the global absolute poverty indicator with an internationally coordinated poverty measure based on national poverty assessments (Klasen, 009).. When assessing progress, one has to recognize that some goals were unrealistic for some countries. and 3 are much harder to reach for those countries where initial levels of education and female/male ratios are low; arguably, it is much harder to reduce poverty, child and maternal mortality by ½, /3 and ¾, respectively, if a country is starting with a much higher absolute level of deprivation in these indicators (Easterly, 009). As a consequence, one should also consider absolute improvements (e.g. absolute reductions in the poverty or under 5 mortality rate) rather than just relative improvements. 3. Drivers of Progress not very clear. In particular, the relationship between income growth and achievement much lower than anticipated (only strong link to income poverty reduction). See some illustrative tables on the links between growth and some achievements are shown in Annex. Correlations of progress are relatively weak (even though correlation of levels relatively high, Bourguignon et al, 008); State fragility is a good predictor of levels, but not of the rate or absolute pace of progress (Harttgen and Klasen, 00). Many country specific factors appear to be decisive for progress, including conflicts, AIDS, governance, and specific health and education initiatives.. Largest burden of deprivations now in middle income countries (Annex ). While 93% of the world s income poor lived in low income countries (using the World Bank classification) in , that share has fallen to 8% in 008 (Sumner, 00); in contrast, 7% of the income poor live in middle income countries, though most are in lower middle income countries. Also, 5% of income poor live in the global player middle income countries ( Schwellenländer, including China, India, South Africa, Mexico, Brazil and Indonesia). See Annex for details. Similar trends exist for other s: 7% of underweight children live in middle income countries (5% in the 6 global players); and 6% of child deaths occur now in middle income countries, 33% of which in global player MICs. Only in education, things are different. Here the burden of out of school children continues to predominate in low income countries. Thus most of the bottom billion (in terms of income poverty, undernutrition, gender gaps, and child mortality) now live in MICs. This is largely driven by graduation of many large countries (including

2 China, India, Indonesia, Bangladesh, Pakistan, etc.) to (lower) middle income status, as measured and decided by the World Bank. But rising income inequality and poor health and education progress in these (and other MICs) also plays a role. While many of the poorer MICs still have little access to private capital flows and will continue to (also) depend on official flows, many other MICs have access to private finance, and generally have the resources to achieve progress. What should the role of aid be in these MICs? Should aid remain engaged, maybe using different modalities and objectives? Or should aid focus on promoting progress in low income countries (now heavily concentrated in Africa). 5. Identifying Under and Overachievers in Progress requires a different method (Annex 3). Instead of simply relying on absolute or relative progress or investigating whether countries are on target paths towards achievements, progress (particularly in the health and education related s) should be assessed by comparing actual improvements with normal improvements in the course of economic development (Clemens, 00). As shown in Annex 3, such typical improvements follow a non linear expansion path that is remarkably similar across countries. Making comparisons with these paths, over and underachievers in progress can be more reliably identified. This is done in detail in annex 3 for undernutrition, education, gender gaps, child mortality, and maternal mortality. A few items are worth noting. First, while on average, many Sub Saharan African countries are underachievers in progress using the comparison with normal progress, there are also some overachievers in that region in all measures so that it appears possible to do better than the normal path in this region as well. When trying to explain the deviations from trends, high growth (and faster income poverty reduction), low gender gaps in 990 and a quick reduction in gender gaps appear to be important drivers of overachievement. Being affected by AIDS slowed improvements and improvements were, on average (and not withstanding positive outliers), lower in Sub Saharan Africa, esp. when it comes to the mortality indicators. 6. Clusters of synergies and trade offs (Annex ). We are currently investigating clusters of synergies and trade offs in achievements. The idea is to identify countries where there are strong synergies in achievements, distinguish them from those where there are few synergies, and also identify countries where progress in one went along with regress in another. Some illustrative figures are shown in Annex, showing initial results of these analyses. Issues that are currently being considered are the role of lags in affecting these synergies, efforts to identify countries that are persistently in poor clusters, and trying to explain the drivers of cluster membership using policy, institutional, and geography variables as well as the role of aid. 7. Some Implications for Summit. Apart from short term measures to try to keep progress towards s on track (involving support to cushion the effects of rising food and fuel prices as well as negative impacts of the financial crisis), it is clear from the discussion that there are no quick fixes to accelerate progress towards the s. While it is possible to further accelerate progress on some s where inputs might be closely related to outputs (e.g. there is further scope to move towards gender parity in school enrolments through a range of simple incentive measures such as female school stipend

3 References: programs, lifting of user fees, conditional cash transfers for girls, etc.), for most s, more fundamental reforms are needed to address the short comings in achievements at present. Among the issues to consider are: a. Realism is important at this stage. Given the nature of some s which were near impossible to reach for some countries from day, the normal rate of progress for type indicators (see Annex 3), and the structural bottlenecks in many countries, it will not be possible to meet the s for many countries; thus, the emphasis should be on trying to accelerate progress, keeping in mind that ultimate achievement at the country is not possible for many countries. b. Should the focus be on under or overachievers? With overachievers, quicker gains may be possible, but this would only help in achieving global goals and will not help underachievers meet their targets. Careful analyses of the determinants of underachievement will be necessary as ultimately success of the project will particularly depend on the ability to help those underachievers improve their rate of progress. c. Much of the problem has shifted to (predominantly lower) middle income countries. Many of them arguably have the resources to meet many of the themselves. This may therefore necessitate different forms of engagement, in terms of a broader engagement beyond aid, different aid modalities, and more targeted support to relevant sectors and the like. 8. Some Implications for s post 05. The discussion above suggests that the present set of s has a range of problems associated with the indicators used, the nature of the goals, the data available to monitor them, among others. A future set of goals might want to focus on fewer targets that are clear, easy to monitor, realistic at the countrylevel, and are outcome based well being indicators that are the outcome of comprehensive development processes. In this vein, maybe an income poverty measure (based on internationally coordinated national poverty lines), an education outcome measure (including test scores or other educational achievement indicators), a health outcome measure (such as child mortality rates), and some environment/sustainability indicator (e.g. water/sanitation access) might suffice. Bourguignon, F., A. Bénassy Quéré, S. Dercon, A. Estache, J.W. Gunning, R. Kanbur, S. Klasen, S. Maxwell, J. P. Platteau, and A. Spadaro (008). Millennium Development Goals at Midpoint: Where Do We Stand and Where Do We Need to Go? Paper written for the DG Development of the European Commission as a background paper for the 009 European Report on Development. Clemens, M. (00). The Long Walk to School: International Education Goals in Historical Perspective. Center for Global Development Working Paper Number 37. Easterly, W. (009). How the Millennium Development Goals are Unfair to Africa. World Development 37(),

4 Harttgen, K and S. Klasen (00): Fragility and Progress: How Useful is the Fragility Concept? EUI Working Papers RSCAS 00/0. Robert Schuman Centre for Advance Studies. Klasen, S. (008). Poverty, Undernutrition, and Child Mortality: Some Inter Regional Puzzles and their Implications for Research and Policy. Journal of Economic Inequality 6, Klasen, S. (009). Levels and Trends in Absolute Poverty in the World: What We Know and What We Don t. In: Mack, E., M. Schramm, S. Klasen, and T. Pogge (eds.) Absolute Poverty and Global Justice. London: Ashgate, 36. Misselhorn, M. (00). Undernutrition and the Nutrition Transition: Revising the Undernutrition Aspect of I. Courant Research Centre: Poverty, Equity and Growth Discussion Paper Number 35. Sumner, A. (00): Has the Global Poverty Problem Changed and if so, What Does It Mean? Or: The New Bottom Billion? 7% of the World s Poor Live in Middle Income Countries. Draft working paper.

5 Annex : Correlations between Income Growth and Achievements (Marica LoBue). Relatively close relationship between growth and absolute poverty reduction Absolute change in poverty headcount ratio, DJI VA TKM Growth of GDP per capita, PPP (constant 005 international $), Relatively weak relationship between growth and other achievements. Absolute change in primary completion rate, DJI Growth of GDP per capita, PPP (constant 005 international $),

6 Proportional change in maternal mortality rate, MNE TKM Growth of GDP per capita, PPP (constant 005 international $), BIH Proportional change in child mortality, SYC MNE TKM CH Growth of GDP per capita, PPP (constant 005 international $), BIH 6

7 absolute change in malnutrition prevalence (weight for age), DJI MNE Growth of GDP per capita, PPP (constant 005 international $),

8 Annex : The Shifting Global Distribution of Failures (Simon Lange) Table : World Bank s analytical classification of 'global players' Country Brazil UM UM UM China L LM LM India L L LM Indonesia L L LM Mexico UM UM UM South Africa UM UM UM Table : Share of poor ($.5 a day definition) by income category Low income Low income (Sumner and Tiwari, 00) Middle income Lower middle income Upper middle income Others Global MICs High income Total

9 Table 3: Share of undernourished children by income category (based on 37 countries) Underweight (weight for age) Low income Middle income Lower middle income Upper middle income Others Global MICs High income Total Stunted (height for age) Low income Middle income Lower middle income Upper middle income Others Global MICs High income Total

10 Table : Children with incomplete primary schooling by income category share of total Low income Middle income Lower middle income Upper middle income Others Global MICs High income Total Table 5: Share of "gap girls" in primary education by income category Low income Middle income Lower middle income Upper middle income Others Global MICs High income Total Note: the term "gap girls" refers to the total number of girls without completed primary schooling for which there is a male counterpart in their country who did complete primary school. 0

11 Table 6: Share of child deaths by income category Low income Middle income Lower middle income Upper middle income Others Global MICs High income Total Notes on failures by income category (Simon Lange) General notes The first table is just a history of global players in table form (World Bank s analytical income classifications). I also tried to extend the analysis to 3 by calculating the number of gap girls. These are girls that did not complete primary school for which there is a boy in their respective country that did complete primary school. I m still not sure whether this makes sense. I reclassified countries that did not exist in 990 or 000 (Ex USSR, Ex Yugoslavia, and Ex Czechoslovakia) using the predecessor s income category. All other countries which were not classified in 990 or 000 are very small in terms of population size. Results For (poverty), my estimates for the low income category are very similar to Sumner s. The share of the poor that moved to the MIC category went entirely to its lower middle income segment. The proportion of poor living in LICs decreased gradually between 990 and 008 with China graduating in the late 90s and India and Indonesia (and others) graduating after 000. The trend differs somewhat by indicator. For example, the share of unschooled children living in LICs increased during the 990s and has sharply decreased since. The same is true for the gap girls. This might reflect sluggish growth in Africa during the 990s. The proportion of undernourished children and child deaths in LICs did not increase during the 990s. There are differences to what extent the problem is now mainly located in MICs. For example, most underweight and stunted children (India) and most child deaths (Nigeria, but

12 also India, Pakistan, and China because of population size) are now found in MICs. The same holds for gap girls (India and Pakistan). However, most children without primary schooling are still found in LICs.

13 Annex 3: Transition paths and some scatter plots (Simon Lange) Clemens (00) Main argumentation:. Raising school enrolment, like economic development in general, takes a long time.. Empirical research shows that for school enrolment rates parental education levels and economic conditions are far more important than education policy interventions. 3. Therefore, there is a remarkable uniformity of experience in the rates of enrollment increases.. and 3, if taken literally, are utopian. They follow a cycle of overambitious goals, foreseeable failure, and an ensuing blame game, in which donor country governments cite lack of political commitment on part of their partners in the developing world and developing country governments blame insufficient aid volumes. 5. Many countries that will not achieve the goals have nevertheless increased enrollment figures extraordinarily rapidly by historical standards. 6. Some countries have done so at the expense of the quality of education. 7. Aid supported education policies can help within limits. 8. Progress should be judged against country specific, historically grounded goals. 9. A country s broader development strategy outside the classroom matters much more than education policy. 3

14 Figure : Burkina Faso s transition path in net primary enrollment compared to historical experiences (Clemens, 00)

15 Undernutrition () Data problems (based on surveys and, thus, very unequally spaced) Weight for age Height for age SOM PRK BRB BIH MNE BIH A PRK SOM BIHBIH BRB MNE Figure : Transition paths for indicators of undernutrition (weight for age: within Rsq = 0., Rsq = 0.99) Africa and Middle East Others DJI ARA Figure 3: Difference between actual childhood undernutrition prevalence (weight for age) and projection, spells between (projection based on first observation and historical transition path) 5

16 Africa and Middle East DJI Others Figure : Difference between actual childhood undernutrition prevalence (height for age) and projection, spells between (projection based on first observation and historical transition path) Deviation (actual vs. trend) - in undernutrition (weight-for-age), ca DJI Deviation (actual vs. trend) - in undernutrition (height-for-age), ca DJI PH Figure 5: Deviations (ca ) in undernutrition indicators against average annual growth in GDP per capita, 005PPP 6

17 7 Primary enrollment () ATG CYM CYM CYM CYM CYM CYM CYM LIE LIE LIE LIE MAC MAC MAC MAC MAC MAC MAC MAC MAC MAC MAC MAC ADO ADO ADO ADO ADO ADO ADO ANT ANT ANT ANT ANT ANT ANT ATG BIH BRB BRB BRB BRB BRB CYM CYM CYM CYM CYM CYM CYM CYM LIE LIE LIE LIE MAC MAC MAC PLW PLW PLW PLW SOM SOM SOM SOM SYC SYC SYC SYC Primary completion rate Ratio of gross primary enrollment rates, girls to boys Figure 6: Transition paths for primary completion rates and gross primary enrollment rates (girls to boys). DJI Deviation (act. - hist. trend) in primary completion rate Average annual growth rate of GDP per capita, 005 PPP, Figure 7: Performance on and GDP per capita growth,

18 DJI South and East Africa Middle East and North Africa West and Central Africa Latin America and Caribbean South Asia and East Asia and Pacific MAC Figure 8: Difference between actual primary completion and projection, 008 (projection based on 990 value and historical transition path) 8

19 Gender gap in schooling (3) The indicator is the ratio of boys gross primary enrollment to girls multiplied with 00 (transition path see above). Deviation (act- - hist. trend) in ratio of enrollment rates Deviation (act. - hist. trend) in primary completion rates Figure 9: Deviation in gender gap against deviation in primary completion,

20 South and East Africa West and Central Africa DJI Middle East and North Africa Latin America and Caribbean South Asia and East Asia and Pacific FJ Figure 0: Difference between actual primary gender gap (ratio of gross enrollment rates, girls to boys) and projection, 008 (projection based on 990 value and historical transition path) 0

21 Child and maternal mortality ( and 5) ADO ADO ADO ADO ADO ADO ADO ATG ATG ATG ATG ATG ATG BIH BIHBIH BIH BIHBIH BIH BIH BIH BRB BRB BRB BRB BRB BRB BRB BRB BRB LIELIELIELIE LIE LIE LIE MCO MCO MCO MCO MCO MCO MCO MNE MNE MNE MNE MNE MNE MNE MNE PLW PLW PLW PLW PLW PLW PLW PLW PLW PRK PRK PRK PRK PRK PRK PRK PRK PRK PRK PRK PRK PRK SMR SMR SMR SMR SMR SMR SMR SMR SOM SOM SOM SOM SOM SOM SOM SOM SYC SYC SYC SYC SYC SYC SYC SYC SYC SYC SYC SYC SYC TKM TKM TKM TKM TKM TKM TKM TKM TKM BIH BIH BIHBIH BRB BRB BRB BRB MNE MNE MNE MNE PRK PRK PRK PRK SOM SOM SOM SOM TKM TKM TKM TKM Under-5 mortality Maternal mortality Figure : Transition paths for child and maternal mortality rate, and , respectively

22 South and East Africa West and Central Africa DJI NA SOM Middle East and North Africa Latin America and Carribean EM South Asia and East Asia and Pacific PRK Figure : Difference between actual under 5 mortality and projection, 008 (projection based on 990 value and historical transition path)

23 South and East Africa West and Central Africa SOM DJI Middle East and North Africa Latin America and Carribean YE South Asia and East Asia and Pacific PRK Figure 3: Difference between actual maternal mortality and projection, 008 (projection based on 990 value and historical transition) 3

24 Transition paths: Is Sub-Saharan Africa different? Sub-Saharan Africa Rest of Developing World Figure : Transition paths (X axis: adjusted years) for primary completion rate SSA vs. Other Developing World

25 3 5 Weight for age Height for age Prim. Compl. Gender gap Child mortality Maternal mortality Time 0.09*** 0.036*** 0.08*** 0.037*** 0.050*** 0.059*** 0.08*** 0.0*** 0.03*** 0.06*** 0.03*** 0.037*** (0.00) (0.00) (0.00) (0.005) (0.00) (0.006) (0.00) (0.003) (0.00) (0.00) (0.00) (0.00) Time*SSA 0.03*** 0.09*** 0.0** 0.0*** 0.05*** 0.09*** (0.006) (0.007) (0.009) (0.005) (0.003) (0.007) Within R squared Implied change after 5 years Starting value Others SSA No. of obs ,5,5,58,58,83, No. of groups Avg. group size Notes: *, **, and *** denote significance at the ten, five, and one percent level, respectively. Robust standard errors in parentheses. All regressions include country fixed effects. 5

26 Notes on Transition paths SSA vs. rest General notes For each indicator, I estimated two regressions. First, I regressed the transformed indicator on years. These are the results that were then used to calculate deviations from the trend (see bar graphs above). Second, I allowed the slope of the function to be different for SSA. One can thus test, whether SSA countries follow a distinct transition path. Results and Discussion The hypothesis that SSA follows the same transition path is always rejected usually at the onepercent level. What s more, SSA is almost always doing worse, the exception being the gender gap in primary school. I also calculated what this would mean for a country starting from the graphs inflection point. For example, starting from a 50 percent primary completion rate (columns 5 and 6), a typical SSA country will increase completion rates by about 3.5 percentage points over the course of 5 years, while other countries will increase their completion rate by more than 0 percentage points on average. 6

27 Regression Analysis: Explaining the Difference Between Actual Indicators and Historical Trends Table : Deviations from trend maternal mortality rates, Dependent variable: Deviation from trend: maternal mortality rate, Constant 0.60*** ** ** 9.080*** (8.3) (90.599) (63.3) (90.3) (7.56) (00.9) GDP per capita (%) 0.5*** ** 7.38 (3.003) (5.76) (.7) (6.67) (.338) (3.53) Poverty Headcount (.75) (.73) (.690) (.70) (.536) (.) Maternal mortaliy *** 0.55*** 0.*** 0.6*** (0.08) (0.6) (0.39) (0.39) Primary completion (3.03) (33.500) Gender gap (0.9) (07.90) Primary completion rate (.367) (.307) Gender gap * (6.9) (5.069) HIV prevalence *** 7.65*** (5.8) (9.8) Sub Saharan Africa 0.33* 3.553*** 36.03*** 7.738* (0.50) (7.605) (75.83) (9.86) (55.305) South Asia (3.7) (65.5) (96.67) (03.5) (35.8) Latin America (78.85) (0.0) (5.78) (8.035) (78.95) Caribbean * (79.685) (8.57) (53.093) (59.86) (86.896) Middle East & North Africa (80.7) (5.537) (73.665) (50.30) (89.30) R squared Adj. R squared Observations Notes: *, **, and *** denote significance at the ten, five, and one percent level, respectively. Robust standard errors in parentheses. 7

28 Dependent variable Deviation from trend: under five mortality rate, Constant 8.5*** ** *** 0.96* ** (.5) (7.89) (0.3) (.655) (56.0) (.6) (77.763) GDP per capita (%) 6.69*** ** **.0 0. (.936) (.66) (.83) (.75) (.379) (.36) (3.3) Poverty Headcount (0.96) (0.07) (0.9) (0.59) (0.09) (0.9) (0.30) Under 5 mortality ** 0.* 0.30* 0.3*** 0.5** (0.076) (0.5) (0.56) (0.09) (0.0) Low height for age (7.7) (0.70) Primary completion (.69) (5.99) Gender gap 7.30* 7.99** (9.85) (.037) Low height for age (0.0) (0.337) Prim. Compl. rate (0.80) (0.35) Gender gap **.93** (0.576) (0.869) HIV prevalence (0.697) (.03) Sub Saharan Africa.389* ***.63*** 9.73*** 6.85*** 63.0*** (8.3) (9.050) (.79) (8.953) (.877) (5.88) South Asia (9.3) (9.050) (8.93) (3.390) (.063) (.35) Latin America (5.686) (5.57) (6.9) (7.5) (6.675) (.) Caribbean (6.75) (6.98) (6.666) (5.3) (7.05) (7.708) M. East & North Africa (6.999) (6.9) (8.8) (7.96) (7.06) (5.9) R squared Adj. R squared Observations Notes: *, **, and *** denote significance at the ten, five, and one percent level, respectively. Robust standard errors in parentheses. 8

29 Annex : Identifying Clusters of Achievements: Some illustrative results (Marica Lo Bue) a) Clusters of improvement in poverty and child mortality Absolute change in child mortality rate, DJI TKM Absolute change of poverty headcount ratio, Absolute change in child mortality rate, Absolute change of poverty headcount ratio, b) Clusters of Improvement Poverty Maternal Mortality 9

30 Absolute change in maternal mortality rate, TKM DJI Absolute change of poverty headcount ratio, Absolute change in maternal mortality rate, Absolute change of poverty headcount ratio, c) Clusters of Improvements Poverty and Education 30

31 Absolute change in primary completion rate, DJI Absolute change of poverty headcount ratio, Absolute change in primary completion rate, Absolute change of poverty headcount ratio, d) Clusters of Improvement Child Mortality and Gender Gap 3

32 3-0 Standardized value for absolute changes in gendergap at school (female to male g Standardized value absolute changes in child mortality,

33 Absolute change in gross primary school enrollment, female to male ratio, DJI Absolute change in child mortality rate, e) Clusters of Improvements Child Mortality Education 33

34 Absolute change in primary completion rate, DJI Absolute change in maternal mortality rate, Absolute change in primary completion rate, Absolute change in maternal mortality rate,