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Putting Children First: Session 3.1.B Saurabh Sinha & Kalkidan Assefa - Analysing child stunting in Africa [25-Oct-17]

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Putting Children First: Identifying solutions and taking action to tackle poverty and inequality in Africa.
Addis Ababa, Ethiopia, 23-25 October 2017

This three-day international conference aimed to engage policy makers, practitioners and researchers in identifying solutions for fighting child poverty and inequality in Africa, and in inspiring action towards change. The conference offered a platform for bridging divides across sectors, disciplines and policy, practice and research.

Published in: Government & Nonprofit
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Putting Children First: Session 3.1.B Saurabh Sinha & Kalkidan Assefa - Analysing child stunting in Africa [25-Oct-17]

  1. 1. ANALYSING CHILD STUNTING IN AFRICA: Does urbanization make a difference? Presentation by Saurabh Sinha Kalkidan Assefa Employment and Social Protection Social Development Policy Division, UNECA International Conference ‘Putting Children First: Identifying Solutions and Taking Action to Tackle Child Poverty and Inequality in Africa’ 23-25 October 2017, Addis Ababa, Ethiopia
  2. 2. Structure of the presentation • Objective of the research • What does the research do? • Urbanisation trends • Hypotheses testing – methodology • Results • Conclusion 2
  3. 3. Objective of the research • Analyse the linkage of urbanization with nutrition outcomes, focusing on child stunting. • The research recognizes the heterogeneous nature of urbanization in Africa and introduces the average annual rate of change of the urban population as a determining factor for the difference in nutritional outcomes between urban and rural areas. 3
  4. 4. What does the research do? The research tests two hypotheses: • Even though the prevalence of childhood stunting is equally high across countries, urban populations are significantly better off than rural populations as in almost every country in Africa, average living standards in urban areas are superior to those in rural areas regardless of national income levels. • In Africa, the size of urban-rural welfare gaps within countries varies a great deal across countries, with higher gaps in less urbanized countries so that in most countries, urban-rural differentials in child stunting converge with increased urbanization. 4
  5. 5. Urbanization trends in Africa • Africa is the fastest urbanizing region in the world. • Richer countries are more urbanized 5 Average annual rate of growth of urban population (%) Number of countries < 1 0 1 – 2 3 2.1 – 3 5 3.1 – 4 13 4.1 – 5 12 > 5 6 • Though close to 60% of people still live in rural areas, it is estimated that by 2030, more than half of Africa’s population will live and work in urban areas.
  6. 6. Higher propn. of children stunted at lower levels of urbanization Cat. Urban popn. as % of total popn. No. of countries Countries Avg. GNI per capita (2011 PPP $) Propn. of children stuntedResource-rich Non-resource-rich I >60 10 Algeria, Libya (NA); Djibouti (EA); Congo, Gabon (CA); South Africa (SA) Morocco, Tunisia (NA); Cabo Verde (WA); Sao Tome and Principe (CA) 9,201 26.3 (4) II 51-60 7 Mauritania (NA); Cote d’Ivoire; Ghana (WA); Cameroon (CA); Botswana (SA) Seychelles (EA); Gambia (WA) 7,834 28.9 (5) III 41-50 10 DR Congo (EA); Benin, Liberia, Nigeria (WA); Angola, Namibia, Zambia (SA) Egypt (NA); Guinea-Bissau, Senegal (WA) 4,263 33.1 (9) IV 31-40 13 Sudan (NA); Madagascar, Tanzania (EA); Guinea, Mali, Sierra Leone, Togo (WA); CAR, Eq. Guinea (CA); Mozambique, Zimbabwe (SA) Somalia (EA); Mauritius (SA) 4,590 35.8 (9) V <30 14 Eritrea, Rwanda, South Sudan, (EA); Burkina Faso, Niger (WA); Chad (CA); Lesotho (SA) Burundi, Comoros, Ethiopia, Kenya, Uganda (EA); Malawi, Swaziland (SA) 1,937 41.9 (13) 54 36 18 5031 6
  7. 7. Africa is urbanizing at a lower income level 7
  8. 8. 0 10 20 30 40 50 60 10 20 30 40 50 60 70 80 90 Stunting(%ofchildrenunder5) % of urban population LOWER LEVELS OF CHILD STUNTING Source: UNDP (2015), Human Development Report; ECA (2015), Demographic Profile of Africa Countries with large urban populations exhibit improved social outcomes… 8
  9. 9. BUT, child stunting in lowest quintile has either increased, stagnated, or declined only slowly Sub-region Country Year Q1 Q5 Q1/Q5 National Average Central Africa Cameroon 1991 39.1 14.8 2.6 31.3 2011 48.6 12.3 4 32.5 Chad 1996-97 50.9 35.5 1.4 44.5 2014-15 41.2 31.5 1.3 39.9 Congo 2005 37.4 24.8 1.5 30.8 2011-12 34.5 9.3 3.7 24.4 Gabon 2000 41 14 2.9 25.1 2012 29.9 5.8 5.2 16.5 East Africa DRC 2007 46.6 25.8 1.8 45.5 2013-14 49.7 22.9 2.2 42.7 Eritrea 1995 53.9 28.8 1.9 44.4 2002 50.1 21 2.4 42.9 Ethiopia 2000 60.6 48.5 1.2 57.7 2016 44.6 25.6 1.7 38.4 Kenya 1993 47.8 23.3 2.1 39.9 2014 30.2 13.8 2.6 26 9
  10. 10. Sub-region Country Year Q1 Q5 Q1/Q5 National Average Southern Africa Lesotho 2009 45.6 28.3 1.6 39.2 2014 45.6 13.4 3.4 33.2 Malawi 1992 60.6 42.6 1.4 55.2 2015 45.7 24.3 1.9 37.1 Namibia 1992 44.9 22.2 2.0 34.8 2013 31.3 8.7 3.6 23.7 Zimbabwe 1994 31.6 19.4 1.6 28.6 2015 33 16.6 2.0 26.8 West Africa Burkina Faso 1993 43 27 1.6 38.8 2010 41.9 18.6 2.3 34.6 Guinea 1999 36.9 18 2.1 30.5 2012 35.2 15.2 2.3 31.2 Liberia 2007 44.5 26.4 1.7 39.4 2013 35.3 19.9 1.8 31.6 Nigeria 1990 53.7 36.7 1.5 48.7 2013 53.8 18 3.0 36.8 10 BUT, child stunting in in lowest quintile has either increased, stagnated, or declined only slowly…
  11. 11. North Africa* bucks the trend: Decline in child stunting is pro-poor Country Year Q1 Q5 Q1/Q5 National Average Egypt 1995 41.7 23.6 1.8 33.7 2014 24.1 23.4 1.0 21.4 Morocco 1992 44.3 12.6 3.5 29.9 2003-04 35 12.5 2.8 22.4 11 * Comparable DHS data available only for 2 countries
  12. 12. SIMILARLY, child stunting in rural areas has either increased, stagnated, or declined only slowly Sub-region Country Year Rural Urban Rural:urban ratio National Average Central Africa Cameroon 1991 37.1 22.7 1.63 31.3 2011 40.5 21.9 1.85 32.5 Chad 1996-97 46.6 36.5 1.28 44.5 2014-15 46.8 35.4 1.32 39.9 Congo 2005 34.2 26.7 1.28 30.8 2011-12 30.4 20.3 1.5 24.4 Gabon 2000 36.2 20.5 1.77 25.1 2012 28.5 14.1 2.02 16.5 East Africa Burundi 1987 53.2 32.5 1.64 52.5 2010 59.5 37.8 1.57 57.7 DRC 2007 51.5 36.7 1.40 45.5 2013-14 47.1 32.5 1.45 42.7 Eritrea 1995 47 34.5 1.36 44.4 2002 48.6 31.7 1.53 42.9 Ethiopia 2000 58.9 47.6 1.24 57.7 2016 39.9 25.4 1.57 38.4 12
  13. 13. Sub-region Country Year Rural Urban Rural:Urban ratio National Average Southern Africa Malawi 1992 56.9 40.7 1.40 55.2 2015-16 38.9 25 1.56 37.1 Mozambique 1997 45.3 33.8 1.34 42.4 2003 51.7 35.6 1.45 42.6 Zambia 1992 52.8 39.2 1.35 46.4 2013-14 42.1 36 1.17 40.1 Zimbabwe 1994 30.2 24.1 1.25 28.6 2015 28.5 22.1 1.29 26.8 West Africa Benin 1996 34 29.1 1.17 32.5 2011-12 46.1 42.3 1.09 44.6 Burkina Faso 1993 41.5 24.2 1.71 38.8 2010 37.3 21.3 1.75 34.6 Mali 1987 30.6 22.3 1.37 27.8 2012-13 41.9 23.2 1.81 38.3 Niger 1992 48 32.8 1.46 45.2 2012 45.9 29.6 1.55 43.9 13 SIMILARLY, child stunting in rural areas has either increased, stagnated, or declined only slowly…
  14. 14. Again, North Africa* bucks the trend: Egypt the only country where urban stunting higher than rural stunting Country Year Rural Urban Rural:Urban ratio National Average Egypt 19 39.5 28.8 1.37 34.8 2014 20.7 23 0.90 21.4 Morocco 1992 36.2 23.3 1.55 29.9 2003-04 28.8 16.2 1.78 22.4 14 * Comparable DHS data available only for 2 countries
  15. 15. KEY OBSERVATIONS, SO FAR • Five child stunting hotspots • Burundi • Benin • Cameroon • Mali • Mozambique • Nutrition policy can be pro-poor, so that the lower quintiles (and rural areas) reduce stunting independent of the top quintiles (and urban areas) – Egypt; Morocco • Lessons can be learnt from Ethiopia, Kenya, Liberia, Malawi, Namibia, Tanzania, Uganda which have achieved appreciable and uniform declines in child stunting, but also from Burkina Faso, Niger, Rwanda, Zambia where progress has been uneven. 15
  16. 16. HYPOTHESES TESTING 1. An independent t-test used to determine whether the mean prevalence rate of two samples are significantly different from each other  Mean significance test between the Rural and Urban stunting prevalence rate  Mean significance test between the Lowest quintile and Highest quintile in stunting prevalence rate Hypothesis testing • Ho: Rural-Urban = 0 • Ha: (Rural-Urban) > 0 or Rural-Urban < 0 2. Pearson correlation coefficient (r) to test the relationship between average annual rate of change of urban population (the urban popn. growth rate) and the Rural-Urban stunting differential (“stunting gap”)  magnitude of the Pearson correlation coefficient determines the strength of the correlation Coefficient Value Strength of Association 0.1 < | r | < .3 small correlation 0.3 < | r | < .5 medium/moderate correlation | r | > .5 large/strong correlation
  17. 17. Result: Normality test • Assessment of the normality of the pooled sample data  Distribution of Rural and Urban stunting prevalence: 17 • Based on the available data and normality test, we can conclude that the distribution of both samples is normal
  18. 18. • The result using both paired and unpaired sample t-test indicates a significant difference between rural and urban child stunting prevalence rate of 12.2 percentage points (significant at 99%) Results: Rural-Urban difference (mean significance test) Variable Obs. Mean Std. Err. Std. Dev. [95% Conf. Interval] Rural stunting Prev. 136 40.68824 .8004105 9.33431 39.10527 42.2712 Urban Stunting Prev. 136 28.47721 .7153849 8.34275 27.06239 29.89202 combined 272 34.58272 .6516131 10.74668 33.29985 35.86559 diff 12.21103 1.073514 10.09751 14.32455 diff = mean (Rural Stunting Prevalence) - mean (Urban stunting Prevalence) t = 11.3748 Ho: mean(diff) = 0 degrees of freedom = 270 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000
  19. 19. Result-normality test • Assessment of the normality of the pooled sample data  Distribution of Lowest Quintile and Highest Quintile in stunting prevalence: • Based on the available data and normality test, we can conclude that the distribution of both samples is also normal
  20. 20. • The result using both paired and unpaired sample t-test indicates a significant difference between child stunting prevalence rate in the lowest quintile (Q1) and the highest quintile (Q5) of 20 percentage points (significant at 99%). Results: Lowest and Highest Wealth Quintile (mean significance test) Variable Obs. Mean Std. Err. Std. Dev. [95% Conf. Interval] lowest 119 44.43025 .8568113 9.346707 42.73353 46.12697 highest 119 24.27395 .8669422 9.457223 22.55717 25.99073 combined 238 34.3521 .8935452 13.78494 32.5918 36.11241 diff 20.1563 1.218899 17.75499 22.55761 diff = mean(lowest) - mean(highest) t = 16.5365 Ho: mean(diff) = 0 degrees of freedom = 236 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000
  21. 21. • Between average annual rate of change of urban population (urban growth rate) and Rural-Urban differential in stunting prevalence • Based on the our data, the Pearson correlation coefficient (r) = 0.4517 • Coefficient of determination (r2) = 0.20, or 20% is explained by the model • The level of statistical significance (p-value) of the correlation coefficient is 0.0 (99% significance) – suggests a strong and significant relationship between the two variables: urban growth rate and Rural-Urban stunting difference. • That is, as urban population growth rate increases, the rural-urban stunting differential (“stunting gap”) also increases. Results: Correlation analysis
  22. 22. Conclusions Important to undertake a more dynamic analysis – ‘annual rate of growth of urban population’ better predictor than ‘extent of urbanization’. As Africa urbanises rapidly, child stunting declining faster in urban areas and among upper quintiles. As the rural-urban differential (“stunting gap”) increases with increasing rate of growth of urban population, a large proportion of children in rural areas from lower income quintiles likely to be left behind. Urgent need to extend universal access to basic services to address child stunting in rural areas. Esp. focus on the stunting hotspots. More rigorous research required, esp. to better analyse the differentials among socioeconomic groups within urban areas – whether the poorest urban quintiles are better or worse off compared to the rural mean. 22
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