The Impact of Mother’s Education on Child Health in Developing Countries: Evidence from a Natural Experiment in Burkina

1. The Impact of Mother’s Education on Child Health in Developing Countries: Evidence from a Natural Experiment in Burkina Faso By Eugenie W. H. Maïga African Center for Economic Transformation (ACET)

2. Background on Burkina Faso • Land area = 274,000 km2, Population =15.21 million (2008), GNI per capita =$480 (2008). • In Burkina Faso, the prevalence rate for stunting (height-for-age) was 39% in 2003. • In 2009 the gross school enrolment rate for primary, secondary, and higher education were 78%, 20%, and 3%, respectively.

3. Gross enrollment rates in primary school by province in 2004-2005 Source: Ministere de l’Enseignement de Base et de l’Alphabetisation, 2005

4. Research problem • Child health and nutrition outcomes in developing countries are disappointing (prevalence of stunting in preschool children was 33% in 2000, De Onis et. al., rates between 30% and 39% are considered high). • Link between child health and child nutrition: malnourished children are more likely to develop illnesses that can have long lasting effects throughout their lives. • Mothers play an important role in child nutrition. How does her education come into play?

5. Why are child health outcomes important? • Costs of related illnesses (physical suffering, time costs and monetary costs). • Impacts on ability to learn i.e. on schooling outcomes. • Impacts on productivity later in life which could adversely affect economic growth

6. Objectives • General objective: • Examine the relationship between maternal education and child health in Burkina Faso • Specific objectives: • Verify whether a strong causal relationship exists • Understand the channels through which mother’s education affects child health • investigate whether threshold effects exist, that is, whether specific years or levels of mothers’ education have unusually large impacts on children’s health.

7. Change in education policy in Burkina Faso • Primary education: construction of 3-year primary schools Ecoles Satellites, ES) in areas of 28 provinces (out of 45) where enrolment rates are low. As of 2008, there were 304 ES. • Became effective in the school year 1995-1996. Instruction is done in two languages, French and language spoken in the area. • Trying to favor girls enrollment in those schools

8. Evolution of enrollment rates in provinces with and without the program Provinces with program Provinces without program

9. Previous Evidence • Empirical studies without pathways (did not use natural experiment): Baya (1998), Appoh and Krekling(2005). • Empirical studies without pathways (used natural experiment): Breierova and Duflo (2004) and Chou et. al. (2007). • Empirical studies with pathway(s): Thomas et al. (1991), Glewwe (1999), Handa (1999), and Webb and Block (2004).

10. Contribution • Many have studied the subject but few studies have used natural experiments (change in policy) in identifying the relationship between mother’s education and child health in developing countries • Use of distance to school as IV for mother’s education in a study of the impact of mother’s education on child health • Use of both natural experiment and pathways

11. Conceptual framework Draws on Becker’s model of the family, and Rosenzweig and Schultz (1983) health production function Consider the following equations: Where X is market goods, Z represents goods that affect child health, H is child health, I represents inputs that affect utility only through their effect on H and μis child health endowment.

12. Conceptual framework • Estimating H requires detailed data on health inputs, data which are not available to this study. A way out of that, is to use reduced form equations. • Maximizing U subject to the health production and the budget constraint yields reduced-form demand functions for X, Z, H and I.

13. Data • Main source: Burkina Faso 2007 Enquete sur les conditions de vie des menages (survey of living standards). • Additional data sources include the ministry of primary education (policy change data: number, location, and year of construction of the schools, and enrollment information) and Internet sources (distance calculator). • Distance between the closest school (village/city name) and the household village/city of residence computed using distance calculator at www.infoplease.com/atlas/calculate-distance.html

14. Descriptive Statistics: Child Characteristics

15. Descriptive Statistics: Regressors

16. Descriptive Statistics: Histogram of parents education

17. Selected child health outcomes by parents’ education

18. Methods and Procedures • Parents’ education and child health maybe affected by many ‘unobservables’. To identify the effect parents education on child health, I will use a system of equations:

19. Methods and procedures • Use two-stage –least –squares to estimate the system (education equation and child health equation) with the primary education program as an instrument for mother’s education. • Add income, mother health knowledge, mother’s bargaining power, etc. to the child health equation and estimate it.

20. RESULTS

21. Table 1:OLS Estimates of Mother’s Health Knowledge, Bargaining Power, and Household Expenditures on Mother’s Education and other Variables.

22. Table 2: Reduced Form Estimates for Child Height-for-Age (HAZ): Pathways Added

23. Table 3: Reduced Form Estimates for Child Weight-for-Height (WHZ): Pathways Added

24. Table 4: First Stage IV Regression for Child Weight-for-Height (WHZ)

25. Table 5: Second Stage IV Regressions for Child Weight-for-Height (WHZ)

26. Threshold effects

27. Summary of results • Strong positive coefficient of mother’s education on WHZ (IV/FE estimation) • Positive but insignificant coefficient of mother’s education for HAZ (OLS estimation). • Household per capita expenditures are a pathway for impact on HAZ. Counterintuitive sign of father’s education, bargaining power and household expenditures in WHZ regression.

28. Summary of results • Evidence of existence of threshold effects but need to consider the cost. • Keep girls in school as long as possible; design nutrition programs targeted at girls/women. • Limitations: could not IV mother’s health knowledge, quality of bargaining power and health knowledge variables, no information on religion and ethnicity.

29. Questions?

30. Thank you!

31. Table A1: First Stage IV Regressions for Child Height-for-Age (HAZ)

32. Table A2: Second Stage IV Regressions for Child Height-for-Age (HAZ)