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By Eugenie W. H. Maïga African Center for Economic Transformation (ACET)

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Presentation on theme: "By Eugenie W. H. Maïga African Center for Economic Transformation (ACET)"— Presentation transcript:

1 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 lEnseignement de Base et de lAlphabetisation, 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 mothers education affects child health investigate whether threshold effects exist, that is, whether specific years or levels of mothers education have unusually large impacts on childrens 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 mothers education and child health in developing countries Use of distance to school as IV for mothers education in a study of the impact of mothers education on child health Use of both natural experiment and pathways

11 Conceptual framework Draws on Beckers 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 μ is 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

14 Descriptive Statistics: Child Characteristics VariableObsMean Std. Dev.MinMax Childs age in months2,00327.316.8059 Male2,00752.7%0.5001 HAZ2,003-1.82.05-5.985.8 Stunting2,00347.6%0.5001 Moderate stunting2,00319.4%0.4001 Severe stunting2,00328.3%0.4501 WHZ1,932-0.32.04-4.965 Wasting1,93220.7%0.4001 Moderate wasting1,9329.5%0.2901 Severe wasting1,93211.2%0.3201

15 Descriptive Statistics: Regressors VariableObsMeanStd. Dev.MinMax Zero to 5km of program school2,0076.9%0.2501 Five to 10km of program school2,00710.1%0.3001 Number of years ES was available2,0070.41.0505 Mothers years of education2,0071.43.12017 Mothers age2,00724.33.291429 Fathers years of education2,0072.14.15017 Fathers age2,00435.711.441597 Monthly expenditures (1,000 FCFA)2,0058.212.440225 Urban residence2,00724.5%0.4301 Television2,00720.1%0.4001 Radio2,00774.9%0.4301 Refrigerator2,0075.4%0.2301 Bicycle2,00786.8%0.3401 Moped2,00738.5%0.4901 Car2,0072.4%0.1501 Electricity2,00715.8%0.3701 Cell phone2,00725.0%0.4301 Mothers health knowledge2,0073.71.8008 Mothers bargaining power2,00765.4%0.4801

16 Descriptive Statistics: Histogram of parents education

17 Selected child health outcomes by parents education StuntingWasting Mother schooling No schooling50.7%21.5% Some schooling 36.1%17.5% Fathers schooling No schooling51.2%21.2% Some schooling 38.4%19.2% Total47.6%20.7%

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 mothers education. Add income, mother health knowledge, mothers bargaining power, etc. to the child health equation and estimate it.


21 Table 1: OLS Estimates of Mothers Health Knowledge, Bargaining Power, and Household Expenditures on Mothers Education and other Variables. OLS VARIABLES Per capita expenditures Health knowledgeBargain power Mothers education (log)0.300***0.541***-0.023 (0.031)(0.064)(0.018) Per capita expenditures (log)0.084*-0.022 (0.049)(0.014) Mothers age-0.0010.041***0.018*** (0.007)(0.013)(0.004) Fathers education (log)-0.125***-0.158***-0.028** (0.021)(0.043)(0.013) Fathers age-0.007***-0.001-0.000 (0.002)(0.004)(0.001) Urban residence0.370***0.499***-0.137*** (0.052)(0.117)(0.037) Has a television0.225*-0.064 (0.128)(0.041) Has a radio0.416***-0.016 (0.094)(0.026) Observations1,934 R-squared0.1640.1730.056

22 Table 2: Reduced Form Estimates for Child Height-for-Age (HAZ): Pathways Added Base FEIncome Health Knowledge Bargaining power Community servicesAll pathways VARIABLESHAZ Mothers education (log)0.133**0.0830.123*0.139**0.130*0.079 (0.064)(0.061)(0.064)(0.065) (0.062) Childs age (months)-0.204***-0.203***-0.204*** -0.203*** (0.035) Fathers education-0.008-0.005-0.008-0.006-0.008-0.004 (0.053)(0.054)(0.053) (0.054) Per capita expenditures (log)0.169***0.170*** (0.060)(0.061) Health knowledge0.0170.012 (0.027) Bargaining power0.1430.152 (0.122)(0.119) Within 30 minutes of a health clinic0.0440.035 (0.107)(0.105) Observations2,0001,9982,000 1,998 R-squared0.1010.1040.1010.1020.1010.105 Number of provinces43

23 Table 3: Reduced Form Estimates for Child Weight-for-Height (WHZ): Pathways Added Base FEIncome Health Knowledge Bargaining power Community services All pathways VARIABLESWHZ Mothers education (log)0.164*0.157*0.165*0.156*0.166*0.154* (0.086)(0.087) (0.086)(0.082)(0.087) Age in months0.011*** 0.012***0.011***0.012*** (0.003)(0.004)(0.003) (0.004) Fathers education-0.140**-0.142**-0.140**-0.143**-0.140**-0.145** (0.058)(0.059)(0.057)(0.059)(0.058)(0.059) Per capita expenditures (log)0.0080.004 (0.072)(0.071) Health Knowledge-0.002-0.003 (0.038)(0.037) Bargaining power-0.221**-0.218** (0.097)(0.096) Within 30 minutes of a health clinic-0.021-0.029 (0.111)(0.117) Observations1,9251,9231,925 1,923 R-squared0.020 0.0220.0200.022 Number of provinces43

24 Table 4: First Stage IV Regression for Child Weight-for-Height (WHZ) VARIABLES Mothers education Per capita expenditures Zero to 5km of ES (d1)-0.041-0.0080.068 (0.066)(0.049)(0.092) Five to 10km of ES (d2)0.157**0.163**0.010 (0.076)(0.071)(0.079) Number of years ES was available (ESyears)-0.016-0.0030.007 (0.020)(0.019)(0.027) Interaction d1*ESyears0.349***0.407***-0.168** (0.107)(0.124)(0.066) Interaction d2*ESyears-0.049-0.0200.048 (0.045)(0.042)(0.045) Health knowledge0.072***0.028** (0.010)(0.013) Bargaining power-0.023-0.035 (0.030)(0.044) Within 30 minutes of a health clinic0.031-0.007 (0.035)(0.041) Wealth index0.368***0.320*** (0.027)(0.026) Observations19251923 R-squared0.1170.2990.161

25 Table 5: Second Stage IV Regressions for Child Weight-for-Height (WHZ) Basic modelFull model without incomeFull model with income VARIABLESWHZ Mothers education (log)1.735***1.736***1.287*** (0.579)(0.551)(0.326) Age in months0.016*** 0.012*** (0.004) Fathers education-0.163**-0.157**-0.177** (0.071) (0.070) Health knowledge-0.162***-0.065 (0.062)(0.040) Bargaining power-0.163-0.287** (0.129)(0.126) Within 30 minutes of a health clinic-0.145-0.033 (0.149)(0.130) Per capita expenditures (log)-1.113*** (0.394) Observations1,925 1,923 R-squared-0.244-0.224-0.227 Number of provinces43

26 Threshold effects

27 Summary of results Strong positive coefficient of mothers education on WHZ (IV/FE estimation) Positive but insignificant coefficient of mothers education for HAZ (OLS estimation). Household per capita expenditures are a pathway for impact on HAZ. Counterintuitive sign of fathers 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 mothers 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) VARIABLES Mothers education Per capita expenditures Zero to 5km of ES (d1)-0.045-0.001-0.0060.025 (0.063)(0.059)(0.047)(0.091) Five to 10km of ES (d2)0.155**0.169**0.156**0.023 (0.074) (0.069)(0.076) Number of years ES was available-0.013-0.007-0.0010.001 (0.020)(0.019) (0.027) Interaction d1*ESyears0.326***0.329***0.380***-0.165** (0.106)(0.112)(0.123)(0.067) Interaction d2*ESyears-0.051-0.042-0.0190.049 (0.044) (0.042)(0.045) Health knowledge0.095***0.070***0.030** (0.011)(0.010)(0.013) Bargaining power-0.084**-0.026-0.029 (0.033)(0.030)(0.044) Within 30 minutes of a health clinic0.108***0.030-0.004 (0.037)(0.034)(0.040) Wealth index0.362***0.314*** (0.027)(0.026) Observations2,000 1,998 R-squared0.1160.1770.2960.157 F (test of excluded instruments)3.112.9132.4527.03 P-value of F-test0.0080.0130.000

32 Table A2: Second Stage IV Regressions for Child Height-for-Age (HAZ) Basic modelFull model without incomeFull model with income VARIABLESHAZ Mothers education-0.637-0.739-0.355 (0.514)(0.521)(0.301) Childs age (months)-0.206***-0.207***-0.199*** (0.029) (0.028) Fathers education0.0280.0240.034 (0.061) Urban residence0.726*0.713**0.030 (0.387)(0.331)(0.204) Health knowledge0.0930.020 (0.058)(0.034) Bargaining power0.1300.217** (0.114)(0.106) Within 30 minutes of a health clinic0.1180.030 (0.128)(0.111) Per capita expenditures0.713* (0.364) Observations2,000 1,998 R-squared0.0470.0380.054

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