By Eugenie W. H. Maïga African Center for Economic Transformation (ACET)
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Transcript of By Eugenie W. H. Maïga African Center for Economic Transformation (ACET)
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)
Background on Burkina FasoLand 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.
Gross enrollment rates in primary school by province in 2004-2005
Source: Ministere de l’Enseignement de Base et de l’Alphabetisation, 2005
Research problemChild 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?
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
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.
Change in education policy in Burkina FasoPrimary 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
Evolution of enrollment rates in provinces with and without the program
Provinces with program
Provinces without program
Previous EvidenceEmpirical 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).
ContributionMany 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
Conceptual frameworkDraws 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 μ is child health endowment.
IZX
IZ
X
321
),,(
),(
pppM
hH
HuU
Conceptual frameworkEstimating 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.
DataMain 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
Descriptive Statistics: Child Characteristics
Variable Obs MeanStd. Dev. Min Max
Child’s age in months 2,003 27.3 16.8 0 59
Male 2,007 52.7% 0.50 0 1
HAZ 2,003 -1.8 2.05 -5.98 5.8
Stunting 2,003 47.6% 0.50 0 1
Moderate stunting 2,003 19.4% 0.40 0 1
Severe stunting 2,003 28.3% 0.45 0 1
WHZ 1,932 -0.3 2.04 -4.96 5
Wasting 1,932 20.7% 0.40 0 1
Moderate wasting 1,932 9.5% 0.29 0 1
Severe wasting 1,932 11.2% 0.32 0 1
Descriptive Statistics: Regressors
Variable Obs Mean Std. Dev. Min MaxZero to 5km of program school 2,007 6.9% 0.25 0 1Five to 10km of program school 2,007 10.1% 0.30 0 1Number of years ES was available 2,007 0.4 1.05 0 5Mother’s years of education 2,007 1.4 3.12 0 17Mother’s age 2,007 24.3 3.29 14 29Father’s years of education 2,007 2.1 4.15 0 17Father’s age 2,004 35.7 11.44 15 97Monthly expenditures (1,000 FCFA) 2,005 8.2 12.44 0 225Urban residence 2,007 24.5% 0.43 0 1Television 2,007 20.1% 0.40 0 1Radio 2,007 74.9% 0.43 0 1Refrigerator 2,007 5.4% 0.23 0 1Bicycle 2,007 86.8% 0.34 0 1Moped 2,007 38.5% 0.49 0 1Car 2,007 2.4% 0.15 0 1Electricity 2,007 15.8% 0.37 0 1Cell phone 2,007 25.0% 0.43 0 1Mother’s health knowledge 2,007 3.7 1.80 0 8Mother’s bargaining power 2,007 65.4% 0.48 0 1
Descriptive Statistics: Histogram of parents education
0.2
.4.6
.8Fr
actio
n
0 5 10 15 20momedu_years
0.2
.4.6
.8Fr
actio
n
0 5 10 15 20dadedu_years
Selected child health outcomes by parents’ education
Stunting Wasting
Mother schoolingNo schooling 50.7% 21.5%
Some schooling
36.1% 17.5%
Father’s schoolingNo schooling 51.2% 21.2%
Some schooling
38.4% 19.2%
Total 47.6% 20.7%
Methods and ProceduresParents’ 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:
iiki
iki
SH
S
X
XZ
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.
RESULTS
Table 1: OLS Estimates of Mother’s Health Knowledge, Bargaining Power, and Household Expenditures on Mother’s Education and other Variables.
OLS OLS OLS
VARIABLESPer capita
expendituresHealth
knowledge Bargain power Mother’s 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)Mother’s age -0.001 0.041*** 0.018***
(0.007) (0.013) (0.004)Father’s education (log) -0.125*** -0.158*** -0.028**
(0.021) (0.043) (0.013)Father’s age -0.007*** -0.001 -0.000
(0.002) (0.004) (0.001)Urban residence 0.370*** 0.499*** -0.137***
(0.052) (0.117) (0.037)Has a television 0.225* -0.064
(0.128) (0.041)Has a radio 0.416*** -0.016
(0.094) (0.026)
Observations 1,934 1,934 1,934R-squared 0.164 0.173 0.056
Table 2: Reduced Form Estimates for Child Height-for-Age (HAZ): Pathways Added
Base FE IncomeHealth
KnowledgeBargaining
powerCommunity
services All pathwaysVARIABLES HAZ HAZ HAZ HAZ HAZ HAZ Mother’s education (log) 0.133** 0.083 0.123* 0.139** 0.130* 0.079
(0.064) (0.061) (0.064) (0.065) (0.065) (0.062)Child’s age (months) -0.204*** -0.203*** -0.204*** -0.204*** -0.203*** -0.203***
(0.035) (0.035) (0.035) (0.035) (0.035) (0.035)Father’s education -0.008 -0.005 -0.008 -0.006 -0.008 -0.004
(0.053) (0.054) (0.053) (0.053) (0.054) (0.054)Per capita expenditures (log) 0.169*** 0.170***
(0.060) (0.061)Health knowledge 0.017 0.012
(0.027) (0.027)Bargaining power 0.143 0.152
(0.122) (0.119)Within 30 minutes of a health clinic 0.044 0.035
(0.107) (0.105)
Observations 2,000 1,998 2,000 2,000 2,000 1,998R-squared 0.101 0.104 0.101 0.102 0.101 0.105
Number of provinces 43 43 43 43 43 43
Table 3: Reduced Form Estimates for Child Weight-for-Height (WHZ): Pathways Added
Base FE IncomeHealth
KnowledgeBargaining
powerCommunity
servicesAll
pathwaysVARIABLES WHZ WHZ WHZ WHZ WHZ WHZ Mother’s education (log) 0.164* 0.157* 0.165* 0.156* 0.166* 0.154*
(0.086) (0.087) (0.087) (0.086) (0.082) (0.087)Age in months 0.011*** 0.011*** 0.011*** 0.012*** 0.011*** 0.012***
(0.003) (0.004) (0.003) (0.003) (0.003) (0.004)Father’s 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) 0.008 0.004
(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)
Observations 1,925 1,923 1,925 1,925 1,925 1,923R-squared 0.020 0.020 0.020 0.022 0.020 0.022
Number of provinces 43 43 43 43 43 43
Table 4: First Stage IV Regression for Child Weight-for-Height (WHZ)
VARIABLESMother’s education
Mother’s education
Per capita expenditures
Zero to 5km of ES (d1) -0.041 -0.008 0.068(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 -0.003 0.007(0.020) (0.019) (0.027)
Interaction d1*ESyears 0.349*** 0.407*** -0.168**(0.107) (0.124) (0.066)
Interaction d2*ESyears -0.049 -0.020 0.048(0.045) (0.042) (0.045)
Health knowledge 0.072*** 0.028**(0.010) (0.013)
Bargaining power -0.023 -0.035(0.030) (0.044)
Within 30 minutes of a health clinic 0.031 -0.007(0.035) (0.041)
Wealth index 0.368*** 0.320***(0.027) (0.026)
Observations 1925 1923 1923R-squared 0.117 0.299 0.161
Table 5: Second Stage IV Regressions for Child Weight-for-Height (WHZ)
Basic model Full model without income Full model with incomeVARIABLES WHZ WHZ WHZ Mother’s education (log) 1.735*** 1.736*** 1.287***
(0.579) (0.551) (0.326)Age in months 0.016*** 0.016*** 0.012***
(0.004) (0.004) (0.004)Father’s education -0.163** -0.157** -0.177**
(0.071) (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)
Observations 1,925 1,925 1,923R-squared -0.244 -0.224 -0.227Number of provinces 43 43 43
Threshold effects
-2-1
01
23
Para
met
er e
stim
ate
0 _Imomedu_ye_5 _Imomedu_ye_10 log_pceMother's years of education
WHZ threshold regression coefficients
-2-1
01
2Pa
ram
eter
est
imat
e
0 _Imomedu_ye_5 _Imomedu_ye_10 log_pceMother's years of education
HAZ threshold regression coefficients
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.
Summary of resultsEvidence 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.
Questions?
Thank you!
Table A1: First Stage IV Regressions for Child Height-for-Age (HAZ)
VARIABLESMother’s education
Mother’s education
Mother’s education
Per capita expenditures
Zero to 5km of ES (d1) -0.045 -0.001 -0.006 0.025(0.063) (0.059) (0.047) (0.091)
Five to 10km of ES (d2) 0.155** 0.169** 0.156** 0.023(0.074) (0.074) (0.069) (0.076)
Number of years ES was available -0.013 -0.007 -0.001 0.001(0.020) (0.019) (0.019) (0.027)
Interaction d1*ESyears 0.326*** 0.329*** 0.380*** -0.165**(0.106) (0.112) (0.123) (0.067)
Interaction d2*ESyears -0.051 -0.042 -0.019 0.049(0.044) (0.044) (0.042) (0.045)
Health knowledge 0.095*** 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 clinic 0.108*** 0.030 -0.004(0.037) (0.034) (0.040)
Wealth index 0.362*** 0.314***(0.027) (0.026)
Observations 2,000 2,000 1,998 1,998R-squared 0.116 0.177 0.296 0.157F (test of excluded instruments) 3.11 2.91 32.45 27.03P-value of F-test 0.008 0.013 0.000 0.000
Table A2: Second Stage IV Regressions for Child Height-for-Age (HAZ)
Basic model Full model without income Full model with incomeVARIABLES HAZ HAZ HAZ
Mother’s education -0.637 -0.739 -0.355(0.514) (0.521) (0.301)
Child’s age (months) -0.206*** -0.207*** -0.199***(0.029) (0.029) (0.028)
Father’s education 0.028 0.024 0.034(0.061) (0.061) (0.061)
Urban residence 0.726* 0.713** 0.030(0.387) (0.331) (0.204)
Health knowledge 0.093 0.020(0.058) (0.034)
Bargaining power 0.130 0.217**(0.114) (0.106)
Within 30 minutes of a health clinic 0.118 0.030
(0.128) (0.111)
Per capita expenditures 0.713*(0.364)
Observations 2,000 2,000 1,998R-squared 0.047 0.038 0.054