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

Slides:



Advertisements
Similar presentations
1 Radio Maria World. 2 Postazioni Transmitter locations.
Advertisements

- Work in Progress - Inventor mobility and regions' innovation potential Riccardo Cappelli, U Insubria Dirk Czarnitzki, K.U.Leuven and ZEW Mannheim Thorsten.
Project QC Consultation on Proposed Reliability Standards and Supporting Documents Information Session with Entities Concerned January 11 and 16,
What Drives Taxi Drivers? Definition an expert seller knows more about the quality a consumer needs than the consumer herself Examples repair services.
Evaluation of agricultural systems across time and spatial scales. An extended LCA approach. Amalia Zucaro, Silvio Viglia and Sergio Ulgiati.
Purpose and Hypothesis Sample Characteristics (N=165) Results Implications Discussion Acknowledgment BIOPSYCHOSOCIAL PREDICTORS OF OBESITY IN BIOPSYCHOSOCIAL.
Childhood and adolescent precursors of adult labour force attachment: Influences of neighbourhood characteristics Piotr Wilk Psychiatry and Bechavioural.
Self-Perceived Health in Early Adulthood: An examination of distal, childhood effects John Cairney, PhD Centre for Addiction and Mental Health Centre for.
/ /17 32/ / /
Reflection nurulquran.com.
Worksheets.
Acid – Base Titration.
Workshop Non-technical innovation The new service economy and new horizons in a global world Luis Rubalcaba Professor of Economic Policy, University of.
Labour quality and ICT capital skill complementarity: International comparisons Mary OMahony NIESR.
THE 2004 LIVING CONDITIONS MONITORING SURVEY : ZAMBIA EXTENT TO WHICH GENDER WAS INCORPORATED presented at the Global Forum on Gender Statistics, Accra.
Almudena Fernandez Luis Felipe Lopez-Calva UNDP Regional Bureau for Latin America and the Caribbean London, 9 November 2009 Transitory Shocks, Permanent.
Input-Output Analysis in Current or Constant Prices: Does it Matter? Erik Dietzenbacher & Umed Temurshoev Faculty of Economic and Business University of.
Plan.be Does Offshoring of Materials and Business Services Affect Employment? Evidence for a small open economy Bernhard Michel (FPB.
The Effect of Disability on Personal Quality of Primary Care Received by Older Adults Chun-Ju Hsiao, PhD, MHS AcademyHealth Disability Research Interest.
Albert Park, University of Michigan Sangui Wang, Chinese Academy of Agricultural Sciences Community-based Development and Poverty Alleviation An Evaluation.
Behavioral health disorders are common.
Heckle and Chide: Empowering matatu passengers to enforce better driving behavior in Kenya James Habyarimana Georgetown University and William Jack Georgetown.
1 Nikolai Kiselev and Vera Rosenbush Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv Calibration Workshop Zurich, Switzerland,
The latest products and tools provided by JMA Kenji KISHIMOTO National Typhoon Center JMA.
Summative Math Test Algebra (28%) Geometry (29%)
Widening Participation in Higher Education: Analysis using Linked Admin Data Institute of Education Institute for Fiscal Studies Centre for Economic Performance.
Contextual effects In the previous sections we found that when regressing pupil attainment on pupil prior ability schools vary in both intercept and slope.
Inequality, Poverty and Globalisation in OIC Countries: A Dynamic Comparative Analysis Muhammad Tariq Majeed PhD Student University of Glasgow, UK
Growth, Trade Openness and Remittances: Lessons for Developing Countries Muhammad Tariq Majeed, PhD Student University of Glasgow, UK.
Lecture 11 Macroeconomic Analysis 2003 Approaches to Macroeconomics.
Center for Education Research and Teacher Training 1/ Claudia Schuchart: Motives for Primary School Choice. Bristol, 10th Juny 2009 Motives for Primary.
4/21/2014 The impact of increased conditionality for out-of-work lone parents Evidence from the UK Labour Force Survey UNCLASSIFIED.
HRM policies & worker/company performance 1. HRM policies 1. HRM policies 2. The effect on worker performance 2. The effect on worker performance Job satisfaction.
Non-monetary rewards & compensating differentials
1 Table design Module 3 Session 2. 2 Objectives of this session By the end of this session, you will be able to: appreciate the different type of objectives.
Investigating Market Power: a New Application Catherine Ball CLEEN New Researchers Workshop 2008 ESRC Centre for Competition Policy,
Perry Points of Pride... Creating the New Perry Way Attractive 1. Become Attractive To All Community 2. Build Community Spirit Identity 3. Establish Our.
Projecting the Effect of Changes in Smoking and Obesity on Future Life Expectancy in the United States Samuel H. Preston University of Pennsylvania Retirement.
The Returns to Academic and Vocational Qualifications Steven McIntosh Department of Economics University of Sheffield.
Does Science Promote Women? Evidence from Academia: for presentation at: Science and Engineering Workforce Project at NBER October 20, 2005 Donna.
Peter Burton and Shelley Phipps Department of Economics Dalhousie University.
Thorsten Beck Chen Lin Yue Ma Why Do Firms Evade Taxes? The Role of Information Sharing and Financial Sector Outreach The Journal of Finance.
1 BRIEFING ON METHODOLOGY FOR PREPARING THE SCALE OF ASSESSMENTS.
1 Searching in a Graph Jeff Edmonds York University COSC 3101 Lecture 5 Generic Search Breadth First Search Dijkstra's Shortest Paths Algorithm Depth First.
BODY IMAGE DISSATISFACTION AND SOCIOCULTURAL INFLUENCES AMONG ADOLESCENTS IN SUBANG JAYA, SELANGOR Shobhna P. & Rosnah S.
CHAPTER 19 An Introduction to Data Structures. CHAPTER GOALS To learn how to use linked lists provided in the standard library To be able to use iterators.
The Role of Infrastructure in Reducing Chronic and Transient Poverty The Case of JBIC Supported Irrigation Project in Sri Lanka Yasuyuki Sawada, Masahiro.
Rabah Arezki Thorvaldur Gylfason Forthcoming in Economic Growth and Development, Frontiers of Economics and Globalization, vol. 11, 2011.
1 Gifts for all occasions… 1. 2 Derived from Sanskrit, meaning "to present" or "to offer" in dedication. Bangalore based Corporate gifts company S PIRITUAL.
The Frequency Table or Frequency Distribution Table
Lamps Lamps. LAM_018LAM_018 LampsLamps Brazilian Samba 50 cm 26 cm 172 USD.
Time Series and Neural Networks Comparison on Gold, Oil, and the Euro A.G. Malliaris, Mary Malliaris Loyola University Chicago School of Business Administration.
Trade and Education Divergence: A gender Perspective Ramya Vijaya Richard Stockton College
Christopher, T. Whelan*, Brian Nolan** and Bertrand Maître*** *School of Sociology and Geary Institute, University College Dublin & School of Sociology.
1 Development of a Valid Model of Input Data Collection of raw data Identify underlying statistical distribution Estimate parameters Test for goodness.
Following lives from birth and through the adult years Kirstine Hansen and Denise Hawkes Centre for Longitudinal Studies Institute of.
Making your point: debating Voting All school assemblies must be delivered as a rap. NOYES.
ROBERT MORGAN EDUCATIONAL CENTER NORTH CAMPUS 1ST FLOOR
NRM Wherry Road, Norwich, NR1 1WS This permit is not valid unless NRM seal is applied PARKING PERMIT Bay No. PY EXPIRES.
Stephen Speed Inspector General and Agency Chief Executive SCS PB2 - £95,000 - £99,999 FTE: 1.0 Les Cramp Deputy Inspector General of Official Receiver.
© Institute for Fiscal Studies Household responses to complex tax incentives HMRC/HMT/ESRC Project: Code: RES Project duration: Jan 1, 2011.
Copyright © 2006 Pearson Addison-Wesley. All rights reserved. Chapter 2 Comparative Development: Differences and Commonalities among Developing Countries.
Resistência dos Materiais, 5ª ed.
Shamim Ferdous, Jason Mckey, Qazi Afroza Sultana, Rabiul Islam Bangladesh Prothibondhi Foundation and Job Placement Ltd Australia Empowerment of Disabled.
Investigating Macroeconomic Determinants of Happiness in Transition Countries: How Important is Government Expenditure? Lena Malešević Perović and Silvia.
Pawns or rooks? Local government responses to economic shocks, Michael Craw Institute of Government University of Arkansas, Little Rock
“WHY CAN’T YOU SIT STILL?:” HYPERACTIVITY AND SCHOOLING OUTCOMES FOR CANADIAN BROTHERS AND SISTERS Kelly Chen, Nicole Fortin, Philip Oreopoulos and Shelley.
Predictors of Depressive Symptoms and Obesity in African-American Women Transitioning from Welfare to Work Mayola Rowser PhDc, DNP, FNP-BC, PMHNP.
. 0. SECOR Conceptual Slides Evaluation Comment Option 1 Criterion 4Criterion 3Criterion 2Criterion 1Options Option 2 © 2010 Accenture. All rights.
NUTRITIONAL STATUS OF CHILDREN, DIVERSITY OF FOOD CONSUMPTION AND ETHNICITY IN LAOS NAFRI POLICY BRIEF, No 17/2014 POLICY THINK TANK 9/10/20151.
Presentation transcript:

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

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 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 Source: Ministere de lEnseignement de Base et de lAlphabetisation, 2005

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?

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 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.

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 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 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).

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

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.

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.

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

Descriptive Statistics: Child Characteristics VariableObsMean Std. Dev.MinMax Childs age in months2, Male2, % HAZ2, Stunting2, % Moderate stunting2, % Severe stunting2, % WHZ1, Wasting1, % Moderate wasting1,9329.5% Severe wasting1, %0.3201

Descriptive Statistics: Regressors VariableObsMeanStd. Dev.MinMax Zero to 5km of program school2,0076.9% Five to 10km of program school2, % Number of years ES was available2, Mothers years of education2, Mothers age2, Fathers years of education2, Fathers age2, Monthly expenditures (1,000 FCFA)2, Urban residence2, % Television2, % Radio2, % Refrigerator2,0075.4% Bicycle2, % Moped2, % Car2,0072.4% Electricity2, % Cell phone2, % Mothers health knowledge2, Mothers bargaining power2, %0.4801

Descriptive Statistics: Histogram of parents education

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%

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:

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.

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*** (0.031)(0.064)(0.018) Per capita expenditures (log)0.084* (0.049)(0.014) Mothers age ***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*** (0.002)(0.004)(0.001) Urban residence0.370***0.499***-0.137*** (0.052)(0.117)(0.037) Has a television0.225* (0.128)(0.041) Has a radio0.416*** (0.094)(0.026) Observations1,934 R-squared

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** *0.139**0.130*0.079 (0.064)(0.061)(0.064)(0.065) (0.062) Childs age (months)-0.204***-0.203***-0.204*** *** (0.035) Fathers education (0.053)(0.054)(0.053) (0.054) Per capita expenditures (log)0.169***0.170*** (0.060)(0.061) Health knowledge (0.027) Bargaining power (0.122)(0.119) Within 30 minutes of a health clinic (0.107)(0.105) Observations2,0001,9982,000 1,998 R-squared Number of provinces43

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) (0.072)(0.071) Health Knowledge (0.038)(0.037) Bargaining power-0.221**-0.218** (0.097)(0.096) Within 30 minutes of a health clinic (0.111)(0.117) Observations1,9251,9231,925 1,923 R-squared Number of provinces43

Table 4: First Stage IV Regression for Child Weight-for-Height (WHZ) VARIABLES Mothers education Per capita expenditures Zero to 5km of ES (d1) (0.066)(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) (0.020)(0.019)(0.027) Interaction d1*ESyears0.349***0.407***-0.168** (0.107)(0.124)(0.066) Interaction d2*ESyears (0.045)(0.042)(0.045) Health knowledge0.072***0.028** (0.010)(0.013) Bargaining power (0.030)(0.044) Within 30 minutes of a health clinic (0.035)(0.041) Wealth index0.368***0.320*** (0.027)(0.026) Observations R-squared

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*** (0.062)(0.040) Bargaining power ** (0.129)(0.126) Within 30 minutes of a health clinic (0.149)(0.130) Per capita expenditures (log)-1.113*** (0.394) Observations1,925 1,923 R-squared Number of provinces43

Threshold effects

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.

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.

Questions?

Thank you!

Table A1: First Stage IV Regressions for Child Height-for-Age (HAZ) VARIABLES Mothers education Per capita expenditures Zero to 5km of ES (d1) (0.063)(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 (0.020)(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 (0.044) (0.042)(0.045) Health knowledge0.095***0.070***0.030** (0.011)(0.010)(0.013) Bargaining power-0.084** (0.033)(0.030)(0.044) Within 30 minutes of a health clinic0.108*** (0.037)(0.034)(0.040) Wealth index0.362***0.314*** (0.027)(0.026) Observations2,000 1,998 R-squared F (test of excluded instruments) P-value of F-test

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