The Harmony of Programs Package: Quasi-experimental Evidence on Health and Nutrition Interventions in Rural Senegal Théophile T. Azomahou a , Fatoumata L. Diallo b , Wladimir Raymond c (a) UNU-MERIT and Maastricht University (b) University Cheikh-Anta-Diop and CRES (c) STATEC Luxembourg UNU-WIDER Development Conference: Human Capital and Growth Helsinki, 6-7 June 2016 T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 1 / 36
Outline Outline 1 Motivation 2 Data and variables 3 Econometric specifications 4 Treatment effects 5 Findings 6 Perspectives T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 2 / 36
Motivation Motivation Why malnutrion and intestinals parasites are important issues in developing countries? In underdeveloped countries, hundreds of millions of children suffer from poverty, health, morbidity and malnutrition. Severe malnutrition can cause delays or even deficits in cognitive development. Intestinal worms are endemic in tropical and subtropical regions. At global level, because of its negative impact on health and education, malnutrition contributes to weaken human capital accumulation: - early growth faltering (exposure in utero) - nutritional effects on younger children (0-2 years) - slower brain development and effects on the delay in school T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 3 / 36
Motivation Motivation (cont’d) Importance of evaluating nutrition and health programs Programme evaluation has become an important tool to inform policy makers about the efficient allocation of resources and for the improvement of existing policies. In Senegal, poverty and vulnerability are higher in rural areas. The government of Senegal who supports since over 10 years nutritional and health programs in rural schools always tries to know to what extent these programs produce positives results. T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 4 / 36
Motivation Motivation (cont’d) Empirical studies do not draw the same conclusions School meals and school performance: some contributions No evidence : Ahmed (2004), Kazianga et al.(2009), Tan et al. (1999), Simeon and Grantham-McGregor (1989). Positive effect : Vermeerrch and Kremer (2004), Cueto and Chinen (2007), Ahmed (2004), Simeon and Grantham-McGregor (1989). Negative effect : Ahmed and del Ninno (2002), Ahmed (2004). T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 5 / 36
Motivation Motivation (cont’d) Empirical studies do not draw the same conclusions Deworming and school performance: some contributions No evidence : Miguel and Kremer (2004), Kvalsig et al. (1991), Nokes et al. (1992). Positive effect : Kvalsig et al. (1991). Negative effect : Miguel and Kremer (2004). Home vs. school deworming : Azomahou and Diallo (2016). Deworming at school has a positive effect on pupils’ performance while deworming at home has a negative impact. This result indicates that the use of widely spread traditional deworming medicines should be discouraged. There is no study that try to measure both the impact of a lunch and a deworming program at school while estimating the determinants of school performance in a joint framework. T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 6 / 36
Motivation Motivation (cont’d) Aim of this study Assess the impact of school deworming and meal as programs package on test scores, enrollment, promotion and dropout rate while elaborating on the determinants of school performance. Contributions of the paper: i) A new dataset (quasi-experimental) ii) Advantages of package programs: Low cost and more effective than simple programs iii) Econometric framework: Double-index selection models (double endogenous selection vs. generalized Roy) iv) Wide range of treatment effects v) Policy analysis: a) cost-effectiveness, b) welfare benefit of programs T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 7 / 36
Data and variables Data and variables Data School deworming and meal programs implemented in early 2000 by the World Food Programme (WFP) and the Government of Senegal Primary data collected by the ‘Consortium pour la recherche Economique et Social (CRES)’ and the Ministry of Education as part of an experimental program on school canteens and deworming in rural Senegal. New and rich data set: an important amount of work (cleaning, recoding and imputing) has been done to make it useable. Sample of about 4500 pupils for 160 schools. Data provide information about pupils, schools, households and communities characteristics. T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 8 / 36
Data and variables Data and variables (cont’d) Variables Outcome indicators: i) Scores: aggregate, French and Math ii) Enrollment, promotion and dropout rate ! Debate on the relevance of such outcomes. Ideally, one would like to have pure nutritional outcomes (e.g. child growth, etc). Control variable gathered into four categories: i) Pupils characteristics (gender, age, Islamic school,...) ii) Schools characteristics (distance to school, class size, water point,...) iii) Household characteristics (food, health and education expenditures,...) iv) Community environment (college, children labor, domestic chores,...) Double treatment ( T 1 : Dworm=1, T 2 : Dmeal=1) Full list of variables: see paper T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 9 / 36
Data and variables Treatment status: Deworming () vs. meal Meal program T 2 ) Total (margins for T 1 ) 0 1 Deworming program ( T 1 ) 0 65.013% 22.914% 87.927% (0.476) (0.420) 1 8.202% 3.871% 12.073% (0.274) (0.192) Total (margins for T 2 ) 73.215% 26.785% 100% T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 10 / 36
Data and variables Aggregate score: by treatment status Meal program 0 1 Deworming program 0 37.687 41.771 (19.306) (18.992) 1 36.694 47.663 (16.561) (14.266) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 11 / 36
Data and variables French score: by treatment status Meal program 0 1 Deworming program 0 38.413 40.742 (21.071) (21.028) 1 35.366 45.242 (19.817) (17.885) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 12 / 36
Data and variables Math score: by treatment status Meal program 0 1 Deworming program 0 36.965 42.678 (21.121) (21.204) 1 37.627 50.084 (17.687) (16.699) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 13 / 36
Data and variables Enrollment rate: by treatment status Meal program 0 1 Deworming program 0 -31.404 7.631 (60.005) (37.574) 1 -32.281 -20.358 (34.715) (51.624) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 14 / 36
Data and variables Promotion rate: by treatment status Meal program 0 1 Deworming program 0 79.152 78.760 (12.959) (11.740) 1 73.345 81.887 (14.798) (6.760) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 15 / 36
Data and variables Dropout rate: by treatment status Meal program 0 1 Deworming program 0 16.603 15.072 (12.989) (9.302) 1 15.181 10.191 (12.400) (0.950) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 16 / 36
DES model DES model (cont’d) Specification Probit selection equations : Let T ∗ 1 i and T ∗ 2 i denote two latent (unobserved) variables, which are assumed to be functions of observed characteristics w ji ( j = 1 or 2 ) of N households/firms ( i = 1 , · · · , N ) . Formally, T ∗ 1 i = γ ′ 1 w 1 i + µ 1 i , (1) T ∗ 2 i = γ ′ 2 w 2 i + µ 2 i , (2) where γ j denotes the vectors of parameters to be estimated, and µ ji denotes the usual error terms. The observed counterparts to T ∗ 1 i and T ∗ 2 i , denoted by T 1 i and T 2 i , are defined as T 1 i = 1 [ T ∗ 1 i > 0 ] , (3) T 2 i = 1 [ T ∗ 2 i > 0 ] , (4) where 1 [ · ] denotes the indicator function. T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 17 / 36
DES model DES model (cont’d) Specification Outcome equation : The outcome for individual i , y i , is given by y i = β ′ x i + δ 1 T 1 i + δ 2 T 2 i + θ T 1 i T 2 i + ε i , (5) where x i denotes control variables (e.g. household income, etc.), β , δ j and θ are parameter vectors and scalars to be estimated, and ε i denotes the error term. By including the interaction term, T 1 i T 2 i , as a regressor in equation (5), we can isolate the exclusive effect of either treatment and their joint effect, while estimating complementarity ( θ > 0) or substitutability ( θ < 0) of policies T 1 i , T 2 i . Equations (1)-(5) is a generalization of the dummy endogenous variable model of Heckman (1978). T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 18 / 36
DES model DES model (cont’d) Estimations FIML (a) method: Assumptions We make the following distributional assumption: ( µ 1 i , µ 2 i , ε i ) ′ is normally distributed with vector mean 0 and covariance matrix: 1 Σ = ρ µ 1 µ 2 1 σ 2 ρ µ 1 ε σ ε ρ µ 2 ε σ ε ε The likelihood function of the model consists of four parts following from the contributions of the two selections: ( T 1 i = 1 , T 2 i = 1 ) , ( T 1 i = 1 , T 2 i = 0 ) , ( T 1 i = 0 , T 2 i = 1 ) , ( T 1 i = 0 , T 2 i = 0 ) T. Azomahou (UNU-MERIT) The Harmony of Programs Package 6-7 June 2016 19 / 36
Recommend
More recommend
