Distributional effects of OPORTUNIDADES on early child development
Adequate health, nutrition, and education during childhood are essential for human development. Deficits in these realms undermine the capacity to acquire the necessary skills to perform in life. Social policies addressing the causes of disadvantages in child development take up an important place in the social agenda. The Mexican Oportunidades program is such a policy. Investments in children’s health, nutrition, and education by the program are expected to facilitate children’s development. Previous studies found little effect of Oportunidades on child’s cognition and positive effects on noncognitve development. However, the majority of these studies take the average outcome as the relevant indicator of the effect of the program which overlooks the effect on the “non-average” child. A methodology capable of unveiling effects along the outcome’s distribution is proposed here. Such methodology, originally proposed by Davidson and Duclos (2013), is based on tests of stochastic dominance and is suitable for observing effects beyond the mean. Four indicators of cognitive development and one of behavioral problems (noncognitve development) are analyzed in a sample of 2,595 children aged 2 to 6 years. The sample was collected in rural communities in Mexico in 2003 as part of the evaluation of the program. Oportunidades decreases behavioral problems experienced by children exposed to the program. The ranges where the effect is found cover a large part of the distribution of the outcomes and a large proportion of the children in the sample. In comparison to other studies, additional effects by gender and ethnicity are now found. Only one indicator of cognitive development (short-term memory) shows positive effects. Nevertheless, the results for this indicator show that children with lower values of cognitive development benefitted from the program while children with higher values did not. These heterogeneous effects highlight the importance of going beyond the average effect approach.
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