Bibliographic citations
Sánchez, A., (2018). Crecimiento económico y desnutrición crónica infantil en las regiones de Moquegua y Loreto en el período 2000 - 2016 [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/3889
Sánchez, A., Crecimiento económico y desnutrición crónica infantil en las regiones de Moquegua y Loreto en el período 2000 - 2016 [Tesis]. : Universidad Nacional Agraria La Molina; 2018. https://hdl.handle.net/20.500.12996/3889
@misc{renati/242770,
title = "Crecimiento económico y desnutrición crónica infantil en las regiones de Moquegua y Loreto en el período 2000 - 2016",
author = "Sánchez Macedo, Alizi Rosario",
publisher = "Universidad Nacional Agraria La Molina",
year = "2018"
}
This study obtained the value of marginal effects of the main socio-economic variables that affect the probability (prevalence) to present chronic child undernutrition (CCU) rates of children under 5 years old in the regions of Loreto and Moquegua, using the glogit model (Logit and probit regression for grouped data), to analyze the incidence of using incomes of its main extractive activities: hydrocarbons and mining activity, respectively. For the previous idea, it was found that the access to safe drinking water, as well as the percentage of rural communities (housing area) and the mother´s educational level have presented marginal coefficients: 1.37%, 1.6% and 0.11% for Loreto, while Moquegua: 0.96%, 1.1% and 0.08%, respectively. About marginal effects of increased income from hydrocarbon and mining activities, this work shows that there are greater contributions to reduce the prevalence of chronic child undernutrition (CCU) rates which they have been obtained from the first extractive activity with a 24, 18%, while the second activity contributed 5.5%. Finally, using the Granger causality test, results show a one way of causality in both regions: from CCU to GDP, when there is lower proportion of that the region have chronically malnourished children, GDP will increase for the following 4 years, (in order of the lags of the model).
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