A Bayesian Hierarchical Model to Estimate and Forecast Child Labour Supply in India.
My research is to estimate the prevalence and causes of child labour (Ages 5-17) across India. The study will offer a new method to obtain accurate estimates of child labour by using a Bayesian hierarchical model. Child labour will be defined distinctly from child work and carefully measured using a combination of different data sources, which will address the limitations of using a single dataset. A multi-level model with demographic information including age, gender, education, fertility and family structure will allow to explain the causes of child labour as well as to predict the risk of child labour in each type of household. Finally, the probability of being child labour for each age will be forecasted.
September 2017 - September 2020
My research interest is in employment, industrial or occupational segregation and child labour in India using both social and economic approaches. Previously, I worked as a researcher in a government-funded research institute (Korea Institute of Industrial Economics and Trade) for three years, and a NGO worker for five years (Good Neighbors International).
I completed two master’s degrees - MSc in Social Research Methods and Statistics (in 2016-2017), MA in International Development (in 2007-2008) - at the University of Manchester.
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