【2024年1月4日】【学科与科研能力提升系列讲座】高质量论文的写作——以“Leveraging Genetic Data for Causal Studies: Exploring the Impact of Childhood Obesity on Educational Attainment and Intergenerational Mobility”为例
发布时间:01-02-24

学科与科研能力提升系列讲座

讲座题目:高质量论文的写作——以“Leveraging Genetic Data for Causal Studies: Exploring the Impact of Childhood Obesity on Educational Attainment and Intergenerational Mobility”为例

时间:2024年1月4日(周四)16:00-17:30

地点:同济大厦A楼2208会议室

主讲人:Yanhong Jin, Ph.D.

Professor,Department of Agricultural, Food and Resource Economics

Rutgers, The State University of New Jersey

主讲内容:

This talk delves into the use of genetic data to unveil the causal effects of childhood obesity on educational attainment and intergenerational mobility. Utilizing the polygenic scores of Add Health participants, we propose a novel instrument variable for childhood obesity based on genetic information related to weight status.

In the first study, we find that childhood obesity has a detrimental impact on overall and subject-specific high school GPAs, probabilities of college enrollment and completion, and years of schooling. While childhood obesity lacks a statistically significant influence on cognitive abilities, it adversely affects educational achievements through health, school absenteeism and aggression, college aspirations and expectations, and family dynamics. This research provides evidence that childhood obesity can hinder educational progress, potentially affecting adult outcomes and exacerbating economic inequality.

In the second study, we find childhood obesity lowers intergenerational income and socioeconomic mobility in adult children. Mechanisms explaining these effects include adulthood income, health, education outcomes, and job opportunities. The results suggest that policies aimed at preventing and reducing childhood obesity may have far-reaching benefits by improving socioeconomic mobility and equality.

Both studies validate the efficacy of the gene-based IV, and the causal effects of childhood obesity persists across various robustness checks. Together, they reinforce the validity of using genetic information to identify and quantify causality in socioeconomic studies.

 

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