Computational Social Science Forum — Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy

CSS Training Program

October 5, 2020
12:00pm to 1:30pm
Virtual Participation


Computational Social Science Forum
Date: Monday, October 5, 2020
Time: 12:00-1:30 PM Pacific Time
Location: Register to receive the schedule and access links.

Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy

Peng Ding, Statistics, UC Berkeley

Abstract: This paper evaluates the effects of being an only child in a family on psychological health, leveraging data on the One-Child Policy in China. We use an instrumental variable approach to address the potential unmeasured confounding between the fertility decision and psychological health, where the instrumental variable is an index on the intensity of the implementation of the One-Child Policy. We establish an analytical link between the local instrumental variable approach and principal stratification to accommodate the continuous instrumental variable. Within the principal stratification framework, we postulate a Bayesian hierarchical model to infer various causal estimands of policy interest while adjusting for the clustering data structure. We apply the method to the data from the China Family Panel Studies and find small but statistically significant negative effects of being an only child on self-reported psychological health for some subpopulations. Our analysis reveals treatment effect heterogeneity with respect to both observed and unobserved characteristics. In particular, urban males suffer the most from being only children, and the negative effect has larger magnitude if the families were more resistant to the One-Child Policy. We also conduct sensitivity analysis to assess the key instrumental variable assumption. Working paper:

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Weekly meetings are hosted by researchers from BIDS and D-Lab, and participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. We welcome social scientists researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. This Forum is organized as part of the Computational Social Science Training Program. Meetings are currently held virtually on Mondays at 12:00-1:30 PM Pacific Time, and interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact for more information.


Peng Ding

Assistant Professor, Statistics, UC Berkeley