Investigating Fertility Intentions for a Second Child in Contemporary China Based on User-Generated Content.

Area of Science:

• Demography
• Sociology
• Computational Social Science

Background:

• China's two-child policy aimed to increase the total fertility rate but has not met expectations.
• Traditional data collection methods (censuses, surveys) for fertility studies are resource-intensive and time-consuming.
• User-generated content (UGC) offers a novel, potentially more efficient data source.

Purpose of the Study:

• To assess couples' intentions regarding having a second child in China.
• To explore the motivations and perceived costs associated with a second child.
• To propose and validate a novel machine-human hybrid approach using social media UGC.

Main Methods:

• A machine-human hybrid approach was developed to analyze user-generated content from social media.
• The method focused on identifying user sentiments and intentions related to having a second child.
• Data analysis explored economic and mental factors influencing fertility decisions.

Main Results:

• Couples perceive significant economic costs, particularly negative career impacts on mothers, with no offsetting economic gains.
• While the joy of having children is a motivation, substantial mental costs like exhaustion and pressure exist.
• Extended family, especially grandparents, provides primary child-rearing support.

Conclusions:

• Government interventions should mitigate the career impact on mothers and enhance child-rearing support systems.
• The proposed UGC analysis method can be adapted to study low fertility rates in other nations.
• Addressing economic and mental burdens is key to potentially increasing fertility rates.