270 Reasons Women Choose Not To Have Children
By Rebecca Adams, Hilary Fung, Alissa Scheller and Shane Shifflett
Far too often, women who choose to be childfree are asked to defend their “immature,” “selfish” lifestyles. They’re told that motherhood is the “most important job in the world” and face accusations of living “meaningless” lives.
The number of childfree women is at a record high: 48 percent of women between the ages of 18 and 44 don’t have kids, according to 2014 Census numbers.
The Huffington Post and YouGov asked 124 women why they choose to be childfree. Their motivations ranged from preferring their current lifestyles (64 percent) to prioritizing their careers (9 percent) — a.k.a. fairly universal things that have motivated men not to have children for centuries. To give insight into the complex, layered decisions women make, HuffPost asked childfree readers to discuss the reasons they have chosen not to have kids and gathered 270 responses here.
Of course, these women don’t owe anyone an explanation, but perhaps allowing the public to read their unique perspectives will open people’s minds to the wide range of mature, unselfish motivations that go into deciding not to have kids.
Read our YouGov survey methodology here.
The HuffPost/YouGov poll consisted of 3,000 completed interviews conducted May 8 to 29 among U.S. adults, including 124 women who are childless and reported not wanting children in the future. It was conducted using a sample selected from YouGov's opt-in online panel to match the demographics and other characteristics of the adult U.S. population.
The Huffington Post has teamed up with YouGov to conduct daily opinion polls. You can learn more about this project and take part in YouGov's nationally representative opinion polling. Data from all HuffPost/YouGov polls can be found here. More details on the poll's methodology are available here.
Most surveys report a margin of error that represents some, but not all, potential survey errors. YouGov's reports include a model-based margin of error, which rests on a specific set of statistical assumptions about the selected sample, rather than the standard methodology for random probability sampling. If these assumptions are wrong, the model-based margin of error may also be inaccurate. Click here for a more detailed explanation of the model-based margin of error.