According to Wikipedia, the Trivers-Willard hypothesis:
"...suggests that female mammals are able to adjust offspring sex ratio in response to their maternal condition. For example, it may predict greater parental investment in males by parents in 'good conditions' and greater investment in females by parents in 'poor conditions' (relative to parents in good condition)."For humans, the hypothesis suggests that people with relatively high social status might be more likely to have boys. Some studies have shown evidence for this hypothesis, but based on my very casual survey, it is not persuasive.
To test whether the T-W hypothesis holds up in humans, I downloaded birth data for the nearly 4 million babies born in the U.S. in 2014.
I selected variables that seemed likely to be related to social status and used logistic regression to identify variables associated with sex ratio.
Summary of results
- Running regression with one variable at a time, many of the variables I selected have a statistically significant effect on sex ratio, with the sign of the effect generally in the direction predicted by T-W.
- However, many of the variables are also correlated with race. If we control for either the mother's race or the father's race, most other variables have no additional predictive power.
- Contrary to other reports, the age of the parents seems to have no predictive power.
- Strangely, the variable that shows the strongest and most consistent relationship with sex ratio is the number of prenatal visits. Although it seems obvious that prenatal visits are a proxy for quality of health care and socioeconomic status, the sign of the effect is opposite what T-W predicts; that is, more prenatal visits is a strong predictor of lower sex ratio (more girls).
This dataset provides strong evidence of a race effect: African Americans and Hispanics are more likely than whites to have girls. Asians are slightly more likely to have girls.
Other than than, there is no evidence to support T-W. The number of prenatal visits has strong predictive power, but the sign of the effect is the opposite of what T-W would predict.
And once we control for race and prenatal visits, no other variables have predictive power (despite the size of the dataset).
You can read all the details in this Jupyter notebook.
Note: Following convention, I report sex ratio in terms of boys per 100 girls. The overall sex ratio at birth is about 105; that is, 105 boys are born for every 100 girls.
I've run exactly the same analysis using data from 2013. Here's the notebook. All of the results and conclusions are substantially the same.