Many of the studies on the sex ratio at birth (SRB) are based on a small number of cases over a short period of time. Taking a multivariate approach to a dataset consisting of nearly 199 million birth records in the United States from 1968 to 2019, we present a detailed analysis of several possible factors that might affect the sex ratio at birth (SRB) and its patterns of variation. We found that race/ethnicity is the variable with the strongest influence on this index. Parental age, birth order and solar radiation also have a bearing on the SRB, albeit only to a moderate degree. The historical evolution of the SRB among Black and American Indian and Alaska Native populations remains unexplained.

While previous studies have confirmed the negative effects of son preference on the prenatal care received by girls, few have examined its effect on birth outcomes. This study contributes to the literature on son preference by examining this relationship. The degree of son preference is measured by the sex ratio at birth, and the data were obtained from the birth registry of South Korea, which has a long history of strong son preference. We find that girls are more likely to be born with low birth weight when son preference is stronger. In addition, when son preference is stronger, girls are more likely to be born outside hospitals, which implies that mothers conceiving girls make fewer prenatal visits to the hospital when their son preference is stronger.

In 2010, according to the sixth Chinese census, the sex ratio at birth (SRB) was 118 males for every 100 females. The global SRB average is about 105. Thus, the gap between 118 and 105 is made up of “missing girls.” Scholars present three main explanations for the skewed SRB statistic: sex-selective abortion, infanticide and delayed or late registration. Most studies take a demographic and cultural approach to explain the high SRB. However, we believe the story of the “missing girls” is also an administrative one and adopt the street-level bureaucrat theory of policy implementation to explain the pervasiveness of late registration in rural China. We use descriptive statistics derived from the 1990, 2000 and 2010 census data to identify the “missing girls.” We believe the combination of late registration and unreported births may point to a larger proportion of “missing girls” than previously reported from the SRB statistic.