The gender most commonly associated with a given name can change considerably over time. In the United States, Hillary, Leslie, and Jordan are common names that have shifted over the last century from being predominantly "boys' names" to almost exclusively "girls' names."
% Female
Anne 99.8 99.8 99.6 100.0 99.7 319,157 April 100.0 100.0 99.6 100.0 99.7 234,849 Donna 100.0 99.8 99.8 100.0 99.7 832,134 Elizabeth 99.7 99.7 99.7 99.7 0 99.7 1,587,027 Hillary 0.0 73.0 98.7 100.0 95.7 29,091 Jordan 0.0 9.1 18.7 17.3 26.9 462,762 Leslie 0.0 52.2 90.4 97.1 70.0 374,341 Matthew 0.4 0.4 0.4 0.4 0 0.4 1,532,144 Ronald 0.0 0.2 0.7 0.0 0.4 1,074,058 Thomas 0.5 0.2 0.6 0.2 0.4 2,277,381
Date of birth is therefore a useful -- but by no means perfect -- feature to consider when imputing gender from a name. (fn: Family national or cultural background can also be beneficial. Among Russian families, newborn Alexis is likely male, but we'd guess female in recent United States births. --also Andreas? -- Robin in England vs U.S.--)
Low variation
/ \
/ \ Matthew
/ \
/ \
/ \ Jordan
/ \
/ \
/ High variation \
/ \ Hillary
/ \
Time-Independent Time-Dependent
For any specific name, a logistic regression that captures the functional form of the relationship between year and gender ratio could be suitable. However, as above, this is a name-by-name process. There is no useful general pattern of names changing associations from one gender to another. Creating a robust specification for every name in the SSA database would therefore require some 91,320 regressions (in the 1880--2012 data), each with a potentially different relationship. Some might be approximated by a linear year term, others perhaps a quadratic term, others with more rapid changes could be more accurately captured by a spline.
Low n
A machine learning approach offers a substantially more efficient process than validating 91,320 separate year-gender-name relationships.
Demonstration uses data from the wonderful https://github.com/OpenGenderTracking/globalnamedata
Need to compare https://bocoup.com/blog/global-name-data to https://github.com/ropensci/genderdata
http://abel.lis.illinois.edu/cgi-bin/genni/nameprediction.cgi?name=leslie
https://dl.acm.org/purchase.cfm?id=2467720
https://databank.illinois.edu/datasets/IDB-9087546
http://abel.ischool.illinois.edu/cgi-bin/genni/nameprediction.cgi?name=Leslie
https://cran.r-project.org/web/packages/gender/vignettes/predicting-gender.html
https://github.com/ropensci/gender
- uses name and year range; only provides point estimates
- Cameron Blevins and Lincoln Mullen, "Jane, John ... Leslie? A Historical Method for Algorithmic Gender Prediction," Digital Humanities Quarterly 9, no. 3 (2015): http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html.
https://github.com/jeremybmerrill/beauvoir
- only uses name; only provides point estimates
https://github.com/cblevins/Gender-ID-By-Time
- ancestor of R's
gender