Donald is not the biggest loser (among winning and losing names)

From 2015 to 2016 there was a 10% drop in U.S. boys given the name Donald at birth, from 690 to 621, plunging the name from 900th to 986th in the overall rankings. Here is the trend in Donalds born from 1880 to 2016, shown on a log scale, from the Social Security names database.


That 2016 drop is relatively big in percentage terms, but it’s been dropping an average of 6% per year since 1957 (it dropped 26% in the 8 years after the introduction of Donald Duck in 1934). I really wish it was a popular name so we could more easily see if the rise of Donald Trump is a factor in this. With so few new Donalds, and the name already trending downward, there’s no way to tell if Trump fanatics may be counterbalancing regular people turned off to the name.

Stability over change

How big is a fall of 69 births, which seems so trivial in relation to the 3.9 million children born last year? Among names with more than 5 births in each year, only 499 fell more, compared with 26,052 that fell less or rose. So Donald is definitely a loser.

But I am always amazed at how little change there is in most names from year to year. It sounds obvious to describe a trend as rising or falling, but names are scarily regular in their annual changes given that the statistics from one year to the next reflect independent decisions by separate people who overwhelmingly don’t know each other.

Here is away of visualizing the change in the number of babies given each name, from 2015 to 2016. There is one dot for each name. Those below the diagonal had a decrease in births, those above had an increase; the closer to the line the less change there was. (To adjust for the 1% drop in total births, these are shown as births per 1,000 total born.)

2015-2016 count change

No name had a change of more than 1700 births this year (Logan dropped 1697, a drop of 13%; Adeline increased 1700, or 71%). There just isn’t much movement. I find that remarkable. (Among top names, James stands out this year: 14,773 born in 2015, rising by 3 to 14,776 in 2016.)

Here’s a look at the top right corner of that figure, just showing names with 3 per 1,000 or more births in either 2015 or 2016:

2015-2016 count change 3per1000

Note that most of these top names became less popular in 2016 (below the diagonal). That fits the long-term trend, well known by now, for names to become less popular over time, which means name diversity is increasing. I described that in the history chapter of my textbook, The Family; and going back to this old blog post from 2011. (This great piece by Tristan Bridges explores why there is more diversity among female names, as you can see by the fact that they are outnumbered among the top names shown here.)

Anyway, since I did it, here are the top 20 winners and losers, in numerical terms, in 2016. Wow, look at that catastrophic 21% drop in girls given the name Alexa (thanks, Amazon). I don’t know what’s up with Brandon and Blake. Your explanations will be as good as mine for these.



For the whole series of name posts on this blog, follow the names tag, including a bunch on the name Mary

Here’s the Stata code I used (not including the long-term Donald trend), including the figure and tables. The dataset is in a zip file at Social Security, here. There is a separate file for each year. The code below runs on the two latest files: yob2015.txt and yob2016.txt.

import delimited [path]\yob2016.txt
sort v2 v1
rename v3 count16
save "[path]\n16.dta", replace
import delimited [path]\yob2015.txt
sort v2 v1
rename v3 count15
merge 1:1 v2 v1 using [path]\n16.dta
drop _merge

gen pctchg = 100*(count16-count15)/count15
drop if pctchg==. /* drops cases that don't appear in both years (5+ names) */

gen countchg = count16-count15
rename v2 sex
rename v1 name

gsort -count16
gen rank16 = _n

gsort -count15
gen rank15 = _n

gsort -countchg
gen riserank=_n

gsort countchg
gen fallrank=_n

gen rankchg = rank15-rank16

format pctchg %9.1f 
format count15 count16 countchg %15.0fc

gen prop15 = (count15/3978497)*1000 /* these are births per 1000, based on NCHS birth report for 15 & 16 */
gen prop16 = (count16/3941109)*1000

*winners table
sort riserank
list sex name count15 count16 countchg pctchg rank15 rank16 rankchg in 1/20, sep(0)

*losers table
sort fallrank
list sex name count15 count16 countchg pctchg rank15 rank16 rankchg in 1/20, sep(0)

*figure for all names
twoway (scatter prop16 prop15 if sex=="M", mc(blue) m(Oh) mlw(vvthin)) (scatter prop16 prop15 if sex=="F" , m(Oh) mc(pink) mlw(vvthin))

*figure for top names
twoway (scatter prop16 prop15 if sex=="M" & (prop15>=3 | prop16>=3), ml(name) ms(i) mlabp(0)) (scatter prop16 prop15 if sex=="F" & (prop15>=3 | prop16>=3), ml(name) ms(i) mlabp(0))

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