The Brian Effect
Dillon Breen
12/13/2021
In this paper I will be seeking to prove and explain the recurring pattern in United States naming in which a single, immensely popular, multisyllabic name precedes the significant rise of multiple different multisyllabic names that rhyme with the original. Many different names fit the mold as a catalyst that started a rhyming movement, both big and small. However, the most impactful name that fits the description above is “Brian.” Therefore I will be calling this phenomenon the Brian effect. And to understand the impact that Brian had on American onomastics, we should first look at Brian’s journey through the naming landscape up until the present.
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
There are a couple of important pieces of surrounding context to note before discussing the information presented in the graph. First is the definition of a rhyme. Despite it being taught to those just learning how to read everywhere, rhyming is a much more complicated topic than one would think. For the purposes of this paper, there are only two types of rhymes that will be considered: perfect and half rhymes. A perfect rhymes1 occurs when two words share phonetically identical pronunciations from the point of their stressed vowel sound on like with “bottle” and “throttle.” A half rhyme occurs when two words share their final consonant or vowel sound but differ elsewhere like with “paper” and “trailer.” Both definitions are included in the overarching word “rhyme” which is important to point out. Secondly, in 1909, the first year that Brian broke the 5 person name barrier that gets you noticed by the Social Security Database, there was only one multisyllabic name rhyming with Brian that was in the 50 most popular boys’ names: Eugene at 43rd. In 2020, 14 of the 50 most popular boys names all fall into the same category. This statistic alone proves nothing in terms of the effect the name Brian specifically had on subsequent names, but it’s interesting to note there’s such an easily identifiable trend.
To prove the name Brian had a lasting effect on future names we have to look at its graph. The first thing you notice looking at the graph is its sudden beginning. Brian is somewhat unique in that before 1909, it never appeared on the database. History and precedent give a lot of credibility to names which in turn makes parents more open to giving those names to their children. The name Brian goes against this logic for a simple reason: immigration. Nearly 3 million people immigrated from Ireland to America in the latter half of the 19th century2, and when these people got married and had children some of them decided to name their children traditionally Irish and Gaelic names. The name Brian coming from the famous king of Ireland Brian Boru3 most definitely falls into that Irish, Gaelic category. The second thing of note is the steady growth that the Brian graph presents. Most novelty names like Brian show a rapid, steep incline shortly followed by a slower but still fairly steep decline, but Brian doesn’t. Brian grows at a slow, steady, and predictable rate until it hits its peak in 1973 and begins its decline at almost the same rate as its initial growth. “Brian” spent about 54 years consistently growing in popularity before it spent 47 years consistently doing the opposite. “Brian” stayed significantly relevant for an extraordinarily long time considering it wasn’t at all a part of the founding stock of American names.
The graphs below show the journey that “Brian” took through the different states.
## Warning: package 'mapdata' was built under R version 4.1.2## Loading required package: maps##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## map
In 1953, twenty years before its eventual peak, “Brian” was largely concentrated on the Northern part of the East Coast and a small portion on the West Coast4 as well. Both of these areas are historically known to have had a dense population of Irish immigrants and their decedents. This offers up the obvious explanation to the original boom of Brian’s. What’s less obvious is the map during Brian’s peak in 1973. This shows a much more prominent red in the states in the North and Midwest where there was a paler white before. State data on a person’s name in relation to their ethnicity is hard to find for Irish Americans because they typically fall under the more broad White category in censuses, but it’s safe to assume by this point in the 1970s, Irish Americans5 faced no real systematic discrimination and were considered part of White America. The Irish’s assimilation into the already majority white America gave parents a new set of established names to give to their children. This can certainly give an explanation for the geographical spread of “Brian” over that twenty year span. And now finally, we come to the near present day where “Brian” is much less prevalent but appears more in areas like the South where before there were almost no “Brian’s” at all. The best way I can think to describe the geographical path of “Brian” over the past century is this: it’s similar to the ripples a heavy rock makes when dropped into water. They were heavily concentrated in their original areas at first, then spread out, and then lost thier energy at the edges of the map.
Next I will be discussing some names that “Brian” affected and reasons as to why.
This graph presents us with a lot of information. Although the exact dates aren’t clear, we can see a pattern present itself with all the names on the graph besides Brian. From the 1880s to the 60s and 70s, each of these names was on the decline. Each one individually experienced a resurgence which brought them to levels near where Brian was at its peak but not quite as high. Some names seem to group up with each other and ride the same wave. Both Justin and Ryan spend about 40 years from 1940 to 1980 increasing until they reach their collective peak. Nathan takes a similar line except it starts peaking at a higher percentage of total male names and doesn’t reach as high of a peak as Justin or Ryan. Next comes Ethan, Jackson, and Logan who start their growth around 1970 then peak and begin to decline around 2000 roughly 30 years later. It should also be noted that none of these groups’ peaks reached that of the one before them either. And finally we have the most recent name explosion in this specific pattern: “Aiden.” Aiden appears in the 70s but doesn’t really begin to rise until the late 1980s. It then grows for about 25 years and peaks just lower than the previous group did at just below 1% of total male names.
To see these specific groups easier, we can look at how each of the different name’s growth correlates with one another.
## Warning: package 'ggplot2' is in use and will not be installed## Warning: package 'GGally' was built under R version 4.1.2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 20 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 32 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 20 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 32 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 20 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 32 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 20 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 32 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 22 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 32 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning: Removed 20 rows containing missing values (geom_point).
## Warning: Removed 20 rows containing missing values (geom_point).
## Warning: Removed 20 rows containing missing values (geom_point).
## Warning: Removed 20 rows containing missing values (geom_point).## Warning: Removed 22 rows containing missing values (geom_point).## Warning: Removed 20 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 33 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 rows containing missing values (geom_point).
## Warning: Removed 32 rows containing missing values (geom_point).## Warning: Removed 33 rows containing missing values (geom_point).## Warning: Removed 32 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 99 rows containing missing values## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).
## Warning: Removed 99 rows containing missing values (geom_point).## Warning: Removed 99 rows containing non-finite values (stat_density).
Now we can group names together based on who has the strongest correlation to one another. Brian was not included because as the catalyst of the trend, it doesn’t group well with any other name. There are some extremely statistically significant correlations between these name groups. They tend to group up how you imagine they would be based on the previous graph, but it’s important to understand just how significant it really is.
A clear pattern is beginning to emerge here. Firstly, Brian burst onto the scene in the 60s and 70s and clearly established normalcy for names that sounded like Brian. It took an introduction from an outside force, in this case Ireland, to shift the balance of boy’s naming in the U.S. In “Children’s First Names: An Empirical Study of Social Taste.” Lieberson discusses what he thinks could be intrinsic functions to taste when it comes to names. One conclusion he comes to that I think is particularly applicable in this case is that “the success of institutional efforts to affect tastes may reflect the ability to use the underpinning of existing tastes as opposed to creating new preferences without basing them on existing societal and cultural dispositions.” In the case of Brian, Ireland and its people subconsciously act as one of those institutions affecting change of taste. They play the role of one by introducing a whole range of new but not entirely dissimilar or unique names to the U.S naming market. By doing so they effectively changed American taste in names. Through the popularity of the name “Brian”, rhyming and therefore similar soundings names gain validity in the eyes of parents.
One note on taste that wasn’t talked about by Lieberson that I think bears itself out in the data is the energy of taste. Not that the abstract concept of taste has an actually measurable energy behind it, but that it acts as it does. Past the point in which Brian peaked in 1973 no other multisyllabic name that ended with an an ever passed the percentage of male names that Brian hit or the length of time Brian consistently grew. Next were Justin and Ryan who grew for about 10 years less than Brian did and didn’t hit the same peak. After that it was Ethan, Jackson, and Logan who grew for a total of roughly 30 years to Justin and Ryan’s 40, and again didn’t hit the same peak as either of its predecessors. And the same applies to Aiden. It has a sharper growth but a less impressive peak. It’s as if the trend has a finite amount of energy that it can spend before fizzling out. It’s a similar situation to the geographical ripples Brian caused. The beginning of the trend is like the rock falling into the water. Ripples are created but no ripple can last as long or be as strong as the last.
Another example of this would be in the case of the “arry’s”
It’s not a one to one comparison, but they both show the same pattern in a different set of rhyming names. In this case the catalyst for this pattern is Harry, the diminutive for Harold. With Harry’s rate on the decline, other similar sounding names fill. By doing so, a name can still hold tradition and familiarity while at the same time remaining unique. The balance of traditionalism and uniqueness is what keeps the rhyming naming pattern going. Once the traditionalism wears itself out and becomes tired and boring, the trend begins to die off. That’s more similar to what’s happening to the Brian trend right now. What’s being shown in this graph is the exact opposite. Larry experiences some volatility begins its first meaningful rise around 1910. It grows for 25 years till 1940, drops slightly, then peaks in 1947 just under Harry’s peak at the start of the new century. Terry then starts to grow around 5 years after Larry in 1915, has its first peak around 1945, does the same decline then spike pattern as Larry had, and then peaks just under Larry at around 1955. Terry is followed closely by Barry who starts to rise and peak at the same time, but because Barry was less common than Terry, it didn’t reach the same heights. Then finally comes Kerry who starts its rise in the 20s and peaks the lowest out of the group in 1959. Barry and Kerry being the final two in the pattern make sense. On top of being relatively obscure, both are Irish full names compared to the rest which are diminutive versions of more common English names. Perry is interesting in this group as well. I believe it never had the same rise and fall pattern as the other names for two reasons. The first being it was too popular to begin with. It already had its own identity and therefore lost its value in the naming market. The second being that just when it was beginning to peak in the 60s, Truman Capote released the novel “In Cold Blood” prominently featuring Perry Smith, a convicted murderer, as perhaps its main character which no doubt turned people off from the Perry name. The Harry naming trend on the whole lacked viable naming options, however. Given the circumstance that there were more unique but recognizable names that rhymed with Harry, this trend may have been more substantial. Both the Brian effect and the Harry trend represent two different ends of the rhyming name trend spectrum.
It seems as of recently, however, this particular trend is coming to an end. There are two main reasons I feel. One being limited power taste has when it comes to phonetic naming trends as mentioned previously. The second being viable names to fuel the machine. For a name to be viable, it must be not too obscure as to be not easily identifiable as a name, but also, not too normal as to already bring its own connotations with it. Look at the graph below for an example
This graph paints a different portrait around rhyming names. Lucas is one of the fastest growing boy’s names in the U.S. right now so it might stand to reason that some rhyming names might be coming up with it but that’s not the case. Each of these names has a specific reason as to why, however. Rufus, although a respectably popular name at the turn of the 20th century, it carries with it some distasteful connotations today. To most anyone born after WW II, Rufus, like Fido, Spike, or Odie, is considered a dog’s name. This is mostly due to Winston Churchill having not one but two dogs named Rufus. Brutus, of course, is most famously known for having killed Julius Caesar. Although many historians consider Brutus heroic and courageous for having killed Caesar it makes sense not many boys are given the name. Eunice has gone almost completely into obscurity. But Lucius has made a bit of a jump in recent years perhaps due to J.K Rowling’s character Lucius Malfoy in Harry Potter. For the most part, however, Lucas has not made an impact on its surrounding rhyming names.
I show this graph to illustrate that the Brian Effect says as much about the names it affects as the original name itself. Just because a newer sounding name has the spotlight shown on it, it doesn’t always mean it will produce the Brian Effect. It unlocks that intrinsic taste that different cultures and communities have for names Lieberson wrote about. It shows there is an underlying predictability to naming patterns even though they seem random at times.
Taste and rhyming go hand in hand in this context. We would never know for sure, but if something happened to the name Brian whether that be a Hitler or Stalinesque figure appeared with the first name Brian, or Charles Dickens wrote about Ebeneezer Brian instead of Scrooge, I would hypothesis that we would have never seen the resurgence of two syllable words that end with the letter n. Overall, I found that there is an identifiable pattern when a unique male name reaches significant enough standing, it can raise other more obscure rhyming names with it. At the same time however, those rhyming names have to fit their own mold and both the catalyst and the reaction say something about underlying name taste in the U.S.
Bibliography
Almeida, Linda Dowling. “What’s New Is Old Again: Revisiting the New Irish in America.” American Journal of Irish Studies 8 (2011): 148–57.
Britannica, T. Editors of Encyclopaedia. “rhyme.” Encyclopedia Britannica, February 4, 2020.
Crowley, George T. “The Irish in California.” Studies: An Irish Quarterly Review 25, no. 99 (1936): 451–62.
Green, Micheal. “BRIAN BORU - THE LAST GREAT HIGH KING OF IRELAND.” The Information about Ireland Site, 1997.
Lieberson, Stanley, and Eleanor O. Bell. “Children’s First Names: An Empirical Study of Social Taste.” American Journal of Sociology 98, no. 3 (1992): 511–54
Lieberson, Stanley, Susan Dumais, and Shyon Baumann. “The Instability of Androgynous Names: The Symbolic Maintenance of Gender Boundaries.” American Journal of Sociology 105, no. 5 (2000): 1249–87.
Office of Immigration Statistics. “2019 Yearbook of Immigration Statistics.” U.S. Department of Homeland Security, 2020.
Ousby,Ian.Cambridge paperback guide to literature in English.United Kingdom:Cambridge University Press,1996.