How popular is the baby name Salvator in the United States right now? How popular was it historically? Use the popularity graph and data table below to find out! Plus, see all the blog posts that mention the name Salvator.

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Popularity of the baby name Salvator


Posts that mention the name Salvator

Baby names that fell the fastest in the U.S. data, 1881 to today (relative decrease)

fallen leaves

We looked at the top baby name rises last month, so this month let’s look at the opposite: the top drops. That is, the baby names that decreased the most in usage, percentage-wise, from one year to the next in the Social Security Administration’s data.

Here’s the format: girl names are on the left, boy names are on the right, and the percentages represent single-year slides in usage. (For example, from 1880 to 1881, usage of the girl name Clementine dropped 68% and usage of the boy name Neil dropped 76%.)

1880s

  • 1881: Clementine, -68%; Neil, -76%
  • 1882: Malissa, -56%; Verne, -67%
  • 1883: Minna, -67%; Morton, -74%
  • 1884: Roxy, -62%; Ellsworth & Newt, -60%
  • 1885: Sina, -68%; Clarance, -74%
  • 1886: Cordia, Dicie & Johnie, -64%; Adelbert, -69%
  • 1887: Faith, -69%; Hardy, -73%
  • 1888: Diana & Hope, -63%; Connie, -55%
  • 1889: Zilpha, -71%; Wendell, -71%

1890s

  • 1890: Buena, -60%; Alvie, -69%
  • 1891: Odie, -65%; Pierce, -76%
  • 1892: Eudora, -67%; Maude, -58%
  • 1893: Lollie, -65%; Levy, -64%
  • 1894: Macy, -64%; Lindsay, -76%
  • 1895: Gina, Laurel & Pennie, -69%; Alvie & Urban, -65%
  • 1896: Dagmar, -75%; Talmage, -67%
  • 1897: Myrta & Ouida, -75%; Benton, -68%
  • 1898: Fae, -71%; Fate, -74%
  • 1899: Rosia, -80%; Fitzhugh, -79%

1900s

  • 1900: Irva, -74%; Dora, -69%
  • 1901: Leonore, -75%; Judge, -81%
  • 1902: Veva, -74%; Davis, -72%
  • 1903: Littie & Samantha, -67%; Hunter, -67%
  • 1904: Genie, -71%; Bessie & Reynold, -67%
  • 1905: Luberta, -75%; Randall, -67%
  • 1906: Dulcie, -75%; Patsy, -69%
  • 1907: Libbie, -71%; Geo, -59%
  • 1908: Aurore, -75%; Elden & Minor, -67%
  • 1909: Arnetta, -68%; Tracy, -75%

1910s

  • 1910: Lollie, -67%; Hadley, -64%
  • 1911: Nada, -72%; Shelton, -73%
  • 1912: Carla, -71%; Rosendo, -67%
  • 1913: Vassie, -67%; Auburn, -67%
  • 1914: Coy & Maryelizabeth, -64%: Hosey, -78%
  • 1915: Thomasine, -67%; Giacomo, -67%
  • 1916: Zudora, -75%; Remus, -72%
  • 1917: Athalie, -78%; Tatsuo, -82%
  • 1918: Theta, -74%; Lennis, -72%
  • 1919: Liberty, -83%; Foch, -84%

1920s

  • 1920: Veatrice, -77%; Pershing, -73%
  • 1921: Fidela & Theone, -70%; Cleven, -71%
  • 1922: Angelyn & Renata, -75%; Dail, -73%
  • 1923: Odilia, -83%; Ugo & Waino, -74%
  • 1924: Gladine, -71%; Masayuki, -72%
  • 1925: Williemae, -72%; Emitt, -72%
  • 1926: Patrice, -75%; Ann, -78%
  • 1927: Vila, -75%; Boston, -76%
  • 1928: Kazue, -79%; Shoji, -93%
  • 1929: Livia, -81%; Tatsuo, -82%

1930s

  • 1930: Ivalee, -71%; Deforest, -72%
  • 1931: Emaline, -76%; Audley, -75%
  • 1932: Zulema, -80%; Hale, -77%
  • 1933: Dessa, -78%; Burleigh, -79%
  • 1934: Nira, -81%; Overton, -71%
  • 1935: Claudean, -73%; Hester, -74%
  • 1936: Norita, -79%; Kenley, -79%
  • 1937: Adel & Berdine, -71%; Grace, -78%
  • 1938: Ever, -75%; Casimiro, -75%
  • 1939: Walda, -74%; Butler, -74%

The SSA data isn’t perfect, but it does become more accurate in the late 1930s, because “many people born before 1937 never applied for a Social Security card, so their names are not included in our data” (SSA). Now, back to the list…

1940s

  • 1940: Avalon & Ellouise, -75%; Jacque, -71%
  • 1941: Lassie, -71%; Faye & Lemar, -71%
  • 1942: Voncille, -75%; Meyer, -70%
  • 1943: Mahala, -76%; Ewing, -76%
  • 1944: Kyle, -77%; Griffith, -77%
  • 1945: Sherrianne, -74%; Ellwood, Kern & Pascal, -67%
  • 1946: Bettyjo, -71%; Adrien, -77%
  • 1947: Judye, -76%; Bernardino, -72%
  • 1948: Tilda, -78%; Saverio, -74%
  • 1949: Vickii, -77%; Alphonza, -75%

1950s

  • 1950: Ranelle, -78%; Agapito, -68%
  • 1951: Vallorie, -90%; Skippy, -72%
  • 1952: Laural, -76%; Edson, -74%
  • 1953: Annelle & Otilia, -72%; Gerrit, -70%
  • 1954: Trenace, -81%; Celso, -76%
  • 1955: Jyl, -79%; Garrie & Robet, -74%
  • 1956: Cerise, -79%; Orlin, -74%
  • 1957: Angelene, -77%; Ruby, -76%
  • 1958: Seneca, -80%; Darryel & Richerd, -72%
  • 1959: Elfrida, -82%; Dietrich, -75%

1960s

  • 1960: Jinny, -72%; Ardis, -74%
  • 1961: Perian, -91%; Cully, -84%
  • 1962: Chantay, -80%; Torin, -73%
  • 1963: Marnita, -82%; Isidore, -75%
  • 1964: Julann, -79%; Tandy, -75%
  • 1965: Tonjua, -90%; Jaimie, -86%
  • 1966: Charlet & Desi, -77%; Glennon, -74%
  • 1967: Jeryl, -83%; Haskell, -72%
  • 1968: Millette, -88%; Daneil, -77%
  • 1969: Lya, -81%; Athony, -73%

1970s

  • 1970: Cinamon, -77%; Aldrin, -77%
  • 1971: Chimene, -77%; Garet, -74%
  • 1972: Jurea, -83%; Rayvon, -77%
  • 1973: Dayatra, -86%; Keelan, -70%
  • 1974: Shondell, -78%; Efraim, -71%
  • 1975: Natonya, -78%; Imari, -76%
  • 1976: Okema, -87%; Nakia, -79%
  • 1977: Liberty, -79%; Tierre, -81%
  • 1978: Farrah, -78%; Quint, -77%
  • 1979: Danetta, -77%; Kinte, -84%

1980s

  • 1980: Vernee, -77%; Kendra, -75%
  • 1981: Santresa, -80%; Jerritt, -74%
  • 1982: Andres, -75%; Stavros, -78%
  • 1983: Tremaine, -81%; Nicanor, -75%
  • 1984: Tyechia, -81%; Jeris, -77%
  • 1985: Gricel, -89%; Duron, -76%
  • 1986: Celenia, -83%; Damiano, -76%
  • 1987: Tareva, -86%; Krystal, -75%
  • 1988: Jeree, -82%; Jammal, -80%
  • 1989: Neyva, -77%; Derrel, -76%

1990s

  • 1990: Catherin, -93%; Salvator, -88%
  • 1991: Tichina, -80%; Arsenio, -76%
  • 1992: Unnamed, -88%; Unnamed, -86% [2nd place: Emilce & Symba, -83%; Quayshaun, -80%]
  • 1993: Akeiba, -88%; Evelyn & Jawara, -71%
  • 1994: Kebrina, -86%; Farrell, -79%
  • 1995: Noheli, -84%; Ajee, -79%
  • 1996: Shatasha, -81%; Unknown, -77%
  • 1997: Hydia, -80%, Halston, -79%
  • 1998: Ajaysia, -77%; Jachai, -91%
  • 1999: Naidelyn, -86%; Denzil, -79%

2000s

  • 2000: Shanequa, -82%; Giovan, -75%
  • 2001: Berania, -78%; Devontre, -75%
  • 2002: Anallely, -86%; Caziah, -81%
  • 2003: Jnaya, -88%; Tyheim, -81%
  • 2004: Nayzeth, -89%; Myzel, -75%
  • 2005: Jenascia, -93%; Hannah, -87%
  • 2006: Babygirl, -86%; Infant, -91% [Counting legit names only: Mikalah, -82%; Jakyri, -79%]
  • 2007: Bethzy, -91%; Brasen, -83%
  • 2008: Lizania, -86%; Duvan, -79%
  • 2009: Aideliz, -88%; Kesan, -78%

2010s

  • 2010: Chastelyn, -95%; Yanixan, -87%
  • 2011: Samuel, -79%; Tiger, -80%
  • 2012: Thaily, -78%; Vadhir, -88%
  • 2013: Shanik, -88%; Oneil, -77%
  • 2014: Audris & Avalie, -80%; Sy, -73%
  • 2015: Rion, -83%; Rawley, -79%
  • 2016: Yazaira, -84%; Treysen, -79%
  • 2017: Brucha, -76%; Makana, -79%
  • 2018: Yuleimy, -85%; Neizan, -78%
  • 2019: Anifer, -86%; Nomar & Gianlucas, -73%

2020s

  • 2020: Diala, -81%; Daer, -80%
  • 2021: Ashvi, -76%; Dontavious, -78%
  • 2022: Ciena, -78%; Kiko, -77%
  • 2023: Kia & Yeyetzi, -76%; Majestic, -77%
  • 2024: Brisley, -85%; Darikson, -80%

(Did you catch the doubles? Alvie, Tatsuo, and Fae/Faye.)

Top drops aren’t quite as exciting as top rises, but certain ones become much more intriguing when you notice that they were also top rises:

  • Rose-then-dropped: Clarance, Lollie, Lindsay, Zudora, Tatsuo, Liberty, Norita, Vallorie, Krystal, Seneca, Nakia, Mikalah, Bethzy, Thaily
  • Dropped-then-rose: Clementine, Malissa, Diana, Alvie, Pierce, Judge, Rosendo

I’ve already written about some of the names above (click the links to see the posts) and I plan to write about a few of the others. In the meanwhile, though, feel free to beat me to it — leave a comment and let us know why you think any of these names saw dropped in usage when they did.

Source: SSA

Image: Adapted from Fall (6282684630) by Kenny Louie under CC BY 2.0.

[Latest update: May 2025]

Glitch alert: Why are there truncated names in the 1989 U.S. baby name data?

glitch

While doing some name research recently, I noticed a whole bunch of typos like “Christop” and “Alexandr” among the top 1,000 U.S. baby names of 1989.

I figured all the typos must be coming from a single source, so I checked the SSA’s state-by-state data, starting with the larger states. Didn’t see anything in California, didn’t see anything in Texas…but then I checked New York, and there they were:

Name# in U.S.# in NY% in NY
Christop (m)1,082*†1,082100%
Christin (f)926†49954%
Stephani (f)636†48977%
Elizabet (f)445†41994%
Alexandr (f)301*†301100%
Alexande (m)301†29999%
Katherin (f)277†24890%
*Debut, †Peak usage

A few of the above may not be typos, but the fact that so many are concentrated in a single place suggests that most are.

Given the time period and consistent truncation, my guess is that one of the counties in New York started using a computer system in 1989 that only allowed the input of up to 8 characters per name.

Now the big question: Did this glitch skew the national baby name rankings?

Yes, but only for Alexandra:

Name(s)# in U.S.Rank in U.S.
Alexandra (f)7,67943rd (old)
Alexandra (f) + Alexandr (f)7,98041st (new)

All 301 of the baby girls named Alexandr were born in New York, so it’s likely that all of them are typos. If we add these 301 to the total for Alexandra, the new number nudges Alexandra up two spots to #41. (This would bump the names Brittney and Hannah down one spot each.)

UPDATE, Apr. 2020: I recently combed through the rest of the 1989 baby name data and found even more typos:

Name# in U.S.# in NY% in NY
Jacqueli (f)157*†157100%
Cassandr (f)152*†152100%
Gabriell (f)144†11580%
Christia (m)82*†8098%
Nathanie (m)58†5595%
Elisabet (f)51†2549%
Jacquely (f)50*†4794%
Kristoph (m)44*44100%
Mackenzi (f)422662%
Salvator (m)41†3790%
Johnatha (m)34†3191%
Katharin (f)23†23100%
Anastasi (f)22*†2091%
Francesc (f)19*†19100%
Kimberle (f)171271%
Dominiqu (f)15*†15100%
Nicolett (f)15*1280%
Annemari (f)14*†1393%
Kassandr (f)13*†13100%
Johnatho (m)12*†12100%
Mackenzi (m)11*†982%
Sebastia (m)11*†11100%
Bernadet (f)9*†9100%
Demetriu (m)9*†9100%
Geneviev (f)9*†9100%
Kristofe (m)9*†9100%
Alejandr (m)8*†675%
Antoinet (f)8*†8100%
Cassondr (f)8*†8100%
Constanc (f)8*†8100%
Francisc (m)8*†788%
Priscill (f)7*†7100%
Annamari (f)6*†6100%
Angeliqu (f)5*†5100%
Francesc (m)55100%
*Debut, †Peak usage

Many of the above were one-hit wonders, which makes sense.

P.S. Here are three more glitches I’ve found since writing this post: the Korea/Kansas mis-codes, the New York state data gaps, and Essfa in Vermont.

Image: Adapted from Data loss of image file (public domain)

[Latest update: Feb. 2025]