Top 100 Most Popular U.S. Baby Names Of 1946

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1946 Popular Names U.S. - Top 100 Baby Names
				Change from 1945						Change from 1945
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Mary	67,467	4.183	0	8,184	1	James	87,436	5.298	0	12,987
2	Linda	52,710	3.268	0	11,248	2	Robert	84,138	5.099	0	14,201
3	Patricia	46,296	2.870	1	10,457	3	John	79,261	4.803	0	13,148
4	Barbara	45,122	2.798	-1	6,846	4	William	60,038	3.638	0	9,825
5	Carol	34,284	2.126	0	3,897	5	Richard	58,868	3.567	0	12,825
6	Sandra	31,692	1.965	0	6,991	6	David	46,439	2.814	0	9,341
7	Nancy	28,344	1.757	0	6,888	7	Michael	41,181	2.495	2	11,282
8	Susan	28,267	1.753	2	9,045	8	Thomas	38,867	2.355	0	6,998
9	Sharon	27,824	1.725	-1	6,990	9	Charles	38,203	2.315	-2	5,741
10	Judith	22,308	1.383	-1	2,119	10	Ronald	31,855	1.930	0	8,192
11	Judy	20,479	1.270	2	3,477	11	Larry	28,446	1.724	0	5,624
12	Carolyn	19,900	1.234	0	2,656	12	Gary	26,924	1.632	1	5,580
13	Donna	19,769	1.226	4	4,372	13	Donald	26,262	1.591	-1	4,127
14	Betty	19,712	1.222	-3	1,323	14	Joseph	24,280	1.471	0	3,465
15	Karen	19,646	1.218	1	3,968	15	Kenneth	22,037	1.335	1	3,944
16	Kathleen	18,759	1.163	3	4,958	16	George	21,574	1.307	-1	2,256
17	Margaret	18,132	1.124	-2	2,449	17	Dennis	21,441	1.299	2	6,451
18	Shirley	17,869	1.108	-4	1,720	18	Paul	21,059	1.276	-1	4,067
19	Diane	15,832	0.982	3	3,608	19	Edward	18,579	1.126	-1	2,711
20	Joyce	15,211	0.943	-2	1,260	20	Jerry	17,562	1.064	0	2,861
21	Janet	14,143	0.877	0	1,817	21	Daniel	12,868	0.780	3	3,163
22	Janice	14,037	0.870	2	3,254	22	Frank	12,664	0.767	-1	1,793
23	Dorothy	12,797	0.793	-3	471	23	Roger	12,411	0.752	-1	1,840
24	Brenda	12,689	0.787	5	3,484	24	Stephen	11,836	0.717	2	3,197
25	Elizabeth	11,753	0.729	1	1,415	25	Raymond	11,284	0.684	-2	1,429
26	Gloria	11,687	0.725	-3	651	26	Terry	10,676	0.647	7	3,843
27	Cheryl	11,526	0.715	5	3,376	27	Gerald	10,027	0.608	-2	1,169
28	Joan	11,219	0.696	-3	824	28	Steven	8,877	0.538	15	2,902
29	Marilyn	10,711	0.664	-2	1,177	29	Wayne	8,808	0.534	6	2,071
30	Beverly	10,430	0.647	7	2,811	30	Walter	8,665	0.525	-3	1,090
31	Virginia	10,016	0.621	-3	775	31	Lawrence	8,515	0.516	-1	1,350
32	Martha	9,761	0.605	-2	1,012	32	Bruce	8,457	0.512	16	2,844
33	Bonnie	9,508	0.590	2	1,763	33	Harold	8,370	0.507	-5	846
34	Pamela	9,366	0.581	8	3,197	34	Douglas	8,333	0.505	-5	940
35	Jane	8,971	0.556	1	1,332	35	Jack	7,801	0.473	-3	948
36	Helen	8,851	0.549	-5	550	36	Arthur	7,537	0.457	-2	737
37	Jean	8,555	0.530	1	1,128	37	Joe	7,535	0.457	-1	908
38	Ruth	8,473	0.525	-5	429	38	Peter	7,481	0.453	0	997
39	Frances	8,450	0.524	-5	510	39	Carl	7,381	0.447	0	1,068
40	Ann	8,077	0.501	-1	1,040	40	Willie	7,333	0.444	-9	306
41	Phyllis	8,046	0.499	-1	1,516	41	Henry	7,314	0.443	-4	711
42	Diana	7,544	0.468	1	1,497	42	Jimmy	7,128	0.432	-1	973
43	Peggy	7,398	0.459	2	1,559	43	Roy	6,917	0.419	-3	735
44	Alice	7,364	0.457	-3	1,006	44	Anthony	6,772	0.410	2	1,087
45	Connie	7,216	0.447	2	1,878	45	Billy	6,715	0.407	-3	697
46	Cynthia	6,894	0.427	44	3,768	46	Danny	6,651	0.403	10	2,476
47	Elaine	6,801	0.422	-3	913	47	Johnny	6,605	0.400	-2	903
48	Catherine	6,366	0.395	2	1,336	48	Ralph	6,587	0.399	-4	802
49	Gail	6,228	0.386	3	1,397	49	Alan	6,489	0.393	3	1,417
50	Christine	6,203	0.385	12	2,125	50	Harry	6,028	0.365	-3	375

United States name popularity data is provided by the Social Security Administration and is based on Social Security card applications.

Data for a given year is not made available until well into the next year.

Data reflects what was recorded and has not been edited for errors, so for example the gender associated with a name may be incorrect.

The more babies that are given a particular name, the higher the popularity ranking. If multiple names have the same usage, the tie is broken by assigning popularity rank in alphabetical order. Therefore in the case of names with fewer occurrences, names with the same number of occurrences may have vastly different rankings because they will be interranked alphabetically.

To safeguard privacy, the SSA does not include names with less than 5 occurrences.

Please note, we update the data each May when the SSA releases new figures. All data changes at that time, including previous years, which will change minutely based on new information.