Top 100 Most Popular U.S. Baby Names Of 1928

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1928 Popular Names U.S. - Top 100 Baby Names
				Change from 1927						Change from 1927
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Mary	66,872	5.594	0	-3,767	1	Robert	60,699	5.319	0	-970
2	Betty	36,080	3.018	1	658	2	John	53,755	4.710	0	-2,199
3	Dorothy	33,731	2.822	-1	-2,256	3	James	52,754	4.623	0	-920
4	Helen	22,934	1.918	0	-2,386	4	William	49,117	4.304	0	-2,365
5	Margaret	20,295	1.698	0	-1,669	5	Charles	31,247	2.738	0	-689
6	Ruth	17,858	1.494	0	-1,556	6	Richard	28,794	2.523	0	2,023
7	Doris	16,572	1.386	0	62	7	Donald	25,698	2.252	1	1,858
8	Barbara	14,873	1.244	2	1,712	8	George	24,400	2.138	-1	-1,549
9	Virginia	14,501	1.213	-1	-1,224	9	Joseph	22,311	1.955	0	-1,195
10	Shirley	14,212	1.189	-1	897	10	Edward	18,489	1.620	0	-624
11	Patricia	12,332	1.032	7	1,779	11	Thomas	16,577	1.453	0	-447
12	Jean	12,205	1.021	2	-306	12	Frank	13,568	1.189	0	-809
13	Frances	12,135	1.015	-1	-854	13	Paul	13,158	1.153	0	-448
14	Elizabeth	11,763	0.984	-1	-764	14	Harold	12,681	1.111	0	-549
15	Mildred	11,647	0.974	-4	-1,406	15	Jack	12,493	1.095	1	-305
16	Anna	10,574	0.884	0	-641	16	Raymond	11,953	1.047	-1	-854
17	Evelyn	10,534	0.881	-2	-963	17	Kenneth	11,168	0.979	1	321
18	Lois	10,422	0.872	1	-83	18	Walter	10,652	0.933	-1	-692
19	Alice	10,139	0.848	-2	-541	19	David	10,001	0.876	2	579
20	Dolores	9,616	0.804	14	2,847	20	Eugene	9,706	0.851	0	-39
21	Marie	9,285	0.777	-1	-1,029	21	Henry	9,316	0.816	-2	-549
22	Martha	8,249	0.690	2	-56	22	Arthur	8,879	0.778	0	-460
23	Norma	8,164	0.683	3	167	23	Albert	8,815	0.772	0	-313
24	Rose	8,161	0.683	-1	-338	24	Ralph	8,100	0.710	0	-334
25	Gloria	8,117	0.679	-3	-417	25	Herbert	8,069	0.707	17	3,050
26	Phyllis	7,920	0.662	3	496	26	Harry	7,607	0.667	-1	-410
27	Joan	7,855	0.657	14	1,823	27	Billy	7,310	0.641	4	847
28	Irene	7,730	0.647	-7	-877	28	Carl	6,944	0.609	-2	-337
29	Marjorie	7,632	0.638	-4	-484	29	Willie	6,819	0.598	-2	-149
30	Joyce	7,243	0.606	5	558	30	Joe	6,682	0.586	0	142
31	Louise	7,053	0.590	-4	-647	31	Howard	6,493	0.569	-3	-340
32	Florence	6,970	0.583	-4	-674	32	Louis	6,296	0.552	-3	-369
33	Catherine	6,912	0.578	-2	-343	33	Alfred	6,247	0.547	12	1,495
34	Ruby	6,692	0.560	-4	-572	34	Roy	6,228	0.546	0	-156
35	Lillian	6,575	0.550	-3	-606	35	Clarence	6,068	0.532	-3	-390
36	Eleanor	6,536	0.547	-3	-424	36	Fred	6,034	0.529	-1	-278
37	Annie	6,326	0.529	-1	-237	37	Earl	5,966	0.523	-4	-437
38	Marilyn	6,066	0.507	8	511	38	Lawrence	5,596	0.490	-1	25
39	Lorraine	5,981	0.500	5	253	39	Norman	5,589	0.490	0	102
40	Thelma	5,978	0.500	-2	-327	40	Francis	5,515	0.483	-4	-129
41	Gladys	5,794	0.485	-4	-620	41	Gerald	5,489	0.481	-3	-22
42	Josephine	5,670	0.474	-3	-439	42	Leonard	5,134	0.450	-2	-177
43	Nancy	5,536	0.463	13	514	43	Ernest	4,940	0.433	-2	-267
44	June	5,518	0.462	-1	-218	44	Anthony	4,716	0.413	-1	-204
45	Edna	5,475	0.458	-5	-609	45	Stanley	4,479	0.392	-1	-425
46	Beverly	5,435	0.455	11	503	46	Marvin	4,452	0.390	1	56
47	Marion	5,422	0.454	4	211	47	Daniel	4,434	0.389	-1	-183
48	Pauline	5,388	0.451	-6	-624	48	Melvin	4,248	0.372	0	46
49	Theresa	5,332	0.446	3	204	49	Bernard	4,066	0.356	1	-99
50	Geraldine	5,156	0.431	8	227	50	Ronald	3,974	0.348	13	863

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.