Top 100 Most Popular U.S. Baby Names Of 1931

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1931 Popular Names U.S. - Top 100 Baby Names
				Change from 1930						Change from 1930
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Mary	60,296	5.463	0	-3,850	1	Robert	60,508	5.657	0	-1,641
2	Betty	36,105	3.271	0	-2,129	2	James	51,219	4.789	0	-2,722
3	Dorothy	26,524	2.403	0	-3,885	3	John	48,423	4.527	0	-4,011
4	Barbara	21,797	1.975	2	3,516	4	William	43,276	4.046	0	-3,989
5	Joan	19,117	1.732	3	3,638	5	Richard	31,816	2.975	0	-367
6	Helen	17,661	1.600	-2	-2,253	6	Charles	29,924	2.798	0	-1,947
7	Margaret	17,333	1.571	-2	-1,021	7	Donald	29,102	2.721	0	54
8	Patricia	16,468	1.492	-1	717	8	George	20,687	1.934	0	-2,097
9	Shirley	14,405	1.305	2	-371	9	Joseph	19,330	1.807	0	-1,651
10	Doris	13,629	1.235	-1	-1,835	10	Thomas	16,846	1.575	1	-168
11	Ruth	13,130	1.190	-1	-1,810	11	Edward	15,645	1.463	-1	-1,704
12	Virginia	11,884	1.077	0	-1,314	12	Paul	12,252	1.145	0	-705
13	Jean	11,012	0.998	1	-973	13	David	11,780	1.101	2	-496
14	Dolores	10,807	0.979	-1	-2,031	14	Kenneth	11,567	1.081	3	-134
15	Joyce	10,580	0.959	3	921	15	Jack	11,478	1.073	-1	-951
16	Elizabeth	10,291	0.932	-1	-705	16	Frank	11,470	1.072	-3	-1,071
17	Nancy	10,033	0.909	7	963	17	Raymond	10,856	1.015	-1	-860
18	Frances	9,715	0.880	-2	-931	18	Harold	10,692	1.000	0	-968
19	Marilyn	9,666	0.876	3	378	19	Billy	9,263	0.866	1	-22
20	Lois	9,350	0.847	-3	-764	20	Walter	8,943	0.836	-1	-786
21	Alice	8,816	0.799	-1	-700	21	Eugene	8,146	0.762	0	-885
22	Norma	8,760	0.794	3	249	22	Henry	7,802	0.729	0	-789
23	Gloria	8,549	0.775	7	945	23	Arthur	7,526	0.704	0	-754
24	Evelyn	8,491	0.769	-5	-1,046	24	Albert	7,130	0.667	0	-813
25	Anna	8,430	0.764	-2	-649	25	Ralph	6,956	0.650	0	-681
26	Mildred	8,167	0.740	-5	-1,185	26	Gerald	6,823	0.638	4	340
27	Marie	7,548	0.684	-1	-879	27	Ronald	6,724	0.629	10	1,089
28	Martha	7,479	0.678	-1	-353	28	Carl	6,581	0.615	-2	-371
29	Phyllis	7,212	0.653	-1	-558	29	Joe	6,444	0.602	-2	-433
30	Rose	6,881	0.623	-1	-809	30	Harry	6,039	0.565	-2	-810
31	Beverly	6,788	0.615	3	332	31	Willie	5,962	0.557	-2	-705
32	Donna	6,601	0.598	5	622	32	Bobby	5,855	0.547	9	672
33	Louise	5,895	0.534	-2	-1,047	33	Fred	5,699	0.533	-1	-348
34	Marjorie	5,880	0.533	-2	-753	34	Howard	5,621	0.526	-3	-721
35	Ann	5,857	0.531	6	325	35	Roy	5,487	0.513	-2	-429
36	Catherine	5,642	0.511	-1	-656	36	Louis	5,424	0.507	-2	-385
37	Irene	5,613	0.509	-4	-850	37	Norman	5,421	0.507	3	125
38	Geraldine	5,410	0.490	5	-39	38	Lawrence	5,367	0.502	-2	-269
39	Ruby	5,333	0.483	-3	-660	39	Clarence	5,063	0.473	0	-433
40	Eleanor	5,257	0.476	-1	-565	40	Earl	5,044	0.472	-2	-521
41	Annie	5,091	0.461	-1	-663	41	Herbert	4,787	0.448	-6	-886
42	Peggy	5,077	0.460	25	993	42	Francis	4,735	0.443	0	-254
43	Florence	5,010	0.454	-5	-855	43	Jerry	4,369	0.408	8	477
44	June	5,002	0.453	3	-93	44	Daniel	4,359	0.408	3	-106
45	Janet	4,947	0.448	8	346	45	Marvin	4,357	0.407	3	-45
46	Carol	4,892	0.443	8	295	46	Anthony	4,304	0.402	-2	-373
47	Lillian	4,806	0.435	-3	-642	47	Ernest	4,297	0.402	-4	-489
48	Rita	4,614	0.418	-6	-854	48	Leonard	4,275	0.400	-3	-386
49	Lorraine	4,587	0.416	-3	-579	49	Stanley	4,200	0.393	0	-184
50	Jane	4,533	0.411	5	-42	50	Alfred	3,949	0.369	-4	-605

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.