Top 1000 Most Popular U.S. Baby Names Of 1974

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1974 Popular Names U.S. - Top 1000 Baby Names
				Change from 1973						Change from 1973
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Jennifer	63,114	4.030	0	667	1	Michael	67,578	4.144	0	-269
2	Amy	29,565	1.888	0	2,601	2	Jason	54,776	3.359	1	8,093
3	Michelle	25,828	1.649	0	-1,106	3	Christopher	48,603	2.980	-1	282
4	Heather	23,181	1.480	4	3,832	4	David	41,815	2.564	1	713
5	Angela	22,802	1.456	2	1,906	5	James	41,354	2.536	-1	-1,535
6	Kimberly	22,419	1.431	-2	-1,114	6	John	37,638	2.308	0	-1,538
7	Melissa	22,166	1.415	-1	-314	7	Robert	37,031	2.271	0	-1,670
8	Lisa	19,774	1.263	-3	-2,895	8	Brian	34,460	2.113	0	-1,255
9	Stephanie	16,983	1.084	0	-280	9	Matthew	27,224	1.669	1	2,678
10	Rebecca	15,167	0.968	0	297	10	William	26,990	1.655	-1	-1,031
11	Nicole	13,116	0.837	0	-808	11	Daniel	23,838	1.462	0	143
12	Elizabeth	12,261	0.783	1	-96	12	Joseph	21,838	1.339	0	507
13	Mary	11,738	0.749	1	-578	13	Eric	20,191	1.238	0	-618
14	Julie	11,359	0.725	-2	-1,428	14	Kevin	19,473	1.194	0	-1,308
15	Christina	10,848	0.693	1	-40	15	Jeffrey	19,326	1.185	2	101
16	Kelly	10,848	0.693	2	331	16	Mark	18,916	1.160	-1	-1,468
17	Jessica	10,652	0.680	17	3,426	17	Richard	18,646	1.143	-1	-1,304
18	Shannon	10,609	0.677	4	805	18	Scott	18,044	1.107	0	-936
19	Christine	9,899	0.632	-2	-651	19	Steven	17,677	1.084	1	488
20	Sarah	9,788	0.625	15	2,572	20	Thomas	17,287	1.060	-1	-651
21	Laura	9,718	0.621	-2	-621	21	Anthony	16,388	1.005	0	-31
22	Dawn	9,418	0.601	-1	-472	22	Timothy	15,894	0.975	0	-153
23	Tracy	9,289	0.593	-3	-623	23	Charles	15,149	0.929	0	-611
24	Tammy	9,239	0.590	-9	-2,005	24	Jeremy	13,300	0.816	9	3,936
25	Tina	8,667	0.553	-1	-641	25	Ryan	13,191	0.809	5	2,676
26	Karen	8,651	0.552	-3	-704	26	Chad	12,796	0.785	-1	-89
27	Susan	8,472	0.541	-2	-811	27	Paul	12,319	0.755	-3	-587
28	April	8,452	0.540	5	1,189	28	Andrew	11,784	0.723	1	1,054
29	Patricia	8,034	0.513	-2	-442	29	Joshua	11,598	0.711	13	3,533
30	Lori	7,962	0.508	-2	-510	30	Shawn	11,445	0.702	-3	-9
31	Maria	7,792	0.498	0	139	31	Gregory	10,837	0.665	-5	-843
32	Wendy	7,562	0.483	-3	-251	32	Aaron	10,511	0.645	2	1,272
33	Cynthia	7,554	0.482	-7	-1,190	33	Jonathan	10,236	0.628	-2	-36
34	Amanda	7,476	0.477	17	1,850	34	Kenneth	10,148	0.622	-6	-833
35	Andrea	7,475	0.477	-5	-292	35	Stephen	10,115	0.620	-3	155
36	Rachel	7,397	0.472	6	1,291	36	Adam	8,427	0.517	9	1,588
37	Tonya	6,878	0.439	-1	-302	37	Sean	8,291	0.508	-1	-267
38	Stacy	6,740	0.430	-6	-599	38	Patrick	8,137	0.499	-1	-346
39	Tara	6,655	0.425	-2	-52	39	Todd	7,850	0.481	-4	-831
40	Danielle	6,627	0.423	9	978	40	Edward	7,825	0.480	-2	-543
41	Tiffany	6,477	0.414	11	977	41	Donald	7,760	0.476	-2	-604
42	Carrie	6,435	0.411	4	599	42	Ronald	7,667	0.470	-2	-662
43	Monica	6,284	0.401	-3	-92	43	Justin	7,430	0.456	8	1,354
44	Sandra	6,268	0.400	-5	-316	44	Keith	7,391	0.453	-3	-777
45	Kristen	6,054	0.387	13	1,117	45	Bryan	7,232	0.443	-2	116
46	Tanya	5,996	0.383	4	365	46	Benjamin	7,188	0.441	9	1,953
47	Erin	5,870	0.375	32	1,993	47	Jose	6,720	0.412	2	627
48	Stacey	5,802	0.370	-7	-450	48	Gary	6,447	0.395	-4	-403
49	Crystal	5,620	0.359	-1	-151	49	George	6,279	0.385	-3	-328
50	Michele	5,586	0.357	-6	-410	50	Peter	5,948	0.365	2	32

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.