Top 1000 Most Popular U.S. Baby Names Of 1976

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1976 Popular Names U.S. - Top 1000 Baby Names
				Change from 1975						Change from 1975
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Jennifer	59,474	3.784	0	1,291	1	Michael	66,954	4.100	0	-1,500
2	Amy	31,341	1.994	0	-910	2	Jason	52,680	3.226	0	494
3	Melissa	25,095	1.596	1	930	3	Christopher	45,210	2.768	0	-1,382
4	Heather	24,198	1.539	-1	-101	4	David	39,294	2.406	1	117
5	Angela	22,042	1.402	0	-1,318	5	James	38,309	2.346	-1	-1,282
6	Michelle	19,555	1.244	0	-3,110	6	John	34,006	2.082	1	-1,091
7	Kimberly	18,966	1.207	0	-1,297	7	Robert	33,810	2.070	-1	-1,525
8	Jessica	18,370	1.169	5	5,441	8	Brian	30,532	1.869	0	-76
9	Lisa	17,351	1.104	-1	-783	9	Matthew	29,981	1.836	0	1,431
10	Amanda	15,590	0.992	4	2,937	10	Daniel	24,720	1.514	1	898
11	Stephanie	15,179	0.966	-2	-595	11	William	24,446	1.497	-1	-1,126
12	Rebecca	14,491	0.922	-1	565	12	Joseph	24,073	1.474	0	1,934
13	Nicole	14,451	0.919	-3	175	13	Eric	21,374	1.309	0	1,563
14	Christina	14,034	0.893	-2	1,059	14	Jeremy	20,139	1.233	10	4,936
15	Sarah	13,993	0.890	2	2,822	15	Kevin	18,481	1.132	0	397
16	Elizabeth	13,546	0.862	-1	1,073	16	Ryan	17,497	1.071	7	2,193
17	Shannon	13,371	0.851	4	4,000	17	Jeffrey	17,023	1.042	-3	-1,308
18	Jamie	12,538	0.798	50	8,304	18	Joshua	16,865	1.033	8	2,291
19	Kelly	11,794	0.750	-3	60	19	Richard	16,762	1.026	-3	-924
20	Laura	11,253	0.716	0	943	20	Steven	16,661	1.020	-2	-402
21	Julie	10,841	0.690	-3	-303	21	Timothy	16,628	1.018	1	674
22	Mary	10,318	0.656	-3	-649	22	Anthony	16,611	1.017	-1	117
23	Rachel	8,455	0.538	3	229	23	Thomas	16,128	0.988	-3	-420
24	Erin	8,384	0.533	14	1,491	24	Scott	15,728	0.963	-7	-1,597
25	Andrea	8,353	0.531	3	310	25	Mark	15,519	0.950	-6	-1,231
26	Christine	8,111	0.516	-4	-991	26	Charles	13,934	0.853	-1	-646
27	Sara	8,023	0.510	27	2,892	27	Andrew	13,316	0.815	1	554
28	Carrie	7,992	0.508	1	136	28	Paul	11,788	0.722	1	-229
29	Jaime	7,837	0.499	234	6,922	29	Chad	11,774	0.721	-2	-1,070
30	April	7,762	0.494	-5	-481	30	Aaron	11,351	0.695	2	802
31	Karen	7,505	0.477	-4	-661	31	Jonathan	11,318	0.693	-1	406
32	Tiffany	7,484	0.476	5	565	32	Benjamin	10,680	0.654	4	1,130
33	Danielle	7,363	0.468	9	946	33	Justin	10,155	0.622	4	1,262
34	Maria	7,347	0.467	-3	-177	34	Shawn	10,107	0.619	-3	-582
35	Dawn	7,049	0.448	-12	-1,697	35	Gregory	10,062	0.616	-2	-340
36	Crystal	6,945	0.442	7	701	36	Adam	9,946	0.609	2	1,292
37	Tracy	6,824	0.434	-13	-1,684	37	Stephen	9,730	0.596	-3	-6
38	Stacy	6,524	0.415	-4	-647	38	Kenneth	9,134	0.559	-3	-503
39	Wendy	6,466	0.411	-6	-1,009	39	Nathan	8,617	0.528	7	1,552
40	Susan	6,405	0.407	-8	-1,081	40	Brandon	7,792	0.477	4	703
41	Tammy	6,227	0.396	-11	-1,302	41	Patrick	7,643	0.468	-2	-420
42	Tara	6,024	0.383	-1	-432	42	Sean	7,579	0.464	-2	-14
43	Patricia	6,017	0.383	-7	-1,032	43	Bryan	7,292	0.446	4	280
44	Cynthia	6,014	0.383	-4	-526	44	Edward	7,056	0.432	-3	-332
45	Monica	5,981	0.381	4	504	45	Jose	7,055	0.432	3	86
46	Misty	5,906	0.376	1	353	46	Travis	6,835	0.419	5	873
47	Lori	5,849	0.372	-8	-800	47	Donald	6,799	0.416	-5	-399
48	Tina	5,746	0.366	-13	-1,319	48	Keith	6,750	0.413	1	-74
49	Amber	5,648	0.359	10	966	49	Ronald	6,461	0.396	-4	-605
50	Emily	5,547	0.353	-2	66	50	Todd	6,441	0.394	-7	-715

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.