Top 1000 Most Popular U.S. Baby Names Of 2000

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2000 Popular Names U.S. - Top 1000 Baby Names
				Change from 1999						Change from 1999
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Emily	25,956	1.301	0	-583	1	Jacob	34,477	1.651	0	-886
2	Hannah	23,082	1.157	0	1,406	2	Michael	32,037	1.535	0	-1,875
3	Madison	19,968	1.001	4	1,915	3	Matthew	28,573	1.369	0	-1,842
4	Ashley	17,997	0.902	2	-139	4	Joshua	27,537	1.319	0	279
5	Sarah	17,702	0.887	-1	-1,402	5	Christopher	24,932	1.194	1	-671
6	Alexis	17,629	0.884	-3	-1,605	6	Nicholas	24,652	1.181	-1	-986
7	Samantha	17,265	0.865	-2	-1,773	7	Andrew	23,641	1.132	0	-214
8	Jessica	15,709	0.787	1	-639	8	Joseph	22,831	1.094	0	-382
9	Elizabeth	15,099	0.757	1	-246	9	Daniel	22,319	1.069	0	-375
10	Taylor	15,078	0.756	-2	-1,827	10	Tyler	21,504	1.030	0	-1,161
11	Lauren	14,175	0.711	1	264	11	William	20,661	0.990	3	-49
12	Alyssa	13,553	0.679	-1	-482	12	Brandon	20,336	0.974	-1	-1,263
13	Kayla	13,312	0.667	0	23	13	Ryan	20,264	0.971	-1	-749
14	Abigail	13,090	0.656	4	1,409	14	John	20,096	0.963	1	-252
15	Brianna	12,879	0.646	-1	465	15	Zachary	19,850	0.951	2	-131
16	Olivia	12,852	0.644	4	1,598	16	David	19,769	0.947	0	-571
17	Emma	12,552	0.629	0	822	17	Anthony	19,651	0.941	1	182
18	Megan	11,434	0.573	-3	-791	18	James	17,987	0.862	1	-565
19	Grace	11,285	0.566	8	2,106	19	Justin	17,783	0.852	1	-493
20	Victoria	10,925	0.548	-4	-946	20	Alexander	17,286	0.828	1	-130
21	Rachel	10,675	0.535	-2	-949	21	Jonathan	16,885	0.809	1	-177
22	Anna	10,589	0.531	7	1,485	22	Christian	16,057	0.769	3	1,278
23	Sydney	10,242	0.513	0	594	23	Austin	15,944	0.764	-10	-4,823
24	Destiny	9,850	0.494	4	677	24	Dylan	15,402	0.738	-1	-577
25	Morgan	9,502	0.476	-1	-104	25	Ethan	15,223	0.729	12	3,780
26	Jennifer	9,386	0.470	-5	-1,228	26	Benjamin	14,840	0.711	3	1,179
27	Jasmine	9,097	0.456	-1	-254	27	Noah	14,274	0.684	-3	-649
28	Haley	9,069	0.455	3	867	28	Samuel	14,171	0.679	-1	131
29	Julia	8,766	0.439	1	15	29	Robert	13,738	0.658	-3	-801
30	Kaitlyn	8,758	0.439	3	601	30	Nathan	13,037	0.624	2	502
31	Nicole	8,562	0.429	-6	-953	31	Cameron	12,761	0.611	2	326
32	Amanda	8,554	0.429	-10	-1,189	32	Kevin	12,669	0.607	2	413
33	Katherine	8,108	0.406	3	174	33	Thomas	12,639	0.605	-2	-152
34	Natalie	8,094	0.406	0	14	34	Jose	12,582	0.603	1	430
35	Hailey	7,830	0.392	19	2,461	35	Hunter	12,538	0.601	1	1,053
36	Alexandra	7,547	0.378	-4	-617	36	Jordan	12,168	0.583	-6	-883
37	Savannah	7,099	0.356	5	294	37	Kyle	11,968	0.573	-9	-1,804
38	Chloe	7,094	0.356	25	2,129	38	Caleb	9,865	0.473	3	586
39	Rebecca	7,066	0.354	-1	-363	39	Jason	9,772	0.468	1	409
40	Stephanie	7,028	0.352	-3	-663	40	Logan	9,736	0.466	2	601
41	Maria	6,854	0.344	-2	-176	41	Aaron	9,554	0.458	-3	-299
42	Sophia	6,563	0.329	11	1,140	42	Eric	9,157	0.439	-3	-223
43	Mackenzie	6,349	0.318	9	924	43	Brian	8,957	0.429	0	150
44	Allison	6,314	0.316	-4	-711	44	Gabriel	8,679	0.416	9	1,310
45	Isabella	6,242	0.313	15	1,183	45	Adam	8,133	0.390	-1	-90
46	Mary	6,198	0.311	-1	-176	46	Jack	8,121	0.389	6	738
47	Amber	6,181	0.310	-6	-659	47	Isaiah	8,029	0.385	4	529
48	Danielle	6,088	0.305	-5	-664	48	Juan	7,709	0.369	-2	-90
49	Gabrielle	5,870	0.294	-3	-260	49	Luis	7,661	0.367	6	451
50	Jordan	5,807	0.291	-2	77	50	Connor	7,529	0.361	-2	-68

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.