North Dakota · 1928

Top 100 names in North Dakota, 1928, by official Social Security birth counts.

Rank	Name	Births
#1	Mary	216
#2	Dorothy	189
#3	Betty	181
#4	Helen	134
#5	Irene	125
#6	Shirley	118
#7	Ruth	115
#8	Doris	113
#9	Alice	110
#10	Lois	109
#11	Joyce	98
#12	Delores	98
#13	Lorraine	94
#14	Margaret	93
#15	Phyllis	91
#16	Donna	91
#17	Beverly	84
#18	Jean	77
#19	Patricia	74
#20	Evelyn	69
#21	Elizabeth	68
#22	Esther	64
#23	Elaine	64
#24	Frances	63
#25	Dolores	62
#26	Rose	59
#27	Anna	56
#28	Marjorie	55
#29	Florence	54
#30	Bernice	53
#31	Virginia	53
#32	Viola	52
#33	Lillian	51
#34	Norma	49
#35	June	49
#36	Marilyn	48
#37	Gladys	48
#38	Vivian	48
#39	Arlene	48
#40	Barbara	48
#41	Lucille	47
#42	Mildred	46
#43	Marie	46
#44	Marion	46
#45	Marian	42
#46	Eleanor	42
#47	Violet	41
#48	Gloria	40
#49	Marlys	40
#50	Ramona	40
#51	Maxine	38
#52	Leona	37
#53	Carol	37
#54	Audrey	37
#55	Ruby	36
#56	Agnes	36
#57	Grace	36
#58	Clara	35
#59	Verna	35
#60	Ann	34
#61	Adeline	34
#62	Genevieve	33
#63	Edna	32
#64	Elsie	32
#65	Eileen	31
#66	Edith	30
#67	Luella	30
#68	Kathryn	29
#69	Mavis	28
#70	Catherine	28
#71	Eunice	28
#72	Deloris	27
#73	Katherine	27
#74	Joan	27
#75	Darlene	26
#76	Harriet	26
#77	Pauline	26
#78	Gertrude	25
#79	Louise	25
#80	Ida	25
#81	Hazel	25
#82	Lila	24
#83	Martha	24
#84	Ella	24
#85	Pearl	24
#86	Loretta	23
#87	Eva	23
#88	Laverne	23
#89	Yvonne	22
#90	Ethel	22
#91	Theresa	21
#92	Beatrice	21
#93	Fern	21
#94	Charlotte	21
#95	Blanche	21
#96	Geraldine	20
#97	Mabel	20
#98	Vera	20
#99	Kathleen	19
#100	Myrtle	19

Uniquely popular in North Dakota

Names whose share of births in this state is far above the national share (per 10,000 births).

Name	Births	Per 10k in state	Per 10k nationwide
Marlys	40	63	1
Mavis	28	44	5
Adeline	34	54	6
Luella	30	48	6
Deloris	27	43	6
Delores	98	155	24
Fern	21	33	7
Verna	35	55	13
Lila	24	38	9
Darlene	26	41	10
Donna	91	144	39
Viola	52	82	23

Figures are from the U.S. Social Security Administration Popular Baby Names dataset (public domain). Names with fewer than 5 recorded births in a year or state are suppressed by the source.