Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland

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Highest Birth Rate | In Labor Force
Population
Families and Households
Female Fertility
Female Fertility (Unmarried)
Race (Community Size)
Race (Percentage)
Ancestry (Community Size)
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian / Chaldean / SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianEuropeanFijianFilipinoFinnishFrenchFrench American IndianFrench CanadianGermanGerman RussianGhanaianGreekGuamanian / ChamorroGuatemalanGuyaneseHaitianHmongHonduranHopiHoumaHungarianIcelanderIndian (Asian)IndonesianInupiatIranianIraqiIrishIroquoisIsraeliItalianJamaicanJapaneseJordanianKenyanKiowaKoreanLaotianLatvianLebaneseLiberianLithuanianLumbeeLuxembourgerMacedonianMalaysianMalteseMarshalleseMenomineeMexicanMexican American IndianMongolianMoroccanNative HawaiianNavajoNepaleseNew ZealanderNicaraguanNigerianNorthern EuropeanNorwegianOkinawanOsageOttawaPaiutePakistaniPalestinianPanamanianParaguayanPennsylvania GermanPeruvianPimaPolishPortuguesePotawatomiPuebloPuerto RicanPuget Sound SalishRomanianRussianSalvadoranSamoanScandinavianScotch-IrishScottishSeminoleSenegaleseSerbianShoshoneSierra LeoneanSiouxSlavicSlovakSloveneSomaliSouth AfricanSouth AmericanSouth American IndianSoviet UnionSpaniardSpanishSpanish AmericanSpanish American IndianSri LankanSubsaharan AfricanSudaneseSwedishSwissSyrianTaiwaneseThaiTlingit-HaidaTohono O'OdhamTonganTrinidadian and TobagonianTsimshianTurkishU.S. Virgin IslanderUgandanUkrainianUruguayanUteVenezuelanVietnameseWelshWest IndianYakamaYaquiYugoslavianYumanYup'ikZimbabwean
Ancestry (Percentage)
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian / Chaldean / SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianEuropeanFijianFilipinoFinnishFrenchFrench American IndianFrench CanadianGermanGerman RussianGhanaianGreekGuamanian / ChamorroGuatemalanGuyaneseHaitianHmongHonduranHopiHoumaHungarianIcelanderIndian (Asian)IndonesianInupiatIranianIraqiIrishIroquoisIsraeliItalianJamaicanJapaneseJordanianKenyanKiowaKoreanLaotianLatvianLebaneseLiberianLithuanianLumbeeLuxembourgerMacedonianMalaysianMalteseMarshalleseMenomineeMexicanMexican American IndianMongolianMoroccanNative HawaiianNavajoNepaleseNew ZealanderNicaraguanNigerianNorthern EuropeanNorwegianOkinawanOsageOttawaPaiutePakistaniPalestinianPanamanianParaguayanPennsylvania GermanPeruvianPimaPolishPortuguesePotawatomiPuebloPuerto RicanPuget Sound SalishRomanianRussianSalvadoranSamoanScandinavianScotch-IrishScottishSeminoleSenegaleseSerbianShoshoneSierra LeoneanSiouxSlavicSlovakSloveneSomaliSouth AfricanSouth AmericanSouth American IndianSoviet UnionSpaniardSpanishSpanish AmericanSpanish American IndianSri LankanSubsaharan AfricanSudaneseSwedishSwissSyrianTaiwaneseThaiTlingit-HaidaTohono O'OdhamTonganTrinidadian and TobagonianTsimshianTurkishU.S. Virgin IslanderUgandanUkrainianUruguayanUteVenezuelanVietnameseWelshWest IndianYakamaYaquiYugoslavianYumanYup'ikZimbabwean
Immigrant Origin (Total)
AfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaAzoresBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma / MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabwe
Immigrant Origin (Percentage)
AfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaAzoresBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma / MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabwe
Income
Income (Families)
Income (Households)
Poverty
Poverty (Families)
Unemployment
Employment Occupations
Employment Industries
Employer Class
Commute Time
Commute Means
School Enrollment
Education by Degree Field
Occupancy
Finances
Physical Characteristics
Maryland
Compare Zip Codes
Comparison Subject

Map of Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland

0.0
1,000.0
Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland Map

Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland

Zip Code Births / 1,000 Women vs State vs National
1. 21650 1,000.0 50.0 (+950)#1 48.0 (+952)#11
2. 20667 1,000.0 50.0 (+950)#2 48.0 (+952)#24
3. 20758 750.0 50.0 (+700)#3 48.0 (+702)#67
4. 21634 630.0 50.0 (+580)#4 48.0 (+582)#108
5. 20645 429.0 50.0 (+379)#5 48.0 (+381)#236
6. 20608 424.0 50.0 (+374)#6 48.0 (+376)#246
7. 20687 413.0 50.0 (+363)#7 48.0 (+365)#254
8. 21156 378.0 50.0 (+328)#8 48.0 (+330)#295
9. 21524 320.0 50.0 (+270)#9 48.0 (+272)#418
10. 20611 264.0 50.0 (+214)#10 48.0 (+216)#596
11. 21071 264.0 50.0 (+214)#11 48.0 (+216)#598
12. 21733 255.0 50.0 (+205)#12 48.0 (+207)#631
13. 21647 240.0 50.0 (+190)#13 48.0 (+192)#714
14. 20674 233.0 50.0 (+183)#14 48.0 (+185)#745
15. 21542 216.0 50.0 (+166)#15 48.0 (+168)#867
16. 21798 214.0 50.0 (+164)#16 48.0 (+166)#880
17. 21869 198.0 50.0 (+148)#17 48.0 (+150)#1,031
18. 21082 191.0 50.0 (+141)#18 48.0 (+143)#1,091
19. 20860 170.0 50.0 (+120)#19 48.0 (+122)#1,334
20. 21155 154.0 50.0 (+104)#20 48.0 (+106)#1,606
21. 20650 150.0 50.0 (+100.0)#21 48.0 (+102)#1,669
22. 20710 149.0 50.0 (+99.0)#22 48.0 (+101)#1,691
23. 20637 149.0 50.0 (+99.0)#23 48.0 (+101)#1,692
24. 20623 140.0 50.0 (+90.0)#24 48.0 (+92.0)#1,897
25. 20779 137.0 50.0 (+87.0)#25 48.0 (+89.0)#1,998
26. 21850 134.0 50.0 (+84.0)#26 48.0 (+86.0)#2,073
27. 21034 133.0 50.0 (+83.0)#27 48.0 (+85.0)#2,105
28. 20896 133.0 50.0 (+83.0)#28 48.0 (+85.0)#2,111
29. 21530 131.0 50.0 (+81.0)#29 48.0 (+83.0)#2,157
30. 20762 129.0 50.0 (+79.0)#30 48.0 (+81.0)#2,214
31. 20664 129.0 50.0 (+79.0)#31 48.0 (+81.0)#2,217
32. 21853 127.0 50.0 (+77.0)#32 48.0 (+79.0)#2,268
33. 21084 127.0 50.0 (+77.0)#33 48.0 (+79.0)#2,271
34. 21851 127.0 50.0 (+77.0)#34 48.0 (+79.0)#2,272
35. 21601 124.0 50.0 (+74.0)#35 48.0 (+76.0)#2,379
36. 20602 117.0 50.0 (+67.0)#36 48.0 (+69.0)#2,625
37. 21162 114.0 50.0 (+64.0)#37 48.0 (+66.0)#2,756
38. 21625 111.0 50.0 (+61.0)#38 48.0 (+63.0)#2,898
39. 20677 110.0 50.0 (+60.0)#39 48.0 (+62.0)#2,966
40. 20880 110.0 50.0 (+60.0)#40 48.0 (+62.0)#2,983
41. 20759 109.0 50.0 (+59.0)#41 48.0 (+61.0)#3,001
42. 21758 109.0 50.0 (+59.0)#42 48.0 (+61.0)#3,006
43. 21754 108.0 50.0 (+58.0)#43 48.0 (+60.0)#3,055
44. 20781 106.0 50.0 (+56.0)#44 48.0 (+58.0)#3,145
45. 21074 103.0 50.0 (+53.0)#45 48.0 (+55.0)#3,310
46. 20695 103.0 50.0 (+53.0)#46 48.0 (+55.0)#3,314
47. 21032 100.0 50.0 (+50.0)#47 48.0 (+52.0)#3,499
48. 21874 100.0 50.0 (+50.0)#48 48.0 (+52.0)#3,518
49. 20877 96.0 50.0 (+46.0)#49 48.0 (+48.0)#3,761
50. 20706 92.0 50.0 (+42.0)#50 48.0 (+44.0)#4,029
51. 21227 92.0 50.0 (+42.0)#51 48.0 (+44.0)#4,033
52. 21562 92.0 50.0 (+42.0)#52 48.0 (+44.0)#4,068
53. 20636 91.0 50.0 (+41.0)#53 48.0 (+43.0)#4,122
54. 21797 91.0 50.0 (+41.0)#54 48.0 (+43.0)#4,127
55. 20751 91.0 50.0 (+41.0)#55 48.0 (+43.0)#4,157
56. 20871 90.0 50.0 (+40.0)#56 48.0 (+42.0)#4,213
57. 21713 88.0 50.0 (+38.0)#57 48.0 (+40.0)#4,418
58. 21791 87.0 50.0 (+37.0)#58 48.0 (+39.0)#4,537
59. 21918 86.0 50.0 (+36.0)#59 48.0 (+38.0)#4,637
60. 21001 85.0 50.0 (+35.0)#60 48.0 (+37.0)#4,702
61. 21205 83.0 50.0 (+33.0)#61 48.0 (+35.0)#4,914
62. 20755 83.0 50.0 (+33.0)#62 48.0 (+35.0)#4,926
63. 21716 83.0 50.0 (+33.0)#63 48.0 (+35.0)#4,939
64. 20722 82.0 50.0 (+32.0)#64 48.0 (+34.0)#5,055
65. 20653 81.0 50.0 (+31.0)#65 48.0 (+33.0)#5,145
66. 21111 80.0 50.0 (+30.0)#66 48.0 (+32.0)#5,301
67. 21220 79.0 50.0 (+29.0)#67 48.0 (+31.0)#5,362
68. 20785 79.0 50.0 (+29.0)#68 48.0 (+31.0)#5,363
69. 21009 79.0 50.0 (+29.0)#69 48.0 (+31.0)#5,366
70. 20879 79.0 50.0 (+29.0)#70 48.0 (+31.0)#5,373
71. 21663 79.0 50.0 (+29.0)#71 48.0 (+31.0)#5,427
72. 21120 78.0 50.0 (+28.0)#72 48.0 (+30.0)#5,534
73. 21837 77.0 50.0 (+27.0)#73 48.0 (+29.0)#5,689
74. 20720 74.0 50.0 (+24.0)#74 48.0 (+26.0)#6,030
75. 20866 73.0 50.0 (+23.0)#75 48.0 (+25.0)#6,205
76. 21013 73.0 50.0 (+23.0)#76 48.0 (+25.0)#6,250
77. 21919 73.0 50.0 (+23.0)#77 48.0 (+25.0)#6,265
78. 21520 73.0 50.0 (+23.0)#78 48.0 (+25.0)#6,270
79. 20715 72.0 50.0 (+22.0)#79 48.0 (+24.0)#6,350
80. 21782 72.0 50.0 (+22.0)#80 48.0 (+24.0)#6,417
81. 21144 71.0 50.0 (+21.0)#81 48.0 (+23.0)#6,499
82. 21742 71.0 50.0 (+21.0)#82 48.0 (+23.0)#6,501
83. 21237 71.0 50.0 (+21.0)#83 48.0 (+23.0)#6,511
84. 20670 71.0 50.0 (+21.0)#84 48.0 (+23.0)#6,620
85. 20707 70.0 50.0 (+20.0)#85 48.0 (+22.0)#6,671
86. 21704 70.0 50.0 (+20.0)#86 48.0 (+22.0)#6,703
87. 20872 70.0 50.0 (+20.0)#87 48.0 (+22.0)#6,723
88. 21045 69.0 50.0 (+19.0)#88 48.0 (+21.0)#6,836
89. 21158 69.0 50.0 (+19.0)#89 48.0 (+21.0)#6,870
90. 20895 69.0 50.0 (+19.0)#90 48.0 (+21.0)#6,879
91. 21777 69.0 50.0 (+19.0)#91 48.0 (+21.0)#6,969
92. 20601 68.0 50.0 (+18.0)#92 48.0 (+20.0)#7,072
93. 21842 68.0 50.0 (+18.0)#93 48.0 (+20.0)#7,116
94. 20711 68.0 50.0 (+18.0)#94 48.0 (+20.0)#7,132
95. 21631 68.0 50.0 (+18.0)#95 48.0 (+20.0)#7,164
96. 21757 68.0 50.0 (+18.0)#96 48.0 (+20.0)#7,165
97. 21521 68.0 50.0 (+18.0)#97 48.0 (+20.0)#7,182
98. 21054 67.0 50.0 (+17.0)#98 48.0 (+19.0)#7,294
99. 21826 66.0 50.0 (+16.0)#99 48.0 (+18.0)#7,518
100. 21014 65.0 50.0 (+15.0)#100 48.0 (+17.0)#7,611

Common Questions

What are the Top 10 Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland?
Top 10 Zip Codes with the Most Births per 1,000 Women in Labor Force in Maryland are:
#1
1,000.0
#2
1,000.0
#3
750.0
#4
630.0
#5
429.0
#6
424.0
#7
413.0
#8
378.0
#9
320.0
#10
264.0
What zip code has the Most Births per 1,000 Women in Labor Force in Maryland?
21650 has the Most Births per 1,000 Women in Labor Force in Maryland with 1,000.0.
What is the Number of Births per 1,000 Women in Labor Force in the State of Maryland?
Number of Births per 1,000 Women in Labor Force in Maryland is 50.0.
What is the Number of Births per 1,000 Women in Labor Force in the United States?
Number of Births per 1,000 Women in Labor Force in the United States is 48.0.