Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan

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Lowest Birth Rate | Below Poverty
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
Michigan
Compare Zip Codes
Comparison Subject

Map of Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan

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Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan Map

Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan

Zip Code Births / 1,000 Women vs State vs National
1. 49266 3.0 53.0 (-50.0)#1 53.0 (-50.0)#63
2. 49284 6.0 53.0 (-47.0)#2 53.0 (-47.0)#240
3. 49816 6.0 53.0 (-47.0)#3 53.0 (-47.0)#272
4. 49765 7.0 53.0 (-46.0)#4 53.0 (-46.0)#299
5. 48226 8.0 53.0 (-45.0)#5 53.0 (-45.0)#376
6. 48631 8.0 53.0 (-45.0)#6 53.0 (-45.0)#389
7. 49305 8.0 53.0 (-45.0)#7 53.0 (-45.0)#408
8. 48363 9.0 53.0 (-44.0)#8 53.0 (-44.0)#478
9. 49705 9.0 53.0 (-44.0)#9 53.0 (-44.0)#556
10. 48465 9.0 53.0 (-44.0)#10 53.0 (-44.0)#577
11. 48860 10.0 53.0 (-43.0)#11 53.0 (-43.0)#659
12. 49912 10.0 53.0 (-43.0)#12 53.0 (-43.0)#680
13. 48734 11.0 53.0 (-42.0)#13 53.0 (-42.0)#736
14. 49113 11.0 53.0 (-42.0)#14 53.0 (-42.0)#785
15. 48878 11.0 53.0 (-42.0)#15 53.0 (-42.0)#791
16. 49646 12.0 53.0 (-41.0)#16 53.0 (-41.0)#864
17. 49920 12.0 53.0 (-41.0)#17 53.0 (-41.0)#893
18. 49344 12.0 53.0 (-41.0)#18 53.0 (-41.0)#896
19. 48467 12.0 53.0 (-41.0)#19 53.0 (-41.0)#922
20. 49718 12.0 53.0 (-41.0)#20 53.0 (-41.0)#960
21. 48432 12.0 53.0 (-41.0)#21 53.0 (-41.0)#966
22. 49228 13.0 53.0 (-40.0)#22 53.0 (-40.0)#1,027
23. 49328 13.0 53.0 (-40.0)#23 53.0 (-40.0)#1,045
24. 48829 13.0 53.0 (-40.0)#24 53.0 (-40.0)#1,051
25. 48454 13.0 53.0 (-40.0)#25 53.0 (-40.0)#1,094
26. 49802 14.0 53.0 (-39.0)#26 53.0 (-39.0)#1,170
27. 49235 14.0 53.0 (-39.0)#27 53.0 (-39.0)#1,217
28. 49885 14.0 53.0 (-39.0)#28 53.0 (-39.0)#1,225
29. 49774 14.0 53.0 (-39.0)#29 53.0 (-39.0)#1,226
30. 48635 14.0 53.0 (-39.0)#30 53.0 (-39.0)#1,234
31. 48731 14.0 53.0 (-39.0)#31 53.0 (-39.0)#1,237
32. 49630 14.0 53.0 (-39.0)#32 53.0 (-39.0)#1,238
33. 48054 15.0 53.0 (-38.0)#33 53.0 (-38.0)#1,329
34. 48005 15.0 53.0 (-38.0)#34 53.0 (-38.0)#1,348
35. 48722 15.0 53.0 (-38.0)#35 53.0 (-38.0)#1,372
36. 49614 15.0 53.0 (-38.0)#36 53.0 (-38.0)#1,379
37. 49089 15.0 53.0 (-38.0)#37 53.0 (-38.0)#1,407
38. 49629 15.0 53.0 (-38.0)#38 53.0 (-38.0)#1,408
39. 48861 15.0 53.0 (-38.0)#39 53.0 (-38.0)#1,413
40. 48104 16.0 53.0 (-37.0)#40 53.0 (-37.0)#1,477
41. 49319 16.0 53.0 (-37.0)#41 53.0 (-37.0)#1,490
42. 48840 16.0 53.0 (-37.0)#42 53.0 (-37.0)#1,502
43. 49325 16.0 53.0 (-37.0)#43 53.0 (-37.0)#1,578
44. 48760 16.0 53.0 (-37.0)#44 53.0 (-37.0)#1,609
45. 48604 17.0 53.0 (-36.0)#45 53.0 (-36.0)#1,687
46. 48208 17.0 53.0 (-36.0)#46 53.0 (-36.0)#1,699
47. 49756 17.0 53.0 (-36.0)#47 53.0 (-36.0)#1,730
48. 48933 17.0 53.0 (-36.0)#48 53.0 (-36.0)#1,743
49. 48159 17.0 53.0 (-36.0)#49 53.0 (-36.0)#1,757
50. 49612 17.0 53.0 (-36.0)#50 53.0 (-36.0)#1,776
51. 48350 18.0 53.0 (-35.0)#51 53.0 (-35.0)#1,871
52. 48095 18.0 53.0 (-35.0)#52 53.0 (-35.0)#1,888
53. 48662 18.0 53.0 (-35.0)#53 53.0 (-35.0)#1,965
54. 48823 19.0 53.0 (-34.0)#54 53.0 (-34.0)#2,000
55. 49046 19.0 53.0 (-34.0)#55 53.0 (-34.0)#2,073
56. 49449 19.0 53.0 (-34.0)#56 53.0 (-34.0)#2,121
57. 49029 19.0 53.0 (-34.0)#57 53.0 (-34.0)#2,156
58. 49619 19.0 53.0 (-34.0)#58 53.0 (-34.0)#2,165
59. 48357 20.0 53.0 (-33.0)#59 53.0 (-33.0)#2,260
60. 49633 20.0 53.0 (-33.0)#60 53.0 (-33.0)#2,320
61. 49072 20.0 53.0 (-33.0)#61 53.0 (-33.0)#2,324
62. 48819 20.0 53.0 (-33.0)#62 53.0 (-33.0)#2,332
63. 49670 20.0 53.0 (-33.0)#63 53.0 (-33.0)#2,355
64. 48856 20.0 53.0 (-33.0)#64 53.0 (-33.0)#2,389
65. 49444 21.0 53.0 (-32.0)#65 53.0 (-32.0)#2,444
66. 49770 21.0 53.0 (-32.0)#66 53.0 (-32.0)#2,466
67. 49079 21.0 53.0 (-32.0)#67 53.0 (-32.0)#2,479
68. 49858 21.0 53.0 (-32.0)#68 53.0 (-32.0)#2,484
69. 49306 21.0 53.0 (-32.0)#69 53.0 (-32.0)#2,489
70. 48164 21.0 53.0 (-32.0)#70 53.0 (-32.0)#2,496
71. 49453 21.0 53.0 (-32.0)#71 53.0 (-32.0)#2,571
72. 49812 21.0 53.0 (-32.0)#72 53.0 (-32.0)#2,633
73. 48193 22.0 53.0 (-31.0)#73 53.0 (-31.0)#2,696
74. 48048 22.0 53.0 (-31.0)#74 53.0 (-31.0)#2,723
75. 48216 22.0 53.0 (-31.0)#75 53.0 (-31.0)#2,761
76. 49755 22.0 53.0 (-31.0)#76 53.0 (-31.0)#2,817
77. 49411 22.0 53.0 (-31.0)#77 53.0 (-31.0)#2,824
78. 49814 22.0 53.0 (-31.0)#78 53.0 (-31.0)#2,843
79. 49431 23.0 53.0 (-30.0)#79 53.0 (-30.0)#2,956
80. 48202 23.0 53.0 (-30.0)#80 53.0 (-30.0)#2,967
81. 48189 23.0 53.0 (-30.0)#81 53.0 (-30.0)#2,969
82. 49419 23.0 53.0 (-30.0)#82 53.0 (-30.0)#2,994
83. 49779 23.0 53.0 (-30.0)#83 53.0 (-30.0)#3,036
84. 49682 23.0 53.0 (-30.0)#84 53.0 (-30.0)#3,037
85. 48367 23.0 53.0 (-30.0)#85 53.0 (-30.0)#3,038
86. 49233 23.0 53.0 (-30.0)#86 53.0 (-30.0)#3,061
87. 49033 23.0 53.0 (-30.0)#87 53.0 (-30.0)#3,086
88. 48720 23.0 53.0 (-30.0)#88 53.0 (-30.0)#3,103
89. 48621 23.0 53.0 (-30.0)#89 53.0 (-30.0)#3,106
90. 49401 24.0 53.0 (-29.0)#90 53.0 (-29.0)#3,169
91. 49660 24.0 53.0 (-29.0)#91 53.0 (-29.0)#3,205
92. 49283 24.0 53.0 (-29.0)#92 53.0 (-29.0)#3,275
93. 49625 24.0 53.0 (-29.0)#93 53.0 (-29.0)#3,351
94. 49757 24.0 53.0 (-29.0)#94 53.0 (-29.0)#3,367
95. 49817 24.0 53.0 (-29.0)#95 53.0 (-29.0)#3,376
96. 48708 25.0 53.0 (-28.0)#96 53.0 (-28.0)#3,433
97. 49684 25.0 53.0 (-28.0)#97 53.0 (-28.0)#3,452
98. 48207 25.0 53.0 (-28.0)#98 53.0 (-28.0)#3,454
99. 48813 25.0 53.0 (-28.0)#99 53.0 (-28.0)#3,457
100. 48165 25.0 53.0 (-28.0)#100 53.0 (-28.0)#3,506

Common Questions

What are the Top 10 Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan?
Top 10 Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Michigan are:
#1
3.0
#2
6.0
#3
6.0
#4
7.0
#5
8.0
#6
8.0
#7
8.0
#8
9.0
#9
9.0
#10
9.0
What zip code has the Fewest Births per 1,000 Women Below Poverty Level in Michigan?
49266 has the Fewest Births per 1,000 Women Below Poverty Level in Michigan with 3.0.
What is the Number of Births per 1,000 Women Below Poverty Level in the State of Michigan?
Number of Births per 1,000 Women Below Poverty Level in Michigan is 53.0.
What is the Number of Births per 1,000 Women Below Poverty Level in the United States?
Number of Births per 1,000 Women Below Poverty Level in the United States is 53.0.