Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota

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Lowest 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
Minnesota
Compare Zip Codes
Comparison Subject

Map of Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota

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Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota Map

Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota

Zip Code Births / 1,000 Women vs State vs National
1.550732.054.0(-52.0)#148.0(-46.0)#27
2.559812.054.0(-52.0)#248.0(-46.0)#29
3.557104.054.0(-50.0)#348.0(-44.0)#161
4.553955.054.0(-49.0)#448.0(-43.0)#216
5.562825.054.0(-49.0)#548.0(-43.0)#226
6.553395.054.0(-49.0)#648.0(-43.0)#231
7.563327.054.0(-47.0)#748.0(-41.0)#390
8.565217.054.0(-47.0)#848.0(-41.0)#406
9.564258.054.0(-46.0)#948.0(-40.0)#443
10.551279.054.0(-45.0)#1048.0(-39.0)#563
11.557199.054.0(-45.0)#1148.0(-39.0)#596
12.550479.054.0(-45.0)#1248.0(-39.0)#630
13.557719.054.0(-45.0)#1348.0(-39.0)#644
14.5537310.054.0(-44.0)#1448.0(-38.0)#748
15.5595510.054.0(-44.0)#1548.0(-38.0)#778
16.5501210.054.0(-44.0)#1648.0(-38.0)#789
17.5668310.054.0(-44.0)#1748.0(-38.0)#828
18.5540911.054.0(-43.0)#1848.0(-37.0)#862
19.5580511.054.0(-43.0)#1948.0(-37.0)#869
20.5598311.054.0(-43.0)#2048.0(-37.0)#959
21.5662811.054.0(-43.0)#2148.0(-37.0)#963
22.5666111.054.0(-43.0)#2248.0(-37.0)#981
23.5672711.054.0(-43.0)#2348.0(-37.0)#984
24.5537011.054.0(-43.0)#2448.0(-37.0)#987
25.5540312.054.0(-42.0)#2548.0(-36.0)#1,016
26.5570912.054.0(-42.0)#2648.0(-36.0)#1,087
27.5561412.054.0(-42.0)#2748.0(-36.0)#1,121
28.5605012.054.0(-42.0)#2848.0(-36.0)#1,133
29.5615712.054.0(-42.0)#2948.0(-36.0)#1,149
30.5612212.054.0(-42.0)#3048.0(-36.0)#1,153
31.5602312.054.0(-42.0)#3148.0(-36.0)#1,155
32.5540813.054.0(-41.0)#3248.0(-35.0)#1,168
33.5644313.054.0(-41.0)#3348.0(-35.0)#1,317
34.5671313.054.0(-41.0)#3448.0(-35.0)#1,327
35.5541414.054.0(-40.0)#3548.0(-34.0)#1,354
36.5572614.054.0(-40.0)#3648.0(-34.0)#1,514
37.5668414.054.0(-40.0)#3748.0(-34.0)#1,531
38.5580315.054.0(-39.0)#3848.0(-33.0)#1,565
39.5507715.054.0(-39.0)#3948.0(-33.0)#1,574
40.5595415.054.0(-39.0)#4048.0(-33.0)#1,698
41.5627115.054.0(-39.0)#4148.0(-33.0)#1,699
42.5612015.054.0(-39.0)#4248.0(-33.0)#1,706
43.5637716.054.0(-38.0)#4348.0(-32.0)#1,763
44.5530116.054.0(-38.0)#4448.0(-32.0)#1,778
45.5658716.054.0(-38.0)#4548.0(-32.0)#1,892
46.5621816.054.0(-38.0)#4648.0(-32.0)#1,917
47.5676016.054.0(-38.0)#4748.0(-32.0)#1,920
48.5636316.054.0(-38.0)#4848.0(-32.0)#1,922
49.5531517.054.0(-37.0)#4948.0(-31.0)#2,009
50.5538117.054.0(-37.0)#5048.0(-31.0)#2,061
51.5637418.054.0(-36.0)#5148.0(-30.0)#2,191
52.5539718.054.0(-36.0)#5248.0(-30.0)#2,248
53.5502718.054.0(-36.0)#5348.0(-30.0)#2,252
54.5654018.054.0(-36.0)#5448.0(-30.0)#2,275
55.5593518.054.0(-36.0)#5548.0(-30.0)#2,304
56.5605118.054.0(-36.0)#5648.0(-30.0)#2,305
57.5609018.054.0(-36.0)#5748.0(-30.0)#2,312
58.5664919.054.0(-35.0)#5848.0(-29.0)#2,415
59.5657819.054.0(-35.0)#5948.0(-29.0)#2,524
60.5606019.054.0(-35.0)#6048.0(-29.0)#2,532
61.5510820.054.0(-34.0)#6148.0(-28.0)#2,618
62.5663620.054.0(-34.0)#6248.0(-28.0)#2,695
63.5570520.054.0(-34.0)#6348.0(-28.0)#2,716
64.5579020.054.0(-34.0)#6448.0(-28.0)#2,747
65.5534220.054.0(-34.0)#6548.0(-28.0)#2,749
66.5604320.054.0(-34.0)#6648.0(-28.0)#2,794
67.5676120.054.0(-34.0)#6748.0(-28.0)#2,795
68.5542021.054.0(-33.0)#6848.0(-27.0)#2,849
69.5574621.054.0(-33.0)#6948.0(-27.0)#2,875
70.5573421.054.0(-33.0)#7048.0(-27.0)#2,936
71.5596521.054.0(-33.0)#7148.0(-27.0)#3,000
72.5651121.054.0(-33.0)#7248.0(-27.0)#3,007
73.5595721.054.0(-33.0)#7348.0(-27.0)#3,044
74.5596121.054.0(-33.0)#7448.0(-27.0)#3,050
75.5671021.054.0(-33.0)#7548.0(-27.0)#3,051
76.5651621.054.0(-33.0)#7648.0(-27.0)#3,057
77.5542522.054.0(-32.0)#7748.0(-26.0)#3,159
78.5651522.054.0(-32.0)#7848.0(-26.0)#3,235
79.5663022.054.0(-32.0)#7948.0(-26.0)#3,249
80.5570622.054.0(-32.0)#8048.0(-26.0)#3,258
81.5591022.054.0(-32.0)#8148.0(-26.0)#3,270
82.5613722.054.0(-32.0)#8248.0(-26.0)#3,277
83.5616722.054.0(-32.0)#8348.0(-26.0)#3,297
84.5576422.054.0(-32.0)#8448.0(-26.0)#3,304
85.5659422.054.0(-32.0)#8548.0(-26.0)#3,306
86.5618522.054.0(-32.0)#8648.0(-26.0)#3,309
87.5638723.054.0(-31.0)#8748.0(-25.0)#3,432
88.5610123.054.0(-31.0)#8848.0(-25.0)#3,454
89.5503723.054.0(-31.0)#8948.0(-25.0)#3,468
90.5662123.054.0(-31.0)#9048.0(-25.0)#3,476
91.5665023.054.0(-31.0)#9148.0(-25.0)#3,546
92.5570323.054.0(-31.0)#9248.0(-25.0)#3,547
93.5653323.054.0(-31.0)#9348.0(-25.0)#3,567
94.5598724.054.0(-30.0)#9448.0(-24.0)#3,604
95.5581224.054.0(-30.0)#9548.0(-24.0)#3,689
96.5573124.054.0(-30.0)#9648.0(-24.0)#3,745
97.5538424.054.0(-30.0)#9748.0(-24.0)#3,803
98.5533224.054.0(-30.0)#9848.0(-24.0)#3,808
99.5591924.054.0(-30.0)#9948.0(-24.0)#3,836
100.5633924.054.0(-30.0)#10048.0(-24.0)#3,838

Common Questions

What are the Top 10 Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota?
Top 10 Zip Codes with the Lowest Births per 1,000 Women in Labor Force in Minnesota are:
#1
2.0
#2
2.0
#3
4.0
#4
5.0
#5
5.0
#6
5.0
#7
7.0
#8
7.0
#9
8.0
#10
9.0
What zip code has the Lowest Births per 1,000 Women in Labor Force in Minnesota?
55073 has the Lowest Births per 1,000 Women in Labor Force in Minnesota with 2.0.
What is the Number Births per 1,000 Women in Labor Force in the State of Minnesota?
Number Births per 1,000 Women in Labor Force in Minnesota is 54.0.
What is the Number Births per 1,000 Women in Labor Force in the United States?
Number Births per 1,000 Women in Labor Force in the United States is 48.0.