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

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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
Maryland
Compare Zip Codes
Comparison Subject

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

40.0
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Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Maryland Map

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

Zip Code Births / 1,000 Women vs State vs National
1.216514.052.0(-48.0)#153.0(-49.0)#124
2.207695.052.0(-47.0)#253.0(-48.0)#152
3.211407.052.0(-45.0)#353.0(-46.0)#303
4.217388.052.0(-44.0)#453.0(-45.0)#390
5.209059.052.0(-43.0)#553.0(-44.0)#463
6.2162010.052.0(-42.0)#653.0(-43.0)#596
7.2083310.052.0(-42.0)#753.0(-43.0)#604
8.2175610.052.0(-42.0)#853.0(-43.0)#630
9.2186110.052.0(-42.0)#953.0(-43.0)#685
10.2153611.052.0(-41.0)#1053.0(-42.0)#761
11.2060712.052.0(-40.0)#1153.0(-41.0)#852
12.2191312.052.0(-40.0)#1253.0(-41.0)#954
13.2102914.052.0(-38.0)#1353.0(-39.0)#1,144
14.2083814.052.0(-38.0)#1453.0(-39.0)#1,275
15.2180115.052.0(-37.0)#1553.0(-38.0)#1,287
16.2100516.052.0(-36.0)#1653.0(-37.0)#1,575
17.2081216.052.0(-36.0)#1753.0(-37.0)#1,647
18.2077717.052.0(-35.0)#1853.0(-36.0)#1,742
19.2167817.052.0(-35.0)#1953.0(-36.0)#1,772
20.2069317.052.0(-35.0)#2053.0(-36.0)#1,781
21.2081618.052.0(-34.0)#2153.0(-35.0)#1,840
22.2105318.052.0(-34.0)#2253.0(-35.0)#1,916
23.2074619.052.0(-33.0)#2353.0(-34.0)#2,010
24.2190319.052.0(-33.0)#2453.0(-34.0)#2,087
25.2154119.052.0(-33.0)#2553.0(-34.0)#2,141
26.2071420.052.0(-32.0)#2653.0(-33.0)#2,291
27.2102820.052.0(-32.0)#2753.0(-33.0)#2,322
28.2116321.052.0(-31.0)#2853.0(-32.0)#2,512
29.2061621.052.0(-31.0)#2953.0(-32.0)#2,518
30.2165821.052.0(-31.0)#3053.0(-32.0)#2,553
31.2104222.052.0(-30.0)#3153.0(-31.0)#2,660
32.2172222.052.0(-30.0)#3253.0(-31.0)#2,746
33.2116122.052.0(-30.0)#3353.0(-31.0)#2,756
34.2115322.052.0(-30.0)#3453.0(-31.0)#2,878
35.2166823.052.0(-29.0)#3553.0(-30.0)#3,083
36.2063424.052.0(-28.0)#3653.0(-29.0)#3,240
37.2180425.052.0(-27.0)#3753.0(-28.0)#3,402
38.2101525.052.0(-27.0)#3853.0(-28.0)#3,415
39.2121425.052.0(-27.0)#3953.0(-28.0)#3,449
40.2110825.052.0(-27.0)#4053.0(-28.0)#3,467
41.2109025.052.0(-27.0)#4153.0(-28.0)#3,498
42.2165525.052.0(-27.0)#4253.0(-28.0)#3,556
43.2077825.052.0(-27.0)#4353.0(-28.0)#3,588
44.2128626.052.0(-26.0)#4453.0(-27.0)#3,713
45.2120426.052.0(-26.0)#4553.0(-27.0)#3,719
46.2103526.052.0(-26.0)#4653.0(-27.0)#3,766
47.2172726.052.0(-26.0)#4753.0(-27.0)#3,776
48.2163826.052.0(-26.0)#4853.0(-27.0)#3,793
49.2113226.052.0(-26.0)#4953.0(-27.0)#3,821
50.2167326.052.0(-26.0)#5053.0(-27.0)#3,827
51.2116026.052.0(-26.0)#5153.0(-27.0)#3,831
52.2173726.052.0(-26.0)#5253.0(-27.0)#3,842
53.2068926.052.0(-26.0)#5353.0(-27.0)#3,848
54.2084127.052.0(-25.0)#5453.0(-26.0)#4,019
55.2101727.052.0(-25.0)#5553.0(-26.0)#4,043
56.2104428.052.0(-24.0)#5653.0(-25.0)#4,199
57.2074828.052.0(-24.0)#5753.0(-25.0)#4,207
58.2073528.052.0(-24.0)#5853.0(-25.0)#4,209
59.2081528.052.0(-24.0)#5953.0(-25.0)#4,229
60.2121928.052.0(-24.0)#6053.0(-25.0)#4,302
61.2177628.052.0(-24.0)#6153.0(-25.0)#4,319
62.2179428.052.0(-24.0)#6253.0(-25.0)#4,370
63.2161729.052.0(-23.0)#6353.0(-24.0)#4,551
64.2104829.052.0(-23.0)#6453.0(-24.0)#4,557
65.2068529.052.0(-23.0)#6553.0(-24.0)#4,579
66.2191729.052.0(-23.0)#6653.0(-24.0)#4,646
67.2065829.052.0(-23.0)#6753.0(-24.0)#4,670
68.2121730.052.0(-22.0)#6853.0(-23.0)#4,757
69.2122330.052.0(-22.0)#6953.0(-23.0)#4,792
70.2162330.052.0(-22.0)#7053.0(-23.0)#4,937
71.2120831.052.0(-21.0)#7153.0(-22.0)#5,037
72.2101231.052.0(-21.0)#7253.0(-22.0)#5,081
73.2112831.052.0(-21.0)#7353.0(-22.0)#5,120
74.2155031.052.0(-21.0)#7453.0(-22.0)#5,130
75.2165731.052.0(-21.0)#7553.0(-22.0)#5,265
76.2074032.052.0(-20.0)#7653.0(-21.0)#5,347
77.2140332.052.0(-20.0)#7753.0(-21.0)#5,355
78.2072132.052.0(-20.0)#7853.0(-21.0)#5,358
79.2120232.052.0(-20.0)#7953.0(-21.0)#5,398
80.2085132.052.0(-20.0)#8053.0(-21.0)#5,414
81.2064032.052.0(-20.0)#8153.0(-21.0)#5,435
82.2110432.052.0(-20.0)#8253.0(-21.0)#5,467
83.2067633.052.0(-19.0)#8353.0(-20.0)#5,838
84.2122134.052.0(-18.0)#8453.0(-19.0)#5,939
85.2121034.052.0(-18.0)#8553.0(-19.0)#6,035
86.2073634.052.0(-18.0)#8653.0(-19.0)#6,075
87.2164934.052.0(-18.0)#8753.0(-19.0)#6,173
88.2184134.052.0(-18.0)#8853.0(-19.0)#6,201
89.2083236.052.0(-16.0)#8953.0(-17.0)#6,625
90.2065736.052.0(-16.0)#9053.0(-17.0)#6,648
91.2179536.052.0(-16.0)#9153.0(-17.0)#6,721
92.2081437.052.0(-15.0)#9253.0(-16.0)#6,958
93.2107837.052.0(-15.0)#9353.0(-16.0)#7,007
94.2161337.052.0(-15.0)#9453.0(-16.0)#7,018
95.2104737.052.0(-15.0)#9553.0(-16.0)#7,043
96.2175537.052.0(-15.0)#9653.0(-16.0)#7,090
97.2153937.052.0(-15.0)#9753.0(-16.0)#7,141
98.2087838.052.0(-14.0)#9853.0(-15.0)#7,217
99.2087638.052.0(-14.0)#9953.0(-15.0)#7,300
100.2104638.052.0(-14.0)#10053.0(-15.0)#7,349

Common Questions

What are the Top 10 Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Maryland?
Top 10 Zip Codes with the Fewest Births per 1,000 Women Below Poverty Level in Maryland are:
#1
4.0
#2
5.0
#3
7.0
#4
8.0
#5
9.0
#6
10.0
#7
10.0
#8
10.0
#9
10.0
#10
11.0
What zip code has the Fewest Births per 1,000 Women Below Poverty Level in Maryland?
21651 has the Fewest Births per 1,000 Women Below Poverty Level in Maryland with 4.0.
What is the Number of Births per 1,000 Women Below Poverty Level in the State of Maryland?
Number of Births per 1,000 Women Below Poverty Level in Maryland is 52.0.
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.