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Demographic Comparison
Population
Highest PopulationHighest Population DensityLowest Population DensityMale / Female RatioFemale / Male RatioOldest PopulationOldest Population | MaleOldest Population | FemaleYoungest PopulationYoungest Population | MaleYoungest Population | Female
Families and Households
Largest Family SizeLargest Household SizeSmallest Family SizeSmallest Household Size
Female Fertility
Highest Birth Rate | All WomenHighest Birth Rate | Women 15-19Highest Birth Rate | Women 35+Highest Birth Rate | Below PovertyHighest Birth Rate | Public IncomeHighest Birth Rate | In Labor ForceLowest Birth Rate | All WomenLowest Birth Rate | Women 15-19Lowest Birth Rate | Women 35+Lowest Birth Rate | Below PovertyLowest Birth Rate | Public IncomeLowest Birth Rate | In Labor Force
Female Fertility (Unmarried)
Highest Illegitimacy | All WomenHighest Illegitimacy | Women 15-19Highest Illegitimacy | Women 35+Lowest Illegitimacy | All WomenLowest Illegitimacy | Women 15-19Lowest Illegitimacy | Women 35+
Race (Community Size)
AsianBlack / African AmericanHawaiian / PacificHispanic or LatinoNative / AlaskanWhite / Caucasian
Race (Percentage)
AsianBlack / African AmericanHawaiian / PacificHispanic or LatinoNative / AlaskanWhite / Caucasian
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
Economic Comparison
Income
Highest Per Capita IncomeHighest Earnings | MaleHighest Earnings | FemaleHighest Wage/Income Gap ($)Highest Wage/Income Gap (%)Highest Inequality / Gini IndexLowest Per Capita IncomeLowest Earnings | MaleLowest Earnings | FemaleLowest Wage/Income Gap ($)Lowest Wage/Income Gap (%)Lowest Inequality / Gini Index
Income (Families)
Highest Family IncomeLowest Family IncomeFamily Income | $0-10kFamily Income | $100k+Family Income | $200k+
Income (Households)
Highest Household IncomeLowest Household IncomeHousehold Income | $0-10kHousehold Income | $200k+Household Income | $100k+
Poverty
Highest PovertyHighest Poverty | MaleHighest Poverty | FemaleHighest Poverty | Female 18-24Highest Poverty | Female 25-34Highest Poverty | Child 0-5Highest Poverty | Child 0-16Highest Poverty | Boys 0-16Highest Poverty | Girls 0-16Highest Poverty | Seniors 65+Highest Poverty | Seniors 75+ Lowest PovertyLowest Poverty | MaleLowest Poverty | FemaleLowest Poverty | Child 0-5Lowest Poverty | Child 0-16Lowest Poverty | Boys 0-16Lowest Poverty | Girls 0-16Lowest Poverty | Seniors 65+Lowest Poverty | Seniors 75+
Poverty (Families)
Highest Family PovertyHighest Poverty | Married CoupleHighest Poverty | Single MaleHighest Poverty | Single FemaleHighest Poverty | Single FatherHighest Poverty | Single MotherLowest Family PovertyLowest Poverty | Married CoupleLowest Poverty | Single MaleLowest Poverty | Single FemaleLowest Poverty | Single FatherLowest Poverty | Single Mother
Unemployment
Highest UnemploymentHighest Unemployment | MaleHighest Unemployment | FemaleHighest Unemployment | Youth < 25Highest Unemployment | Senior > 65Lowest UnemploymentLowest Unemployment | MaleLowest Unemployment | FemaleLowest Unemployment | Youth < 25Lowest Unemployment | Senior > 65
Employment Occupations
Architecture & EngineeringArts, Media & EntertainmentBusiness & FinancialCleaning & MaintenanceCommunity & Social Service Computer & MathematicsComputer, Engineering & ScienceConstruction & ExtractionEducation Instruction & LibraryEducation, Arts & MediaFarming, Fishing & ForestryFirefighting & PreventionFood Preparation & ServingHealth Diagnosing & TreatingHealth TechnologistsHealthcare SupportInstallation, Maintenance & RepairLaw EnforcementLegal Services & SupportLife, Physical & Social ScienceManagementMaterial MovingOffice & AdministrationPersonal Care and ServiceProductionSales & RelatedSecurity & ProtectionTransportation
Employment Industries
Accommodation & Food ServicesAdministrative & Support ServicesAgriculture, Fishing & HuntingArts, Entertainment & RecreationConstructionEducational ServicesEnterprise ManagementFinance & InsuranceHealth Care & Social AssistanceInformation & TechnologyManufacturingMining, Quarrying, & ExtractionProfessional & Scientific ServicesPublic AdministrationReal Estate, Rental & LeasingRetail TradeTransportation & WarehousingUtilitiesWholesale Trade
Employer Class
Private Company EmployeesSelf-Employed / IncorporatedSelf-Employed / Not IncorporatedNot-for-profit OrganizationsLocal Government EmployeesState Government EmployeesFederal Government EmployeesUnpaid Family Workers
Commute Time
Longest CommuteLongest Commute | MaleLongest Commute | FemaleShortest CommuteShortest Commute | MaleShortest Commute | Female
Commute Means
Drive | AloneDrive | CarpoolPublic TransitTaxi / CabWalk to WorkWork from Home
Education Comparison
School Enrollment
In Private SchoolNursery / PreschoolKindergartenElementary SchoolMiddle SchoolHigh SchoolCollegeUniversity
Education by Degree Field
Computers and MathematicsBio, Nature & AgriculturalPhysical and Health SciencesPsychologySocial SciencesEngineeringMultidisciplinary StudiesScience and EngineeringBusinessEducationLiterature and LanguagesLiberal Arts and HistoryVisual and Performing ArtsCommunicationsArts and Humanities
Housing Comparison
Occupancy
Highest Owner-OccupiedHighest Renter-OccupiedLowest Owner-OccupiedLowest Renter-Occupied
Finances
Highest Median Housing CostsHighest Property PricesHighest Owner Housing CostsHighest Renter Housing CostsHighest Housing Costs / IncomeLowest Median Housing CostsLowest Property PricesLowest Owner Housing CostsLowest Renter Housing CostsLowest Housing Costs / Income
Physical Characteristics
Detached Home ResidencyCondo/Apartment ResidencyUtility Gas HeatingBottled Gas / LP HeatingElectrical HeatingFuel Oil HeatingCoal / Coke HeatingFuel-less HeatingHouseholds without a CarsHouseholds with 1 or more CarsHouseholds with 2 or more CarsHouseholds with 3 or more Cars

Zip Codes with the Fewest Births per 1,000 Women in New York

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Comparison Subject
Select Geography Level
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Comparison Scope

Common Questions

What are the Top 10 Zip Codes with the Fewest Births per 1,000 Women in New York?
Top 10 Zip Codes with the Fewest Births per 1,000 Women in New York are:
#1
2.0
#2
2.0
#3
2.0
#4
3.0
#5
3.0
#6
4.0
#7
4.0
#8
5.0
#9
5.0
#10
6.0
What zip code has the Fewest Births per 1,000 Women in New York?
14530 has the Fewest Births per 1,000 Women in New York with 2.0.
What is the Number of Births per 1,000 Women in the State of New York?
Number of Births per 1,000 Women in New York is 47.0.
What is the Number of Births per 1,000 Women in the United States?
Number of Births per 1,000 Women in the United States is 51.0.

Map of Zip Codes with the Fewest Births per 1,000 Women in New York

20.0
0.0
Zip Codes with the Fewest Births per 1,000 Women in New York Map

Zip Codes with the Fewest Births per 1,000 Women in New York

Zip Codes with the Fewest Births per 1,000 Women in New York Chart
Zip Code Births / 1,000 Women vs State vs National
1.145302.047.0(-45.0)#151.0(-49.0)#18
2.128332.047.0(-45.0)#251.0(-49.0)#25
3.109732.047.0(-45.0)#351.0(-49.0)#38
4.110203.047.0(-44.0)#451.0(-48.0)#65
5.121543.047.0(-44.0)#551.0(-48.0)#80
6.109304.047.0(-43.0)#651.0(-47.0)#112
7.121374.047.0(-43.0)#751.0(-47.0)#143
8.148535.047.0(-42.0)#851.0(-46.0)#207
9.125235.047.0(-42.0)#951.0(-46.0)#210
10.119786.047.0(-41.0)#1051.0(-45.0)#268
11.134766.047.0(-41.0)#1151.0(-45.0)#273
12.134246.047.0(-41.0)#1251.0(-45.0)#283
13.122107.047.0(-40.0)#1351.0(-44.0)#331
14.128287.047.0(-40.0)#1451.0(-44.0)#334
15.138307.047.0(-40.0)#1551.0(-44.0)#346
16.105487.047.0(-40.0)#1651.0(-44.0)#352
17.125207.047.0(-40.0)#1751.0(-44.0)#362
18.121387.047.0(-40.0)#1851.0(-44.0)#365
19.121557.047.0(-40.0)#1951.0(-44.0)#382
20.100198.047.0(-39.0)#2051.0(-43.0)#407
21.129868.047.0(-39.0)#2151.0(-43.0)#441
22.119448.047.0(-39.0)#2251.0(-43.0)#446
23.148028.047.0(-39.0)#2351.0(-43.0)#449
24.121668.047.0(-39.0)#2451.0(-43.0)#486
25.117679.047.0(-38.0)#2551.0(-42.0)#528
26.113639.047.0(-38.0)#2651.0(-42.0)#538
27.123059.047.0(-38.0)#2751.0(-42.0)#540
28.105779.047.0(-38.0)#2851.0(-42.0)#543
29.117199.047.0(-38.0)#2951.0(-42.0)#565
30.148219.047.0(-38.0)#3051.0(-42.0)#571
31.136169.047.0(-38.0)#3151.0(-42.0)#594
32.137979.047.0(-38.0)#3251.0(-42.0)#599
33.136079.047.0(-38.0)#3351.0(-42.0)#609
34.129579.047.0(-38.0)#3451.0(-42.0)#612
35.138019.047.0(-38.0)#3551.0(-42.0)#616
36.1000310.047.0(-37.0)#3651.0(-41.0)#643
37.1003810.047.0(-37.0)#3751.0(-41.0)#649
38.1151410.047.0(-37.0)#3851.0(-41.0)#681
39.1219610.047.0(-37.0)#3951.0(-41.0)#685
40.1420210.047.0(-37.0)#4051.0(-41.0)#689
41.1172410.047.0(-37.0)#4151.0(-41.0)#702
42.1292010.047.0(-37.0)#4251.0(-41.0)#715
43.1210610.047.0(-37.0)#4351.0(-41.0)#716
44.1474110.047.0(-37.0)#4451.0(-41.0)#721
45.1342810.047.0(-37.0)#4551.0(-41.0)#736
46.1283710.047.0(-37.0)#4651.0(-41.0)#742
47.1218211.047.0(-36.0)#4751.0(-40.0)#779
48.1098311.047.0(-36.0)#4851.0(-40.0)#809
49.1248711.047.0(-36.0)#4951.0(-40.0)#822
50.1386211.047.0(-36.0)#5051.0(-40.0)#825
51.1211711.047.0(-36.0)#5151.0(-40.0)#842
52.1340911.047.0(-36.0)#5251.0(-40.0)#859
53.1362511.047.0(-36.0)#5351.0(-40.0)#864
54.1334311.047.0(-36.0)#5451.0(-40.0)#867
55.1291211.047.0(-36.0)#5551.0(-40.0)#869
56.1410312.047.0(-35.0)#5651.0(-39.0)#935
57.1000512.047.0(-35.0)#5751.0(-39.0)#940
58.1057912.047.0(-35.0)#5851.0(-39.0)#942
59.1178912.047.0(-35.0)#5951.0(-39.0)#947
60.1205712.047.0(-35.0)#6051.0(-39.0)#994
61.1474712.047.0(-35.0)#6151.0(-39.0)#995
62.1373612.047.0(-35.0)#6251.0(-39.0)#1,000
63.1343112.047.0(-35.0)#6351.0(-39.0)#1,007
64.1380212.047.0(-35.0)#6451.0(-39.0)#1,023
65.1483612.047.0(-35.0)#6551.0(-39.0)#1,027
66.1484612.047.0(-35.0)#6651.0(-39.0)#1,031
67.1123613.047.0(-34.0)#6751.0(-38.0)#1,048
68.1334013.047.0(-34.0)#6851.0(-38.0)#1,090
69.1308413.047.0(-34.0)#6951.0(-38.0)#1,126
70.1474413.047.0(-34.0)#7051.0(-38.0)#1,154
71.1482213.047.0(-34.0)#7151.0(-38.0)#1,194
72.1172614.047.0(-33.0)#7251.0(-37.0)#1,228
73.1303214.047.0(-33.0)#7351.0(-37.0)#1,241
74.1403114.047.0(-33.0)#7451.0(-37.0)#1,252
75.1193314.047.0(-33.0)#7551.0(-37.0)#1,268
76.1281614.047.0(-33.0)#7651.0(-37.0)#1,293
77.1214814.047.0(-33.0)#7751.0(-37.0)#1,295
78.1314014.047.0(-33.0)#7851.0(-37.0)#1,299
79.1308014.047.0(-33.0)#7951.0(-37.0)#1,308
80.1207414.047.0(-33.0)#8051.0(-37.0)#1,318
81.1207514.047.0(-33.0)#8151.0(-37.0)#1,319
82.1417014.047.0(-33.0)#8251.0(-37.0)#1,325
83.1447614.047.0(-33.0)#8351.0(-37.0)#1,326
84.1051114.047.0(-33.0)#8451.0(-37.0)#1,329
85.1405414.047.0(-33.0)#8551.0(-37.0)#1,342
86.1480314.047.0(-33.0)#8651.0(-37.0)#1,344
87.1412914.047.0(-33.0)#8751.0(-37.0)#1,348
88.1312214.047.0(-33.0)#8851.0(-37.0)#1,351
89.1486114.047.0(-33.0)#8951.0(-37.0)#1,353
90.1154515.047.0(-32.0)#9051.0(-36.0)#1,421
91.1257215.047.0(-32.0)#9151.0(-36.0)#1,432
92.1200915.047.0(-32.0)#9251.0(-36.0)#1,435
93.1202515.047.0(-32.0)#9351.0(-36.0)#1,450
94.1478715.047.0(-32.0)#9451.0(-36.0)#1,456
95.1400815.047.0(-32.0)#9551.0(-36.0)#1,498
96.1293515.047.0(-32.0)#9651.0(-36.0)#1,507
97.1365915.047.0(-32.0)#9751.0(-36.0)#1,548
98.1485016.047.0(-31.0)#9851.0(-35.0)#1,555
99.1455916.047.0(-31.0)#9951.0(-35.0)#1,569
100.1335016.047.0(-31.0)#10051.0(-35.0)#1,593
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