European vs Bangladeshi Community Comparison

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European
Race
Ancestry
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian/Chaldean/SyriacAustralianAustrianBahamianBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish 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
Immigration
NonimmigrantsImmigrantsAfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma/MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth AmericaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabweAzores
Bangladeshi
Race
Ancestry
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian/Chaldean/SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianFijianFilipinoFinnishFrenchFrench 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
Immigration
NonimmigrantsImmigrantsAfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma/MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth AmericaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabweAzores
Social Comparison
Social Comparison
Income
Poverty
Unemployment
Labor Participation
Family Structure
Vehicle Availability
Education Level
Disability

Social Comparison

Europeans

Bangladeshis

Good
Fair
8,117
SOCIAL INDEX
78.6/ 100
SOCIAL RATING
87th/ 347
SOCIAL RANK
2,611
SOCIAL INDEX
23.6/ 100
SOCIAL RATING
249th/ 347
SOCIAL RANK

Bangladeshi Integration in European Communities

The statistical analysis conducted on geographies consisting of 141,154,249 people shows a very strong positive correlation between the proportion of Bangladeshis within European communities in the United States with a correlation coefficient (R) of 0.878. On average, for every 1% (one percent) increase in Europeans within a typical geography, there is an increase of 0.233% in Bangladeshis. To illustrate, in a geography comprising of 100,000 individuals, a rise of 1,000 Europeans corresponds to an increase of 233.0 Bangladeshis.
European Integration in Bangladeshi Communities

European vs Bangladeshi Income

When considering income, the most significant differences between European and Bangladeshi communities in the United States are seen in wage/income gap (29.4% compared to 22.2%, a difference of 32.6%), per capita income ($45,836 compared to $35,897, a difference of 27.7%), and median male earnings ($57,637 compared to $46,744, a difference of 23.3%). Conversely, both communities are more comparable in terms of householder income under 25 years ($51,796 compared to $47,589, a difference of 8.8%), median female earnings ($39,457 compared to $35,960, a difference of 9.7%), and median earnings ($47,915 compared to $41,263, a difference of 16.1%).
European vs Bangladeshi Income
Income MetricEuropeanBangladeshi
Per Capita Income
Exceptional
$45,836
Tragic
$35,897
Median Family Income
Exceptional
$108,099
Tragic
$88,358
Median Household Income
Exceptional
$88,751
Tragic
$74,112
Median Earnings
Excellent
$47,915
Tragic
$41,263
Median Male Earnings
Exceptional
$57,637
Tragic
$46,744
Median Female Earnings
Fair
$39,457
Tragic
$35,960
Householder Age | Under 25 years
Fair
$51,796
Tragic
$47,589
Householder Age | 25 - 44 years
Excellent
$98,310
Tragic
$81,363
Householder Age | 45 - 64 years
Exceptional
$106,367
Tragic
$86,402
Householder Age | Over 65 years
Exceptional
$63,779
Tragic
$54,719
Wage/Income Gap
Tragic
29.4%
Exceptional
22.2%

European vs Bangladeshi Poverty

When considering poverty, the most significant differences between European and Bangladeshi communities in the United States are seen in receiving food stamps (9.5% compared to 15.0%, a difference of 58.5%), married-couple family poverty (4.2% compared to 6.0%, a difference of 42.3%), and family poverty (7.7% compared to 10.9%, a difference of 40.9%). Conversely, both communities are more comparable in terms of single male poverty (13.3% compared to 13.3%, a difference of 0.69%), single mother poverty (29.1% compared to 31.7%, a difference of 9.2%), and single father poverty (16.6% compared to 15.2%, a difference of 9.5%).
European vs Bangladeshi Poverty
Poverty MetricEuropeanBangladeshi
Poverty
Exceptional
11.3%
Tragic
14.8%
Families
Exceptional
7.7%
Tragic
10.9%
Males
Exceptional
10.2%
Tragic
13.6%
Females
Exceptional
12.3%
Tragic
16.0%
Females 18 to 24 years
Poor
20.4%
Tragic
22.5%
Females 25 to 34 years
Good
13.2%
Tragic
15.9%
Children Under 5 years
Exceptional
15.9%
Tragic
20.6%
Children Under 16 years
Exceptional
14.3%
Tragic
19.9%
Boys Under 16 years
Exceptional
14.5%
Tragic
20.0%
Girls Under 16 years
Exceptional
14.6%
Tragic
20.0%
Single Males
Tragic
13.3%
Tragic
13.3%
Single Females
Average
21.1%
Tragic
24.2%
Single Fathers
Poor
16.6%
Exceptional
15.2%
Single Mothers
Average
29.1%
Tragic
31.7%
Married Couples
Exceptional
4.2%
Tragic
6.0%
Seniors Over 65 years
Exceptional
9.2%
Fair
11.2%
Seniors Over 75 years
Exceptional
10.5%
Good
12.0%
Receiving Food Stamps
Exceptional
9.5%
Tragic
15.0%

European vs Bangladeshi Unemployment

When considering unemployment, the most significant differences between European and Bangladeshi communities in the United States are seen in male unemployment (4.8% compared to 5.7%, a difference of 19.2%), unemployment among women with children ages 6 to 17 years (8.7% compared to 7.4%, a difference of 18.9%), and unemployment (4.6% compared to 5.4%, a difference of 16.8%). Conversely, both communities are more comparable in terms of unemployment among women with children under 6 years (7.5% compared to 7.5%, a difference of 0.41%), unemployment among seniors over 75 years (9.7% compared to 9.6%, a difference of 0.59%), and unemployment among ages 60 to 64 years (4.5% compared to 4.6%, a difference of 1.1%).
European vs Bangladeshi Unemployment
Unemployment MetricEuropeanBangladeshi
Unemployment
Exceptional
4.6%
Poor
5.4%
Males
Exceptional
4.8%
Tragic
5.7%
Females
Exceptional
4.7%
Good
5.2%
Youth < 25
Exceptional
10.8%
Average
11.6%
Age | 16 to 19 years
Exceptional
16.1%
Exceptional
16.9%
Age | 20 to 24 years
Exceptional
9.7%
Exceptional
10.0%
Age | 25 to 29 years
Excellent
6.4%
Average
6.6%
Age | 30 to 34 years
Exceptional
5.2%
Good
5.3%
Age | 35 to 44 years
Exceptional
4.3%
Fair
4.8%
Age | 45 to 54 years
Exceptional
4.1%
Average
4.5%
Age | 55 to 59 years
Exceptional
4.5%
Exceptional
4.7%
Age | 60 to 64 years
Exceptional
4.5%
Exceptional
4.6%
Age | 65 to 74 years
Exceptional
5.1%
Good
5.3%
Seniors > 65
Exceptional
4.9%
Fair
5.2%
Seniors > 75
Tragic
9.7%
Tragic
9.6%
Women w/ Children < 6
Good
7.5%
Good
7.5%
Women w/ Children 6 to 17
Exceptional
8.7%
Exceptional
7.4%
Women w/ Children < 18
Exceptional
4.9%
Good
5.3%

European vs Bangladeshi Labor Participation

When considering labor participation, the most significant differences between European and Bangladeshi communities in the United States are seen in in labor force | age 16-19 (41.1% compared to 42.5%, a difference of 3.2%), in labor force | age > 16 (64.7% compared to 65.9%, a difference of 1.9%), and in labor force | age 45-54 (82.6% compared to 81.3%, a difference of 1.6%). Conversely, both communities are more comparable in terms of in labor force | age 20-64 (79.3% compared to 79.3%, a difference of 0.020%), in labor force | age 35-44 (84.1% compared to 84.1%, a difference of 0.020%), and in labor force | age 30-34 (84.3% compared to 84.3%, a difference of 0.050%).
European vs Bangladeshi Labor Participation
Labor Participation MetricEuropeanBangladeshi
In Labor Force | Age > 16
Tragic
64.7%
Exceptional
65.9%
In Labor Force | Age 20-64
Poor
79.3%
Poor
79.3%
In Labor Force | Age 16-19
Exceptional
41.1%
Exceptional
42.5%
In Labor Force | Age 20-24
Exceptional
77.1%
Exceptional
78.1%
In Labor Force | Age 25-29
Average
84.6%
Exceptional
85.1%
In Labor Force | Age 30-34
Tragic
84.3%
Tragic
84.3%
In Labor Force | Age 35-44
Poor
84.1%
Poor
84.1%
In Labor Force | Age 45-54
Fair
82.6%
Tragic
81.3%

European vs Bangladeshi Family Structure

When considering family structure, the most significant differences between European and Bangladeshi communities in the United States are seen in single mother households (5.7% compared to 8.1%, a difference of 42.4%), single father households (2.3% compared to 3.1%, a difference of 34.9%), and married-couple households (49.6% compared to 43.5%, a difference of 13.9%). Conversely, both communities are more comparable in terms of divorced or separated (12.2% compared to 12.3%, a difference of 0.38%), family households (65.0% compared to 64.3%, a difference of 1.2%), and average family size (3.14 compared to 3.37, a difference of 7.3%).
European vs Bangladeshi Family Structure
Family Structure MetricEuropeanBangladeshi
Family Households
Exceptional
65.0%
Average
64.3%
Family Households with Children
Exceptional
27.9%
Exceptional
30.1%
Married-couple Households
Exceptional
49.6%
Tragic
43.5%
Average Family Size
Tragic
3.14
Exceptional
3.37
Single Father Households
Excellent
2.3%
Tragic
3.1%
Single Mother Households
Exceptional
5.7%
Tragic
8.1%
Currently Married
Exceptional
49.3%
Tragic
43.7%
Divorced or Separated
Poor
12.2%
Poor
12.3%
Births to Unmarried Women
Excellent
30.2%
Tragic
34.4%

European vs Bangladeshi Vehicle Availability

When considering vehicle availability, the most significant differences between European and Bangladeshi communities in the United States are seen in no vehicles in household (7.1% compared to 8.7%, a difference of 21.8%), 2 or more vehicles in household (61.4% compared to 58.4%, a difference of 5.0%), and 3 or more vehicles in household (22.9% compared to 21.9%, a difference of 4.5%). Conversely, both communities are more comparable in terms of 4 or more vehicles in household (7.6% compared to 7.6%, a difference of 0.52%), 1 or more vehicles in household (93.0% compared to 91.4%, a difference of 1.8%), and 3 or more vehicles in household (22.9% compared to 21.9%, a difference of 4.5%).
European vs Bangladeshi Vehicle Availability
Vehicle Availability MetricEuropeanBangladeshi
No Vehicles Available
Exceptional
7.1%
Exceptional
8.7%
1+ Vehicles Available
Exceptional
93.0%
Exceptional
91.4%
2+ Vehicles Available
Exceptional
61.4%
Exceptional
58.4%
3+ Vehicles Available
Exceptional
22.9%
Exceptional
21.9%
4+ Vehicles Available
Exceptional
7.6%
Exceptional
7.6%

European vs Bangladeshi Education Level

When considering education level, the most significant differences between European and Bangladeshi communities in the United States are seen in no schooling completed (1.5% compared to 3.5%, a difference of 139.6%), doctorate degree (2.1% compared to 1.2%, a difference of 72.1%), and professional degree (4.8% compared to 3.1%, a difference of 56.2%). Conversely, both communities are more comparable in terms of nursery school (98.6% compared to 96.6%, a difference of 2.1%), kindergarten (98.6% compared to 96.6%, a difference of 2.1%), and 1st grade (98.5% compared to 96.5%, a difference of 2.1%).
European vs Bangladeshi Education Level
Education Level MetricEuropeanBangladeshi
No Schooling Completed
Exceptional
1.5%
Tragic
3.5%
Nursery School
Exceptional
98.6%
Tragic
96.6%
Kindergarten
Exceptional
98.6%
Tragic
96.6%
1st Grade
Exceptional
98.5%
Tragic
96.5%
2nd Grade
Exceptional
98.5%
Tragic
96.5%
3rd Grade
Exceptional
98.4%
Tragic
96.3%
4th Grade
Exceptional
98.3%
Tragic
96.1%
5th Grade
Exceptional
98.2%
Tragic
95.9%
6th Grade
Exceptional
98.0%
Tragic
95.7%
7th Grade
Exceptional
97.3%
Tragic
94.5%
8th Grade
Exceptional
97.1%
Tragic
94.3%
9th Grade
Exceptional
96.4%
Tragic
93.4%
10th Grade
Exceptional
95.5%
Tragic
92.2%
11th Grade
Exceptional
94.4%
Tragic
90.9%
12th Grade, No Diploma
Exceptional
93.1%
Tragic
89.3%
High School Diploma
Exceptional
91.4%
Tragic
86.9%
GED/Equivalency
Exceptional
87.9%
Tragic
83.1%
College, Under 1 year
Exceptional
68.2%
Tragic
61.4%
College, 1 year or more
Exceptional
61.8%
Tragic
54.5%
Associate's Degree
Excellent
48.2%
Tragic
40.0%
Bachelor's Degree
Excellent
39.5%
Tragic
30.2%
Master's Degree
Excellent
15.8%
Tragic
10.5%
Professional Degree
Exceptional
4.8%
Tragic
3.1%
Doctorate Degree
Exceptional
2.1%
Tragic
1.2%

European vs Bangladeshi Disability

When considering disability, the most significant differences between European and Bangladeshi communities in the United States are seen in self-care disability (2.4% compared to 2.8%, a difference of 18.7%), disability age under 5 (1.5% compared to 1.3%, a difference of 18.1%), and disability age 35 to 64 (11.7% compared to 13.6%, a difference of 16.6%). Conversely, both communities are more comparable in terms of disability age 5 to 17 (5.8% compared to 5.8%, a difference of 0.080%), disability age 18 to 34 (7.4% compared to 7.4%, a difference of 0.52%), and male disability (12.1% compared to 12.0%, a difference of 0.53%).
European vs Bangladeshi Disability
Disability MetricEuropeanBangladeshi
Disability
Tragic
12.3%
Tragic
12.6%
Males
Tragic
12.1%
Tragic
12.0%
Females
Poor
12.4%
Tragic
13.1%
Age | Under 5 years
Tragic
1.5%
Poor
1.3%
Age | 5 to 17 years
Tragic
5.8%
Tragic
5.8%
Age | 18 to 34 years
Tragic
7.4%
Tragic
7.4%
Age | 35 to 64 years
Poor
11.7%
Tragic
13.6%
Age | 65 to 74 years
Good
23.2%
Tragic
26.8%
Age | Over 75 years
Exceptional
46.7%
Tragic
49.4%
Vision
Fair
2.2%
Tragic
2.3%
Hearing
Tragic
3.5%
Tragic
3.2%
Cognitive
Exceptional
17.0%
Tragic
18.6%
Ambulatory
Fair
6.2%
Poor
6.3%
Self-Care
Exceptional
2.4%
Tragic
2.8%