Bangladeshi vs Lithuanian Community Comparison

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Bangladeshi
Race
Ancestry
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian/Chaldean/SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianEuropeanFijianFilipinoFinnishFrenchFrench American IndianFrench CanadianGermanGerman RussianGhanaianGreekGuamanian/ChamorroGuatemalanGuyaneseHaitianHmongHonduranHopiHoumaHungarianIcelanderIndian (Asian)IndonesianInupiatIranianIraqiIrishIroquoisIsraeliItalianJamaicanJapaneseJordanianKenyanKiowaKoreanLaotianLatvianLebaneseLiberianLumbeeLuxembourgerMacedonianMalaysianMalteseMarshalleseMenomineeMexicanMexican 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
Lithuanian
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
Social Comparison
Social Comparison
Income
Poverty
Unemployment
Labor Participation
Family Structure
Vehicle Availability
Education Level
Disability

Social Comparison

Bangladeshis

Lithuanians

Fair
Excellent
2,611
SOCIAL INDEX
23.6/ 100
SOCIAL RATING
249th/ 347
SOCIAL RANK
8,827
SOCIAL INDEX
85.7/ 100
SOCIAL RATING
46th/ 347
SOCIAL RANK

Lithuanian Integration in Bangladeshi Communities

The statistical analysis conducted on geographies consisting of 127,692,632 people shows a weak negative correlation between the proportion of Lithuanians within Bangladeshi communities in the United States with a correlation coefficient (R) of -0.253. On average, for every 1% (one percent) increase in Bangladeshis within a typical geography, there is a decrease of 0.004% in Lithuanians. To illustrate, in a geography comprising of 100,000 individuals, a rise of 1,000 Bangladeshis corresponds to a decrease of 4.2 Lithuanians.
Bangladeshi Integration in Lithuanian Communities

Bangladeshi vs Lithuanian Income

When considering income, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in per capita income ($35,897 compared to $49,448, a difference of 37.8%), median male earnings ($46,744 compared to $61,228, a difference of 31.0%), and median family income ($88,358 compared to $115,395, a difference of 30.6%). Conversely, both communities are more comparable in terms of householder income under 25 years ($47,589 compared to $53,552, a difference of 12.5%), median female earnings ($35,960 compared to $42,108, a difference of 17.1%), and householder income over 65 years ($54,719 compared to $65,209, a difference of 19.2%).
Bangladeshi vs Lithuanian Income
Income MetricBangladeshiLithuanian
Per Capita Income
Tragic
$35,897
Exceptional
$49,448
Median Family Income
Tragic
$88,358
Exceptional
$115,395
Median Household Income
Tragic
$74,112
Exceptional
$93,852
Median Earnings
Tragic
$41,263
Exceptional
$50,991
Median Male Earnings
Tragic
$46,744
Exceptional
$61,228
Median Female Earnings
Tragic
$35,960
Exceptional
$42,108
Householder Age | Under 25 years
Tragic
$47,589
Exceptional
$53,552
Householder Age | 25 - 44 years
Tragic
$81,363
Exceptional
$105,223
Householder Age | 45 - 64 years
Tragic
$86,402
Exceptional
$112,484
Householder Age | Over 65 years
Tragic
$54,719
Exceptional
$65,209
Wage/Income Gap
Exceptional
22.2%
Tragic
28.7%

Bangladeshi vs Lithuanian Poverty

When considering poverty, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in receiving food stamps (15.0% compared to 9.7%, a difference of 55.6%), married-couple family poverty (6.0% compared to 4.0%, a difference of 51.7%), and family poverty (10.9% compared to 7.2%, a difference of 51.6%). Conversely, both communities are more comparable in terms of single male poverty (13.3% compared to 13.0%, a difference of 2.6%), seniors poverty over the age of 75 (12.0% compared to 10.6%, a difference of 13.2%), and single father poverty (15.2% compared to 17.3%, a difference of 13.8%).
Bangladeshi vs Lithuanian Poverty
Poverty MetricBangladeshiLithuanian
Poverty
Tragic
14.8%
Exceptional
10.5%
Families
Tragic
10.9%
Exceptional
7.2%
Males
Tragic
13.6%
Exceptional
9.5%
Females
Tragic
16.0%
Exceptional
11.4%
Females 18 to 24 years
Tragic
22.5%
Exceptional
18.7%
Females 25 to 34 years
Tragic
15.9%
Exceptional
12.2%
Children Under 5 years
Tragic
20.6%
Exceptional
15.2%
Children Under 16 years
Tragic
19.9%
Exceptional
13.5%
Boys Under 16 years
Tragic
20.0%
Exceptional
14.0%
Girls Under 16 years
Tragic
20.0%
Exceptional
13.9%
Single Males
Tragic
13.3%
Fair
13.0%
Single Females
Tragic
24.2%
Exceptional
19.2%
Single Fathers
Exceptional
15.2%
Tragic
17.3%
Single Mothers
Tragic
31.7%
Exceptional
27.4%
Married Couples
Tragic
6.0%
Exceptional
4.0%
Seniors Over 65 years
Fair
11.2%
Exceptional
9.1%
Seniors Over 75 years
Good
12.0%
Exceptional
10.6%
Receiving Food Stamps
Tragic
15.0%
Exceptional
9.7%

Bangladeshi vs Lithuanian Unemployment

When considering unemployment, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in unemployment among women with children ages 6 to 17 years (7.4% compared to 9.4%, a difference of 28.3%), male unemployment (5.7% compared to 5.0%, a difference of 14.1%), and unemployment (5.4% compared to 4.8%, a difference of 13.1%). Conversely, both communities are more comparable in terms of unemployment among ages 55 to 59 years (4.7% compared to 4.7%, a difference of 0.29%), unemployment among ages 30 to 34 years (5.3% compared to 5.4%, a difference of 0.39%), and unemployment among seniors over 65 years (5.2% compared to 5.1%, a difference of 0.86%).
Bangladeshi vs Lithuanian Unemployment
Unemployment MetricBangladeshiLithuanian
Unemployment
Poor
5.4%
Exceptional
4.8%
Males
Tragic
5.7%
Exceptional
5.0%
Females
Good
5.2%
Exceptional
4.7%
Youth < 25
Average
11.6%
Exceptional
11.3%
Age | 16 to 19 years
Exceptional
16.9%
Exceptional
16.7%
Age | 20 to 24 years
Exceptional
10.0%
Good
10.2%
Age | 25 to 29 years
Average
6.6%
Good
6.5%
Age | 30 to 34 years
Good
5.3%
Good
5.4%
Age | 35 to 44 years
Fair
4.8%
Exceptional
4.4%
Age | 45 to 54 years
Average
4.5%
Exceptional
4.3%
Age | 55 to 59 years
Exceptional
4.7%
Excellent
4.7%
Age | 60 to 64 years
Exceptional
4.6%
Excellent
4.8%
Age | 65 to 74 years
Good
5.3%
Fair
5.4%
Seniors > 65
Fair
5.2%
Average
5.1%
Seniors > 75
Tragic
9.6%
Tragic
9.9%
Women w/ Children < 6
Good
7.5%
Fair
7.8%
Women w/ Children 6 to 17
Exceptional
7.4%
Tragic
9.4%
Women w/ Children < 18
Good
5.3%
Exceptional
5.0%

Bangladeshi vs Lithuanian Labor Participation

When considering labor participation, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in in labor force | age 16-19 (42.5% compared to 40.4%, a difference of 5.0%), in labor force | age 45-54 (81.3% compared to 83.6%, a difference of 2.8%), and in labor force | age > 16 (65.9% compared to 64.8%, a difference of 1.7%). Conversely, both communities are more comparable in terms of in labor force | age 25-29 (85.1% compared to 85.8%, a difference of 0.86%), in labor force | age 20-64 (79.3% compared to 80.2%, a difference of 1.2%), and in labor force | age 35-44 (84.1% compared to 85.2%, a difference of 1.3%).
Bangladeshi vs Lithuanian Labor Participation
Labor Participation MetricBangladeshiLithuanian
In Labor Force | Age > 16
Exceptional
65.9%
Poor
64.8%
In Labor Force | Age 20-64
Poor
79.3%
Exceptional
80.2%
In Labor Force | Age 16-19
Exceptional
42.5%
Exceptional
40.4%
In Labor Force | Age 20-24
Exceptional
78.1%
Exceptional
77.0%
In Labor Force | Age 25-29
Exceptional
85.1%
Exceptional
85.8%
In Labor Force | Age 30-34
Tragic
84.3%
Exceptional
85.6%
In Labor Force | Age 35-44
Poor
84.1%
Exceptional
85.2%
In Labor Force | Age 45-54
Tragic
81.3%
Exceptional
83.6%

Bangladeshi vs Lithuanian Family Structure

When considering family structure, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in single mother households (8.1% compared to 5.4%, a difference of 49.8%), single father households (3.1% compared to 2.1%, a difference of 45.5%), and births to unmarried women (34.4% compared to 29.6%, a difference of 16.4%). Conversely, both communities are more comparable in terms of family households (64.3% compared to 64.0%, a difference of 0.32%), divorced or separated (12.3% compared to 11.7%, a difference of 4.7%), and average family size (3.37 compared to 3.10, a difference of 8.5%).
Bangladeshi vs Lithuanian Family Structure
Family Structure MetricBangladeshiLithuanian
Family Households
Average
64.3%
Fair
64.0%
Family Households with Children
Exceptional
30.1%
Tragic
26.6%
Married-couple Households
Tragic
43.5%
Exceptional
48.9%
Average Family Size
Exceptional
3.37
Tragic
3.10
Single Father Households
Tragic
3.1%
Exceptional
2.1%
Single Mother Households
Tragic
8.1%
Exceptional
5.4%
Currently Married
Tragic
43.7%
Exceptional
49.0%
Divorced or Separated
Poor
12.3%
Exceptional
11.7%
Births to Unmarried Women
Tragic
34.4%
Exceptional
29.6%

Bangladeshi vs Lithuanian Vehicle Availability

When considering vehicle availability, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in 4 or more vehicles in household (7.6% compared to 6.3%, a difference of 19.4%), 3 or more vehicles in household (21.9% compared to 20.1%, a difference of 8.7%), and no vehicles in household (8.7% compared to 8.4%, a difference of 2.7%). Conversely, both communities are more comparable in terms of 2 or more vehicles in household (58.4% compared to 58.2%, a difference of 0.33%), 1 or more vehicles in household (91.4% compared to 91.7%, a difference of 0.35%), and no vehicles in household (8.7% compared to 8.4%, a difference of 2.7%).
Bangladeshi vs Lithuanian Vehicle Availability
Vehicle Availability MetricBangladeshiLithuanian
No Vehicles Available
Exceptional
8.7%
Exceptional
8.4%
1+ Vehicles Available
Exceptional
91.4%
Exceptional
91.7%
2+ Vehicles Available
Exceptional
58.4%
Exceptional
58.2%
3+ Vehicles Available
Exceptional
21.9%
Excellent
20.1%
4+ Vehicles Available
Exceptional
7.6%
Average
6.3%

Bangladeshi vs Lithuanian Education Level

When considering education level, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in no schooling completed (3.5% compared to 1.4%, a difference of 146.5%), doctorate degree (1.2% compared to 2.3%, a difference of 86.5%), and professional degree (3.1% compared to 5.4%, a difference of 74.2%). Conversely, both communities are more comparable in terms of nursery school (96.6% compared to 98.6%, a difference of 2.1%), kindergarten (96.6% compared to 98.6%, a difference of 2.1%), and 1st grade (96.5% compared to 98.6%, a difference of 2.1%).
Bangladeshi vs Lithuanian Education Level
Education Level MetricBangladeshiLithuanian
No Schooling Completed
Tragic
3.5%
Exceptional
1.4%
Nursery School
Tragic
96.6%
Exceptional
98.6%
Kindergarten
Tragic
96.6%
Exceptional
98.6%
1st Grade
Tragic
96.5%
Exceptional
98.6%
2nd Grade
Tragic
96.5%
Exceptional
98.5%
3rd Grade
Tragic
96.3%
Exceptional
98.5%
4th Grade
Tragic
96.1%
Exceptional
98.3%
5th Grade
Tragic
95.9%
Exceptional
98.2%
6th Grade
Tragic
95.7%
Exceptional
98.1%
7th Grade
Tragic
94.5%
Exceptional
97.5%
8th Grade
Tragic
94.3%
Exceptional
97.3%
9th Grade
Tragic
93.4%
Exceptional
96.6%
10th Grade
Tragic
92.2%
Exceptional
95.8%
11th Grade
Tragic
90.9%
Exceptional
94.8%
12th Grade, No Diploma
Tragic
89.3%
Exceptional
93.6%
High School Diploma
Tragic
86.9%
Exceptional
92.0%
GED/Equivalency
Tragic
83.1%
Exceptional
88.9%
College, Under 1 year
Tragic
61.4%
Exceptional
68.8%
College, 1 year or more
Tragic
54.5%
Exceptional
62.9%
Associate's Degree
Tragic
40.0%
Exceptional
50.6%
Bachelor's Degree
Tragic
30.2%
Exceptional
42.2%
Master's Degree
Tragic
10.5%
Exceptional
17.7%
Professional Degree
Tragic
3.1%
Exceptional
5.4%
Doctorate Degree
Tragic
1.2%
Exceptional
2.3%

Bangladeshi vs Lithuanian Disability

When considering disability, the most significant differences between Bangladeshi and Lithuanian communities in the United States are seen in disability age 35 to 64 (13.6% compared to 10.8%, a difference of 26.1%), disability age 65 to 74 (26.8% compared to 21.4%, a difference of 25.5%), and disability age under 5 (1.3% compared to 1.6%, a difference of 21.2%). Conversely, both communities are more comparable in terms of disability age 5 to 17 (5.8% compared to 5.8%, a difference of 0.44%), male disability (12.0% compared to 11.6%, a difference of 3.5%), and ambulatory disability (6.3% compared to 6.0%, a difference of 4.5%).
Bangladeshi vs Lithuanian Disability
Disability MetricBangladeshiLithuanian
Disability
Tragic
12.6%
Poor
11.9%
Males
Tragic
12.0%
Tragic
11.6%
Females
Tragic
13.1%
Average
12.2%
Age | Under 5 years
Poor
1.3%
Tragic
1.6%
Age | 5 to 17 years
Tragic
5.8%
Tragic
5.8%
Age | 18 to 34 years
Tragic
7.4%
Tragic
7.0%
Age | 35 to 64 years
Tragic
13.6%
Excellent
10.8%
Age | 65 to 74 years
Tragic
26.8%
Exceptional
21.4%
Age | Over 75 years
Tragic
49.4%
Exceptional
45.1%
Vision
Tragic
2.3%
Exceptional
2.0%
Hearing
Tragic
3.2%
Tragic
3.4%
Cognitive
Tragic
18.6%
Exceptional
16.3%
Ambulatory
Poor
6.3%
Excellent
6.0%
Self-Care
Tragic
2.8%
Exceptional
2.4%