Educational Attainment in the Golden Township, Walsh County, North Dakota (Township)

Educational Attainment#1

Highest level of education among people aged 25 years and older.
Scope: population of Walsh County and the Golden Township
Golden Township
Walsh County
0%10%20%30%40%50%CountHigher Degree1H.S. Diploma2No H.S. Diploma250.925926%29.287015%50.925926%50.9%5545.370370%57.598231%45.370370%45.4%493.703704%13.114754%3.703704%3.7%4
Count number of people in category
1Post-Secondary Degree 2H.S. = High School

Relative Educational Attainment#2

Highest level of education among people aged 25 years and older, as percentage more or less than Walsh County at large.
Scope: population of Walsh County and the Golden Township
-50%0%+50%%ref.Higher Degree1H.S. Diploma2No H.S. Diploma273.885680%73.885680%+73.9%50.926%50.9%29.287%29.3%-21.229576%-21.229576%-21.2%45.370%45.4%57.598%57.6%-71.759259%-71.759259%-71.8%3.704%3.70%13.115%13.1%
% percentage with given level in the Golden Township
ref. percentage with given level in Walsh County
1Post-Secondary Degree 2H.S. = High School

Detailed Educational Attainment#3

Highest level of education among people aged 25 years and older.
Scope: population of Walsh County and the Golden Township
Golden Township
Walsh County
0%10%20%30%CountDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None0.000000%0.000000%0.0%0.481395%04.629630%0.962789%4.629630%4.6%53.703704%2.966432%3.703704%3.7%428.703704%12.308093%28.703704%28.7%3113.888889%12.568306%13.888889%13.9%156.481481%20.947177%6.481481%6.5%738.888889%36.651054%38.888889%38.9%423.703704%6.362217%3.703704%3.7%40.000000%0.000000%0.0%5.659641%00.000000%0.000000%0.0%1.092896%0
Count number of people in category
1Degree 2Diploma or equivialent 3H.S. = High School

Detailed Relative Educational Attainment#4

Highest level of education among people aged 25 years and older, as percentage more or less than Walsh County at large.
Scope: population of Walsh County and the Golden Township
0%%ref.Doctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None-100.000000%-100.000000%-100.0%0.000%0%0.481%0.48%380.855856%380.855856%+380.9%4.630%4.63%0.963%0.96%24.853801%24.853801%+24.9%3.704%3.70%2.966%2.97%133.210007%133.210007%+133.2%28.704%28.7%12.308%12.3%10.507246%10.507246%+10.5%13.889%13.9%12.568%12.6%-69.057971%-69.057971%-69.1%6.481%6.48%20.947%20.9%6.105786%6.105786%+6.1%38.889%38.9%36.651%36.7%-41.785958%-41.785958%-41.8%3.704%3.70%6.362%6.36%-100.000000%-100.000000%-100.0%0.000%0%5.660%5.66%-100.000000%-100.000000%-100.0%0.000%0%1.093%1.09%
% percentage with given level in the Golden Township
ref. percentage with given level in Walsh County
1Degree 2Diploma or equivialent 3H.S. = High School

Educational Attainment Sex Ratio#5

Percentage more likely men or women are to have various highest levels of education among people aged 25 years and older.
Scope: population of Walsh County and the Golden Township
More Females More Males
Walsh County
Golden Township
1,000%500%0%FMHigher Degree1H.S. Diploma2No H.S. Diploma2-12.193682%7.482517%7.482517%7%49.057%49.1%52.727%52.7%-1.056163%8.932806%8.932806%9%43.396%43.4%47.273%47.3%-1,000.000000%-1,000.000000%> 1000%35.743287%7.547%7.55%0.000%0%
F females in category as a percentage of all females
M males in category as a percentage of all males
1Post-Secondary Degree 2H.S. = High School

Detailed Educational Attainment Sex Ratio#6

Percentage more likely men or women are to have various highest levels of education among people aged 25 years and older.
Scope: population of Walsh County and the Golden Township
More Females More Males
Walsh County
Golden Township
1k%0k%1k%FMProfessional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.31,000.000000%22.445065%1,000.000000%> 1000%0.000%0%9.091%9.09%-32.215735%1,000.000000%1,000.000000%> 1000%0.000%0%7.273%7.27%-117.924528%-21.243534%-117.924528%0k%39.623%39.6%18.182%18.2%-9.323468%92.727273%92.727273%0k%9.434%9.43%18.182%18.2%-38.364780%-14.050979%-38.364780%0k%7.547%7.55%5.455%5.45%16.650718%6.033813%16.650718%0k%35.849%35.8%41.818%41.8%-1,000.000000%-1,000.000000%> 1000%19.512654%7.547%7.55%0.000%0%
F females in category as a percentage of all females
M males in category as a percentage of all males
1Degree 2Diploma or equivialent 3H.S. = High School

Bachelor's Degrees By Age#7

Percentage of age cohort whose highest degree is a Bachelor's.
Scope: population of Walsh County and the Golden Township
Golden Township
Walsh County
0%10%20%30%40%50%B.X.Total65+45-6435-4425-340.000000%0.000000%0.0%8.553792%02327.272727%11.694291%27.272727%27.3%124451.612903%19.613260%51.612903%51.6%163130.000000%14.711359%30.000000%30.0%310
B.X. number of people with a Bachelor's degree
Total number of people in age cohort

Relative Bachelor's Degrees By Age#8

Percentage of population whose highest degree is a Bachelor's, as a percentage more or less than Walsh County at large.
Scope: population of Walsh County and the Golden Township
100%50%0%50%100%150%%ref.65+45-6435-4425-34-100.000000%-100.000000%100.0%0.000%0%8.554%8.55%133.214030%133.214030%133.2%27.273%27.3%11.694%11.7%163.153112%163.153112%163.2%51.613%51.6%19.613%19.6%103.924051%103.924051%103.9%30.000%30.0%14.711%14.7%
% percentage in the Golden Township with a Bachelor's degree
ref percentage in Walsh County with a Bachelor's degree

Bachelor's Degrees Sex Ratio By Age#9

Percentage more likely men or women are to have their highest degree be a Bachelor's.
Scope: population of Walsh County and the Golden Township
More Females More Males
Walsh County
Golden Township
1,000%500%0%FM45-6435-4425-34-433.333333%-72.635694%-433.333333%433.3%66.667%66.7%12.500%12.5%-77.777778%-99.539347%-77.777778%77.8%66.667%66.7%37.500%37.5%-1,000.000000%-1,000.000000%> 1000%12.777904%100.000%100%0.000%0%
F female degree holders as a percentage of all females
M male degree holders as a percentage of all males

Median Earnings by Educational Attainment#10

Among population 25 years old and over with earnings.
Scope: population of Walsh County and the Golden Township
Golden Township
Walsh County
$0k$20k$40kCount%Bachelor's DegreeSome CollegeH.S. Diploma2Total$33,438.000000$48,313.000000$33,438.000000$33.4k3128.704%28.7%$42,000.000000$37,165.000000$42,000.000000$42.0k2220.370%20.4%$19,286.000000$27,253.000000$19,286.000000$19.3k4238.889%38.9%$27,321.000000$34,047.000000$27,321.000000$27.3k108100.000%100%
Count number of people with given highest level of educational attainment
% percentage of people with given highest level of educational attainment
1or professional degree 2H.S. = High School

Median Earnings by Educational Attainment#11

By sex among population 25 years old and over with earnings.
Scope: population of Walsh County and the Golden Township
Female Male
Walsh County
Golden Township
Shaded bar tips show excess over facing bar.
$20k$10k$0k$10k$20k$30k$40kFMTotal$-17,019.000000$-27,238.000000$-17,019.000000$17.0k$17,019.000000$19,856.000000$27,238.000000$13,229.000000$36,875.000000$36.9k5355
F females with given highest level of educational attainment
M males with given highest level of educational attainment
1or professional degree 2H.S. = High School

Employment by Educational Attainment#12

Percentage of population that is employed by highest level of educational attainment among the population aged 25 to 64 years old.
Scope: population of Walsh County and the Golden Township
Golden Township
Walsh County
0%20%40%60%80%100%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma387.500000%87.371663%87.500000%87.5%35100.000000%88.335046%100.000000%100.0%18100.000000%77.548919%100.000000%100.0%230.000000%0.000000%0.0%56.115108%0
Count employed people with given educational attainment aged 25 to 64 years old
1or higher 2two or four year institution 3H.S. = High School

Lacking High School Diploma By Race#13

Percent of racial or ethnic group lacking a high school diploma (or equivalent).
Scope: population of Walsh County and the Golden Township
Female Male
Walsh County
Golden Township
Shaded bar tips show excess over facing bar.
5%0%5%10%MFWhite1-0.000000%-7.547170%-9.173789%-7.547170%7.5%0.000000%9.173789%3.027650%0.000000%0.0%04
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

College Graduates By Race#14

Percent of racial or ethnic group with a bachelor's degree or higher.
Scope: population of Walsh County and the Golden Township
Female Male
Walsh County
Golden Township
Shaded bar tips show excess over facing bar.
30%20%10%0%10%20%30%MFWhite1-34.545455%-5.077187%-17.122302%-0.997356%-39.622642%39.6%34.545455%17.122302%34.545455%34.5%1921
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

Map of Educational Attainment by Tract in the Golden Township

Coarse: Post-Secondary Degree Educational Attainment by Tract#15

Percentage of the population 25 years and older with given highest level of educational attainment
33.5%34.0%34.5%35.0%35.5%36.1%

Coarse: High School Diploma Educational Attainment by Tract#16

Percentage of the population 25 years and older with given highest level of educational attainment
51.0%52.2%53.3%54.4%55.5%56.7%

Coarse: No High School Diploma Educational Attainment by Tract#17

Percentage of the population 25 years and older with given highest level of educational attainment
7.3%9.0%10.6%12.2%13.8%15.5%

Detailed: Doctorate Degree Educational Attainment by Tract#18

Percentage of the population 25 years and older with given highest level of educational attainment
0.067%0.077%0.085%0.094%0.103%0.112%

Detailed: Professional Degree Educational Attainment by Tract#19

Percentage of the population 25 years and older with given highest level of educational attainment
1.00%1.15%1.31%1.46%1.61%1.77%

Detailed: Master's Degree Educational Attainment by Tract#20

Percentage of the population 25 years and older with given highest level of educational attainment
2.11%2.57%3.03%3.49%3.95%4.41%

Detailed: Bachelor's Degree Educational Attainment by Tract#21

Percentage of the population 25 years and older with given highest level of educational attainment
15.06%15.43%15.80%16.17%16.54%16.91%

Detailed: Associate's Degree Educational Attainment by Tract#22

Percentage of the population 25 years and older with given highest level of educational attainment
12.2%13.0%13.7%14.4%15.2%16.0%

Detailed: Some College Educational Attainment by Tract#23

Percentage of the population 25 years and older with given highest level of educational attainment
18.8%20.4%21.9%23.4%24.9%26.4%

Detailed: High School Diploma or Equivalent Educational Attainment by Tract#24

Percentage of the population 25 years and older with given highest level of educational attainment
30.25%30.65%31.04%31.44%31.83%32.23%

Detailed: Some High School Educational Attainment by Tract#25

Percentage of the population 25 years and older with given highest level of educational attainment
4.0%4.9%5.8%6.7%7.6%8.5%

Detailed: Less than High School Educational Attainment by Tract#26

Percentage of the population 25 years and older with given highest level of educational attainment
3.3%4.0%4.7%5.4%6.1%6.9%

Detailed: None Educational Attainment by Tract#27

Percentage of the population 25 years and older with given highest level of educational attainment
0.000%0.014%0.027%0.041%0.054%0.068%
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Educational Attainment by County Subdivision in North Dakota

There are 1,763 county subdivisions in North Dakota. This section compares the Golden Township to the 50 most populous county subdivisions in North Dakota and to those entities that contain or substantially overlap with the Golden Township. The least populous of the compared county subdivisions has a population of 1,474.

No H.S. Diploma by County Subdivision#28

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Golden Township, selected other county subdivisions in North Dakota, and entities that contain the Golden Township
0%5%10%15%20%Count#Fort TottenHarveyNorth RoletteTurtle MountainTurtle MtnFort YatesNorth SiouxOakesGraftonWalsh CountyWalshUnited States of AmericaUnited StatesBowmanZIP Code 58270ZIP 58270RugbyHillsboroDevils LakeCarringtonValley CityLangdonMidwestBeulahJamestownDickinsonWest North CentralWahpetonNew TownFort BertholdHarrison TownshipHarrisonBottineauLisbonNorth DakotaMandanHazenTatman TownshipTatmanBismarckDickinson NorthDickinson NWillistonNedrose TownshipNedroseMinotGrand ForksApple Creek TownshipApple CrkFargoLincolnMayvilleWatford CityStanleyEast MortonMandanWest FargoHoraceCasseltonGolden TownshipGoldenGibbs TownshipGibbsWaterford TownshipWaterfordHay Creek TownshipHay CrkLincoln-Fort RiceBurnt Creek-RiverviewBurnt Crk-RiverviewMekinock TownshipMekinock23.289474%23.289474%23.3%177120.692194%20.692194%20.7%281217.218225%17.218225%17.2%359316.925287%16.925287%16.9%589416.347826%16.347826%16.3%188515.766423%15.766423%15.8%108613.894888%13.894888%13.9%193713.421679%13.421679%13.4%395813.114754%13.114754%13.1%1,00813.020590%13.020590%13.0%27,818,38027.8M12.964564%12.964564%13.0%150912.536232%12.536232%12.5%17312.516945%12.516945%12.5%2771011.692845%11.692845%11.7%1341111.463705%11.463705%11.5%5781211.317009%11.317009%11.3%1711310.545611%10.545611%10.5%4891410.534125%10.534125%10.5%1421510.244520%10.244520%10.2%4,641,8354.64M10.240964%10.240964%10.2%2381610.188895%10.188895%10.2%1,068179.341662%9.341662%9.3%1,331189.174633%9.174633%9.2%1,278,0511.28M9.167205%9.167205%9.2%426199.077381%9.077381%9.1%122208.935611%8.935611%8.9%68218.784177%8.784177%8.8%151228.591282%8.591282%8.6%136238.281573%8.281573%8.3%120247.963451%7.963451%8.0%38,03438.0k7.836290%7.836290%7.8%1,122257.608696%7.608696%7.6%133267.274590%7.274590%7.3%71277.263281%7.263281%7.3%3,425287.084469%7.084469%7.1%156297.076386%7.076386%7.1%1,095307.053395%7.053395%7.1%107316.809689%6.809689%6.8%2,041326.196399%6.196399%6.2%1,958336.142241%6.142241%6.1%114346.055621%6.055621%6.1%4,270355.963529%5.963529%6.0%121365.716963%5.716963%5.7%61375.670471%5.670471%5.7%159385.256242%5.256242%5.3%80395.124378%5.124378%5.1%103404.337539%4.337539%4.3%55414.320296%4.320296%4.3%887424.259043%4.259043%4.3%73434.102564%4.102564%4.1%64443.703704%3.703704%3.7%43.098592%3.098592%3.1%44452.619693%2.619693%2.6%29461.895551%1.895551%1.9%49471.573677%1.573677%1.6%33481.125083%1.125083%1.1%17491.084011%1.084011%1.1%1250
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#29

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Golden Township, selected other county subdivisions in North Dakota, and entities that contain the Golden Township
0%10%20%30%40%Count#Lincoln-Fort RiceBurnt Creek-RiverviewBurnt Crk-RiverviewHoraceHay Creek TownshipHay CrkFargoGolden TownshipGoldenWest FargoMayvilleApple Creek TownshipApple CrkGrand ForksBismarckWaterford TownshipWaterfordCasseltonDickinson NorthDickinson NUnited States of AmericaUnited StatesWest North CentralMekinock TownshipMekinockMidwestHarrison TownshipHarrisonNorth DakotaMandanMinotGibbs TownshipGibbsTatman TownshipTatmanHillsboroLincolnMandanStanleyTurtle MountainTurtle MtnCarringtonJamestownDickinsonValley CityBowmanHazenOakesWatford CityWillistonWahpetonNew TownHarveyBottineauEast MortonNedrose TownshipNedroseZIP Code 58270ZIP 58270LangdonDevils LakeFort BertholdWalsh CountyWalshRugbyFort YatesBeulahNorth SiouxNorth RoletteLisbonGraftonFort Totten43.156891%43.156891%43.2%905142.091330%42.091330%42.1%636239.439907%39.439907%39.4%676338.916828%38.916828%38.9%1,006438.241175%38.241175%38.2%26,96527.0k537.037037%37.037037%37.0%4036.554479%36.554479%36.6%7,505635.520150%35.520150%35.5%379735.183190%35.183190%35.2%653835.039084%35.039084%35.0%11,07211.1k934.505355%34.505355%34.5%16,27116.3k1034.236676%34.236676%34.2%3791132.500000%32.500000%32.5%5071231.834696%31.834696%31.8%7011330.315023%30.315023%30.3%64,767,78764.8M30.056536%30.056536%30.1%4,186,9564.19M29.177958%29.177958%29.2%3231428.952816%28.952816%29.0%13,118,64213.1M28.388598%28.388598%28.4%4881528.172535%28.172535%28.2%134,554135k27.727336%27.727336%27.7%3,9701627.045242%27.045242%27.0%8,1061726.549296%26.549296%26.5%3771826.331967%26.331967%26.3%2571926.265271%26.265271%26.3%3012025.874815%25.874815%25.9%5252125.867508%25.867508%25.9%3282225.427070%25.427070%25.4%3872325.287356%25.287356%25.3%8802424.751820%24.751820%24.8%3742524.699485%24.699485%24.7%2,5892624.698203%24.698203%24.7%3,5192724.412336%24.412336%24.4%1,1322824.373379%24.373379%24.4%2822924.141876%24.141876%24.1%4223023.974082%23.974082%24.0%3333123.930100%23.930100%23.9%6713223.859377%23.859377%23.9%3,6923321.863568%21.863568%21.9%1,0163421.577381%21.577381%21.6%2903521.060383%21.060383%21.1%2863620.404296%20.404296%20.4%3233720.248756%20.248756%20.2%4073820.105471%20.105471%20.1%3053919.202899%19.202899%19.2%26518.694362%18.694362%18.7%2524018.187227%18.187227%18.2%9174117.082786%17.082786%17.1%1304216.718709%16.718709%16.7%1,28516.357885%16.357885%16.4%3624316.347826%16.347826%16.3%1884415.920826%15.920826%15.9%3704515.912409%15.912409%15.9%1094615.635492%15.635492%15.6%3264714.354727%14.354727%14.4%2084813.081889%13.081889%13.1%385498.684211%8.684211%8.7%6650
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#30

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Golden Township, selected other county subdivisions in North Dakota, and entities that contain the Golden Township
0%1%2%3%4%5%Count#Apple Creek TownshipApple CrkBurnt Creek-RiverviewBurnt Crk-RiverviewGolden TownshipGoldenGrand ForksMandanFargoHay Creek TownshipHay CrkOakesDickinson NorthDickinson NBismarckUnited States of AmericaUnited StatesMayvilleWest North CentralMidwestWest FargoFort BertholdZIP Code 58270ZIP 58270StanleyCasseltonDickinsonLincoln-Fort RiceHoraceNorth DakotaValley CityMandanWillistonMekinock TownshipMekinockHarrison TownshipHarrisonEast MortonWahpetonMinotWatford CityFort YatesGibbs TownshipGibbsBeulahDevils LakeNew TownJamestownLisbonWalsh CountyWalshTatman TownshipTatmanRugbyLincolnBottineauNorth SiouxHillsboroNedrose TownshipNedroseGraftonNorth RoletteFort TottenBowmanHarveyTurtle MountainTurtle MtnCarringtonLangdonHazenWaterford TownshipWaterford5.280172%5.280172%5.3%9814.831238%4.831238%4.8%7324.629630%4.629630%4.6%54.436849%4.436849%4.4%1,40234.416404%4.416404%4.4%5644.336789%4.336789%4.3%3,05853.907157%3.907157%3.9%10163.743701%3.743701%3.7%5273.496821%3.496821%3.5%7783.437599%3.437599%3.4%1,62193.336158%3.336158%3.3%7,127,6737.13M3.092784%3.092784%3.1%33102.978981%2.978981%3.0%414,980415k2.893416%2.893416%2.9%1,311,0191.31M2.829867%2.829867%2.8%581112.759527%2.759527%2.8%21122.681159%2.681159%2.7%372.628121%2.628121%2.6%40132.564103%2.564103%2.6%40142.484559%2.484559%2.5%354152.479733%2.479733%2.5%52162.450408%2.450408%2.5%42172.435475%2.435475%2.4%11,63211.6k2.350658%2.350658%2.4%109182.213996%2.213996%2.2%317192.177847%2.177847%2.2%337202.168022%2.168022%2.2%24211.977894%1.977894%2.0%34221.940299%1.940299%1.9%39231.936733%1.936733%1.9%90241.898439%1.898439%1.9%569251.854494%1.854494%1.9%52261.652174%1.652174%1.7%19271.619718%1.619718%1.6%23281.592083%1.592083%1.6%37291.566839%1.566839%1.6%79301.562500%1.562500%1.6%21311.469185%1.469185%1.5%154321.449275%1.449275%1.4%21331.444184%1.444184%1.4%1111.434426%1.434426%1.4%14341.355626%1.355626%1.4%30351.330705%1.330705%1.3%27361.263424%1.263424%1.3%20371.167883%1.167883%1.2%8381.134380%1.134380%1.1%13390.988794%0.988794%1.0%15400.985389%0.985389%1.0%29410.959233%0.959233%1.0%20420.921053%0.921053%0.9%7430.864304%0.864304%0.9%10440.810015%0.810015%0.8%11450.775862%0.775862%0.8%27460.727995%0.727995%0.7%11470.667656%0.667656%0.7%9480.286041%0.286041%0.3%5490.000000%0.000000%0.0%050
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#31

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in North Dakota, and entities that contain the Golden Township
Female
Male
10x5x0x5x10xFM#Hay CrkLincoln-Fort RiceGibbsWaterfordGraftonDickinson NNew TownHoraceEast MortonTatmanApple CrkJamestownWest FargoWahpetonDevils LakeHarrisonLangdonOakesWalshFort TottenTurtle MtnBowmanMandanNorth DakotaRugbyFargoValley CityNorth SiouxNorth RoletteWest North CentralMidwestBismarckUnited StatesDickinsonHillsboroBottineauHarveyFort BertholdWillistonMinotWatford CityStanleyGrand ForksBurnt Crk-RiverviewNedroseLisbonHazenFort YatesCarringtonBeulahCasseltonMayvilleMekinockLincolnZIP 58270MandanGolden-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%3.606%3.61%1-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.202%0.20%2.800%2.80%2-1.000000x-1.000000x6.209670x6.209670x6.21x0.853%0.85%5.300%5.30%3-1.000000x-1.000000x4.653667x4.653667x4.65x0.988%0.99%4.600%4.60%4-1.000000x-1.000000x2.731209x2.731209x2.73x7.262%7.26%19.834%19.8%5-1.000000x-1.000000x2.690088x2.690088x2.69x3.803%3.80%10.231%10.2%6-1.000000x-1.000000x2.563883x2.563883x2.56x5.038%5.04%12.917%12.9%7-1.000000x-1.000000x2.255349x2.255349x2.26x2.570%2.57%5.797%5.80%8-1.000000x-1.000000x2.202686x2.202686x2.20x3.141%3.14%6.919%6.92%9-1.000000x-1.000000x2.140078x2.140078x2.14x4.545%4.55%9.728%9.73%10-1.000000x-1.000000x1.780041x1.780041x1.78x4.348%4.35%7.739%7.74%11-1.000000x-1.000000x1.692780x1.692780x1.69x7.561%7.56%12.800%12.8%12-1.000000x-1.000000x1.676716x1.676716x1.68x3.219%3.22%5.398%5.40%13-1.000000x-1.000000x1.662931x1.662931x1.66x6.902%6.90%11.478%11.5%14-1.000000x-1.000000x1.464176x1.464176x1.46x9.313%9.31%13.636%13.6%15-1.000000x-1.000000x1.420197x1.420197x1.42x7.115%7.11%10.104%10.1%16-1.000000x-1.000000x1.407999x1.407999x1.41x8.836%8.84%12.441%12.4%17-1.000000x-1.000000x1.388510x1.388510x1.39x11.699%11.7%16.244%16.2%18-1.000000x-1.000000x1.357433x1.357433x1.36x11.114%11.1%15.087%15.1%-1.000000x-1.000000x1.300927x1.300927x1.30x20.398%20.4%26.536%26.5%19-1.000000x-1.000000x1.266483x1.266483x1.27x15.130%15.1%19.161%19.2%20-1.000000x-1.000000x1.230012x1.230012x1.23x11.655%11.7%14.336%14.3%21-1.000000x-1.000000x1.229407x1.229407x1.23x7.022%7.02%8.633%8.63%22-1.000000x-1.000000x1.216853x1.216853x1.22x7.174%7.17%8.730%8.73%-1.000000x-1.000000x1.190748x1.190748x1.19x11.431%11.4%13.612%13.6%23-1.000000x-1.000000x1.176963x1.176963x1.18x5.565%5.56%6.550%6.55%24-1.000000x-1.000000x1.175263x1.175263x1.18x9.732%9.73%11.438%11.4%25-1.000000x-1.000000x1.169870x1.169870x1.17x14.601%14.6%17.081%17.1%26-1.000000x-1.000000x1.143825x1.143825x1.14x16.012%16.0%18.315%18.3%27-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.119570x1.119570x1.12x6.866%6.87%7.687%7.69%28-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.108378x1.108378x1.11x8.835%8.83%9.792%9.79%29-1.000000x-1.000000x1.045752x1.045752x1.05x11.460%11.5%11.984%12.0%30-1.000000x-1.000000x1.036533x1.036533x1.04x8.455%8.46%8.764%8.76%31-1.000000x-1.000000x1.030884x1.030884x1.03x20.405%20.4%21.036%21.0%32-1.000000x-1.000000x1.019603x1.019603x1.02x8.847%8.85%9.021%9.02%33-1.039914x-1.039914x1.04x1.000000x1.000000x7.229%7.23%6.952%6.95%34-1.078903x-1.078903x1.08x1.000000x1.000000x7.074%7.07%6.556%6.56%35-1.106367x-1.106367x1.11x1.000000x1.000000x5.970%5.97%5.396%5.40%36-1.154347x-1.154347x1.15x1.000000x1.000000x5.692%5.69%4.931%4.93%37-1.190445x-1.190445x1.19x1.000000x1.000000x6.744%6.74%5.665%5.67%38-1.197234x-1.197234x1.20x1.000000x1.000000x1.230%1.23%1.027%1.03%39-1.245115x-1.245115x1.25x1.000000x1.000000x7.748%7.75%6.223%6.22%40-1.276253x-1.276253x1.28x1.000000x1.000000x9.235%9.23%7.236%7.24%41-1.356773x-1.356773x1.36x1.000000x1.000000x8.792%8.79%6.480%6.48%42-1.391755x-1.391755x1.39x1.000000x1.000000x19.107%19.1%13.729%13.7%43-1.467442x-1.467442x1.47x1.000000x1.000000x13.359%13.4%9.103%9.10%44-1.498616x-1.498616x1.50x1.000000x1.000000x12.236%12.2%8.165%8.17%45-1.706563x-1.706563x1.71x1.000000x1.000000x5.144%5.14%3.014%3.01%46-1.811224x-1.811224x1.81x1.000000x1.000000x7.466%7.47%4.122%4.12%47-1.947300x-1.947300x1.95x1.000000x1.000000x1.512%1.51%0.776%0.78%48-2.073921x-2.073921x2.07x1.000000x1.000000x8.187%8.19%3.947%3.95%49-2.520889x-2.520889x2.52x1.000000x1.000000x17.385%17.4%6.897%6.90%-3.736264x-3.736264x3.74x1.000000x1.000000x7.143%7.14%1.912%1.91%50-10.000000x-10.000000x> 10.0x1.000000x1.000000x7.547%7.55%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#32

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in North Dakota, and entities that contain the Golden Township
Female
Male
10x5x0x5x10xFM#GoldenLincoln-Fort RiceHazenStanleyCarringtonLangdonMandanFort TottenWahpetonDickinson NBurnt Crk-RiverviewMandanBeulahFort YatesNedroseWillistonBottineauHillsboroZIP 58270Valley CityBowmanWest FargoJamestownTatmanBismarckOakesWalshFort BertholdWatford CityMayvilleGraftonNorth DakotaGrand ForksFargoMidwestWest North CentralUnited StatesHarveyDickinsonApple CrkLisbonMinotCasseltonDevils LakeGibbsMekinockNorth SiouxRugbyNorth RoletteHoraceHay CrkEast MortonLincolnHarrisonNew TownTurtle Mtn-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%9.091%9.09%-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%4.697%4.70%1-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%0.559%0.56%2-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%4.587%4.59%3-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.517%1.52%4-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.417%1.42%5-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%8.235%8.24%6-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.955%1.96%7-1.000000x-1.000000x4.381867x4.381867x4.38x0.724%0.72%3.174%3.17%8-1.000000x-1.000000x3.964176x3.964176x3.96x1.391%1.39%5.516%5.52%9-1.000000x-1.000000x3.960022x3.960022x3.96x1.913%1.91%7.574%7.57%10-1.000000x-1.000000x3.941533x3.941533x3.94x0.890%0.89%3.508%3.51%11-1.000000x-1.000000x3.771400x3.771400x3.77x0.675%0.68%2.546%2.55%12-1.000000x-1.000000x3.559322x3.559322x3.56x0.714%0.71%2.542%2.54%13-1.000000x-1.000000x3.287265x3.287265x3.29x0.484%0.48%1.592%1.59%14-1.000000x-1.000000x3.116463x3.116463x3.12x1.006%1.01%3.135%3.14%15-1.000000x-1.000000x2.973659x2.973659x2.97x0.676%0.68%2.011%2.01%16-1.000000x-1.000000x2.815815x2.815815x2.82x0.628%0.63%1.768%1.77%17-1.000000x-1.000000x2.748931x2.748931x2.75x1.482%1.48%4.075%4.08%-1.000000x-1.000000x2.637955x2.637955x2.64x1.320%1.32%3.481%3.48%18-1.000000x-1.000000x2.444838x2.444838x2.44x0.507%0.51%1.239%1.24%19-1.000000x-1.000000x2.386875x2.386875x2.39x1.664%1.66%3.971%3.97%20-1.000000x-1.000000x2.261875x2.261875x2.26x0.900%0.90%2.035%2.03%21-1.000000x-1.000000x2.247082x2.247082x2.25x0.866%0.87%1.946%1.95%22-1.000000x-1.000000x2.119946x2.119946x2.12x2.230%2.23%4.727%4.73%23-1.000000x-1.000000x2.021196x2.021196x2.02x2.507%2.51%5.067%5.07%24-1.000000x-1.000000x1.971067x1.971067x1.97x0.970%0.97%1.912%1.91%-1.000000x-1.000000x1.922680x1.922680x1.92x1.877%1.88%3.608%3.61%25-1.000000x-1.000000x1.884442x1.884442x1.88x1.269%1.27%2.391%2.39%26-1.000000x-1.000000x1.824373x1.824373x1.82x2.161%2.16%3.943%3.94%27-1.000000x-1.000000x1.703316x1.703316x1.70x0.733%0.73%1.248%1.25%28-1.000000x-1.000000x1.674474x1.674474x1.67x1.815%1.81%3.038%3.04%-1.000000x-1.000000x1.644249x1.644249x1.64x3.343%3.34%5.497%5.50%29-1.000000x-1.000000x1.627456x1.627456x1.63x3.304%3.30%5.377%5.38%30-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.436893x1.436893x1.44x0.676%0.68%0.971%0.97%31-1.000000x-1.000000x1.423255x1.423255x1.42x2.030%2.03%2.889%2.89%32-1.000000x-1.000000x1.346441x1.346441x1.35x4.462%4.46%6.008%6.01%33-1.000000x-1.000000x1.206657x1.206657x1.21x1.319%1.32%1.592%1.59%34-1.000000x-1.000000x1.092634x1.092634x1.09x1.813%1.81%1.981%1.98%35-1.000000x-1.000000x1.044561x1.044561x1.04x2.509%2.51%2.621%2.62%36-1.000000x-1.000000x1.036274x1.036274x1.04x1.539%1.54%1.595%1.59%37-1.112634x-1.112634x1.11x1.000000x1.000000x1.707%1.71%1.534%1.53%38-1.390929x-1.390929x1.39x1.000000x1.000000x2.592%2.59%1.863%1.86%39-1.478421x-1.478421x1.48x1.000000x1.000000x1.377%1.38%0.932%0.93%40-1.487849x-1.487849x1.49x1.000000x1.000000x1.620%1.62%1.089%1.09%41-2.042296x-2.042296x2.04x1.000000x1.000000x1.309%1.31%0.641%0.64%42-2.195838x-2.195838x2.20x1.000000x1.000000x3.427%3.43%1.561%1.56%43-2.503026x-2.503026x2.50x1.000000x1.000000x5.710%5.71%2.281%2.28%44-2.811994x-2.811994x2.81x1.000000x1.000000x2.932%2.93%1.043%1.04%45-4.851399x-4.851399x4.85x1.000000x1.000000x2.280%2.28%0.470%0.47%46-7.335968x-7.335968x7.34x1.000000x1.000000x3.821%3.82%0.521%0.52%47-10.000000x-10.000000x> 10.0x1.000000x1.000000x3.053%3.05%0.145%0.15%48-10.000000x-10.000000x> 10.0x1.000000x1.000000x1.399%1.40%0.000%0%49
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 49 by male-to-female sex ratio

Educational Attainment by County Subdivision in the Midwest

There are 19,478 county subdivisions in the Midwest. This section compares the Golden Township to the 50 most populous county subdivisions in the Midwest and to those entities that contain or substantially overlap with the Golden Township. The least populous of the compared county subdivisions has a population of 112,821.

No H.S. Diploma by County Subdivision#33

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Golden Township, selected other county subdivisions in the Midwest, and entities that contain the Golden Township
0%10%20%30%Count#Aurora TownshipAuroraCenter TownshipCtrKansas CityClevelandWayne TownshipWayneDetroitMilwaukeeNorth TownshipNDaytonChicagoProviso TownshipProvisoRockford TownshipRockfordSt. LouisWarrenGrand RapidsToledoBlue TownshipCincinnatiAkronSt. PaulSterling HeightsSterling HtsWalsh CountyWalshUnited States of AmericaUnited StatesThornton TownshipThorntonWichitaZIP Code 58270ZIP 58270Kaw TownshipOmahaBloomingdale TownshipBloomingdaleColumbusMaine TownshipMaineTopekaMinneapolisSpringfield TownshipSpringfieldWorth TownshipWorthMidwestLawrence TownshipLawrenceSioux FallsWest North CentralCapital TownshipCapitalWheeling TownshipWheelingPalatine TownshipPalatineNorth DakotaSchaumburg TownshipSchaumburgLincolnWashington TownshipWashingtonOlatheFargoCedar RapidsYork TownshipMilton TownshipMiltonMadisonDowners Grove TownshipDowners GrvGolden TownshipGoldenLisle TownshipLisleOverland ParkAnn Arbor33.464284%33.464284%33.5%29,81429.8k123.165532%23.165532%23.2%22,92622.9k222.659362%22.659362%22.7%21,21821.2k321.582420%21.582420%21.6%54,98155.0k421.037720%21.037720%21.0%18,48918.5k521.003600%21.003600%21.0%90,67890.7k617.485105%17.485105%17.5%63,80063.8k717.062713%17.062713%17.1%17,39117.4k816.928359%16.928359%16.9%14,88914.9k916.905373%16.905373%16.9%309,770310k1015.636222%15.636222%15.6%16,11016.1k1115.608074%15.608074%15.6%18,24118.2k1215.466266%15.466266%15.5%33,80433.8k1315.286174%15.286174%15.3%14,05914.1k1414.563179%14.563179%14.6%17,64317.6k1514.332972%14.332972%14.3%26,34326.3k1614.241047%14.241047%14.2%15,25815.3k1713.761583%13.761583%13.8%26,28626.3k1813.591726%13.591726%13.6%17,95118.0k1913.278342%13.278342%13.3%24,61324.6k2013.132171%13.132171%13.1%12,21212.2k2113.114754%13.114754%13.1%1,00813.020590%13.020590%13.0%27,818,38027.8M12.974232%12.974232%13.0%14,37014.4k2212.568464%12.568464%12.6%31,23131.2k2312.536232%12.536232%12.5%17312.455744%12.455744%12.5%15,16315.2k2412.198899%12.198899%12.2%35,05035.1k2512.171847%12.171847%12.2%9,3752611.376979%11.376979%11.4%60,83160.8k2711.132206%11.132206%11.1%10,96511.0k2811.032803%11.032803%11.0%9,3772910.958428%10.958428%11.0%29,34729.3k3010.767091%10.767091%10.8%11,14911.1k3110.678962%10.678962%10.7%11,17511.2k3210.244520%10.244520%10.2%4,641,8354.64M10.179618%10.179618%10.2%8,161339.702326%9.702326%9.7%9,123349.174633%9.174633%9.2%1,278,0511.28M8.623103%8.623103%8.6%6,837358.424707%8.424707%8.4%9,343368.392669%8.392669%8.4%6,695377.963451%7.963451%8.0%38,03438.0k7.739434%7.739434%7.7%7,226387.092309%7.092309%7.1%11,88611.9k396.804680%6.804680%6.8%6,403406.424130%6.424130%6.4%5,331416.055621%6.055621%6.1%4,270425.981653%5.981653%6.0%5,099435.461095%5.461095%5.5%4,715445.266517%5.266517%5.3%4,126454.836248%4.836248%4.8%7,351464.020265%4.020265%4.0%4,166473.703704%3.703704%3.7%43.522812%3.522812%3.5%2,870483.377424%3.377424%3.4%4,282493.157717%3.157717%3.2%2,05750
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#34

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Golden Township, selected other county subdivisions in the Midwest, and entities that contain the Golden Township
0%20%40%60%Count#Ann ArborOverland ParkMilton TownshipMiltonLisle TownshipLisleMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonPalatine TownshipPalatineYork TownshipMinneapolisOlatheWheeling TownshipWheelingSchaumburg TownshipSchaumburgKaw TownshipSt. PaulMaine TownshipMaineFargoLincolnGolden TownshipGoldenLawrence TownshipLawrenceChicagoCapital TownshipCapitalColumbusOmahaCincinnatiSt. LouisGrand RapidsBloomingdale TownshipBloomingdaleCedar RapidsUnited States of AmericaUnited StatesSioux FallsWest North CentralMidwestWichitaNorth DakotaSterling HeightsSterling HtsTopekaSpringfield TownshipSpringfieldProviso TownshipProvisoWorth TownshipWorthMilwaukeeRockford TownshipRockfordAkronCenter TownshipCtrNorth TownshipNZIP Code 58270ZIP 58270Thornton TownshipThorntonToledoDaytonWarrenWayne TownshipWayneWalsh CountyWalshKansas CityClevelandAurora TownshipAuroraBlue TownshipDetroit72.810169%72.810169%72.8%47,43047.4k158.823344%58.823344%58.8%74,57874.6k257.681507%57.681507%57.7%45,19045.2k356.900171%56.900171%56.9%46,35646.4k456.337583%56.337583%56.3%85,63285.6k553.762123%53.762123%53.8%55,71155.7k650.610540%50.610540%50.6%47,62347.6k749.860853%49.860853%49.9%39,77539.8k849.728972%49.728972%49.7%42,93542.9k947.670489%47.670489%47.7%127,663128k1046.512581%46.512581%46.5%38,59838.6k1146.409378%46.409378%46.4%51,46851.5k1241.792515%41.792515%41.8%39,02039.0k1341.581304%41.581304%41.6%50,61950.6k1439.969357%39.969357%40.0%74,08874.1k1539.511462%39.511462%39.5%38,91838.9k1638.241175%38.241175%38.2%26,96527.0k1737.146608%37.146608%37.1%62,25462.3k1837.037037%37.037037%37.0%4036.577273%36.577273%36.6%29,32429.3k1936.546320%36.546320%36.5%669,666670k2036.298510%36.298510%36.3%28,78028.8k2134.685843%34.685843%34.7%185,460185k2234.651487%34.651487%34.7%99,56199.6k2333.783048%33.783048%33.8%64,52964.5k2433.007421%33.007421%33.0%72,14372.1k2532.904381%32.904381%32.9%39,86339.9k2632.623147%32.623147%32.6%25,12725.1k2731.793440%31.793440%31.8%27,10227.1k2830.315023%30.315023%30.3%64,767,78764.8M30.259813%30.259813%30.3%28,45328.5k2930.056536%30.056536%30.1%4,186,9564.19M28.952816%28.952816%29.0%13,118,64213.1M28.877567%28.877567%28.9%71,75771.8k3028.172535%28.172535%28.2%134,554135k27.532180%27.532180%27.5%25,60325.6k3127.007248%27.007248%27.0%22,95423.0k3226.964567%26.964567%27.0%27,92127.9k3326.237989%26.237989%26.2%27,03327.0k3425.217641%25.217641%25.2%26,38926.4k3523.517740%23.517740%23.5%85,81285.8k3621.592552%21.592552%21.6%25,23525.2k3720.213821%20.213821%20.2%26,69726.7k3820.139240%20.139240%20.1%19,93119.9k3919.650916%19.650916%19.7%20,02920.0k4019.202899%19.202899%19.2%26518.971090%18.971090%19.0%21,01221.0k4118.048021%18.048021%18.0%33,17133.2k4217.635555%17.635555%17.6%15,51115.5k4317.280259%17.280259%17.3%15,89315.9k4416.826535%16.826535%16.8%14,78814.8k4516.718709%16.718709%16.7%1,28516.167409%16.167409%16.2%15,13915.1k4616.073076%16.073076%16.1%40,94640.9k4715.929601%15.929601%15.9%14,19214.2k4815.821208%15.821208%15.8%16,95117.0k4913.833543%13.833543%13.8%59,72359.7k50
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#35

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Golden Township, selected other county subdivisions in the Midwest, and entities that contain the Golden Township
0%5%10%15%Count#Ann ArborMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonMinneapolisYork TownshipOverland ParkLisle TownshipLisleSt. PaulKaw TownshipMilton TownshipMiltonSt. LouisCincinnatiGolden TownshipGoldenLincolnChicagoFargoCapital TownshipCapitalOmahaMaine TownshipMainePalatine TownshipPalatineWheeling TownshipWheelingColumbusUnited States of AmericaUnited StatesTopekaGrand RapidsLawrence TownshipLawrenceWest North CentralSpringfield TownshipSpringfieldCedar RapidsMidwestSioux FallsOlatheZIP Code 58270ZIP 58270Center TownshipCtrSchaumburg TownshipSchaumburgWichitaNorth DakotaProviso TownshipProvisoAkronBloomingdale TownshipBloomingdaleRockford TownshipRockfordMilwaukeeWorth TownshipWorthClevelandToledoNorth TownshipNWalsh CountyWalshSterling HeightsSterling HtsKansas CityDetroitDaytonWayne TownshipWayneWarrenBlue TownshipAurora TownshipAuroraThornton TownshipThornton17.877867%17.877867%17.9%11,64611.6k19.171173%9.171173%9.2%13,94013.9k27.658384%7.658384%7.7%7,93637.306290%7.306290%7.3%6,87546.241155%6.241155%6.2%16,71416.7k56.197734%6.197734%6.2%5,35165.940071%5.940071%5.9%7,53175.933545%5.933545%5.9%4,83485.920847%5.920847%5.9%10,97511.0k95.896414%5.896414%5.9%7,178105.800061%5.800061%5.8%4,544114.880448%4.880448%4.9%10,66710.7k124.729595%4.729595%4.7%9,034134.629630%4.629630%4.6%54.568292%4.568292%4.6%7,656144.485379%4.485379%4.5%82,18982.2k154.336789%4.336789%4.3%3,058164.233985%4.233985%4.2%3,357174.059919%4.059919%4.1%11,66511.7k184.030539%4.030539%4.0%3,970193.487439%3.487439%3.5%2,782203.407574%3.407574%3.4%3,779213.357865%3.357865%3.4%17,95418.0k223.336158%3.336158%3.3%7,127,6737.13M3.233245%3.233245%3.2%2,748233.174629%3.174629%3.2%3,846243.083448%3.083448%3.1%2,472252.978981%2.978981%3.0%414,980415k2.911721%2.911721%2.9%3,015262.905776%2.905776%2.9%2,477272.893416%2.893416%2.9%1,311,0191.31M2.841677%2.841677%2.8%2,672282.830666%2.830666%2.8%2,349292.681159%2.681159%2.7%372.602914%2.602914%2.6%2,576302.551250%2.551250%2.6%2,382312.519246%2.519246%2.5%6,260322.435475%2.435475%2.4%11,63211.6k2.213918%2.213918%2.2%2,281332.163955%2.163955%2.2%2,858342.113682%2.113682%2.1%1,628352.067272%2.067272%2.1%2,416362.043126%2.043126%2.0%7,455371.958049%1.958049%2.0%2,049381.927780%1.927780%1.9%4,911391.642065%1.642065%1.6%3,018401.575684%1.575684%1.6%1,606411.444184%1.444184%1.4%1111.439893%1.439893%1.4%1,339421.388310%1.388310%1.4%1,300431.318660%1.318660%1.3%5,693441.257490%1.257490%1.3%1,106451.183365%1.183365%1.2%1,040461.047058%1.047058%1.0%963470.997751%0.997751%1.0%1,069480.936111%0.936111%0.9%834490.876686%0.876686%0.9%97150
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#36

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in the Midwest, and entities that contain the Golden Township
Female
Male
10x5x0xFM#WalshCedar RapidsLincolnDetroitOmahaToledoNorth DakotaThorntonKansas CityMadisonLawrenceFargoProvisoRockfordBlue TownshipWest North CentralWayneMidwestTopekaMilwaukeeKaw TownshipDaytonSioux FallsUnited StatesOlatheWorthNOverland ParkAuroraWheelingGrand RapidsSchaumburgChicagoWichitaColumbusClevelandPalatineCapitalWarrenSpringfieldYork TownshipSt. LouisWashingtonBloomingdaleCtrCincinnatiAkronAnn ArborSt. PaulLisleMinneapolisSterling HtsDowners GrvMiltonMaineZIP 58270Golden-1.000000x-1.000000x1.357433x1.357433x1.36x11.114%11.1%15.087%15.1%-1.000000x-1.000000x1.243824x1.243824x1.24x5.354%5.35%6.660%6.66%1-1.000000x-1.000000x1.238529x1.238529x1.24x6.342%6.34%7.854%7.85%2-1.000000x-1.000000x1.237056x1.237056x1.24x18.951%19.0%23.443%23.4%3-1.000000x-1.000000x1.230289x1.230289x1.23x10.968%11.0%13.494%13.5%4-1.000000x-1.000000x1.227011x1.227011x1.23x12.945%12.9%15.884%15.9%5-1.000000x-1.000000x1.216853x1.216853x1.22x7.174%7.17%8.730%8.73%-1.000000x-1.000000x1.203250x1.203250x1.20x11.909%11.9%14.330%14.3%6-1.000000x-1.000000x1.189752x1.189752x1.19x20.748%20.7%24.685%24.7%7-1.000000x-1.000000x1.189278x1.189278x1.19x4.425%4.43%5.263%5.26%8-1.000000x-1.000000x1.181964x1.181964x1.18x9.406%9.41%11.117%11.1%9-1.000000x-1.000000x1.176963x1.176963x1.18x5.565%5.56%6.550%6.55%10-1.000000x-1.000000x1.174964x1.174964x1.17x14.460%14.5%16.990%17.0%11-1.000000x-1.000000x1.168755x1.168755x1.17x14.456%14.5%16.896%16.9%12-1.000000x-1.000000x1.161774x1.161774x1.16x13.244%13.2%15.387%15.4%13-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.132245x1.132245x1.13x19.779%19.8%22.395%22.4%14-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.125507x1.125507x1.13x10.423%10.4%11.732%11.7%15-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%16-1.000000x-1.000000x1.115695x1.115695x1.12x11.779%11.8%13.142%13.1%17-1.000000x-1.000000x1.114342x1.114342x1.11x16.070%16.1%17.907%17.9%18-1.000000x-1.000000x1.110589x1.110589x1.11x9.201%9.20%10.219%10.2%19-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.107847x1.107847x1.11x6.105%6.11%6.764%6.76%20-1.000000x-1.000000x1.099747x1.099747x1.10x10.199%10.2%11.216%11.2%21-1.000000x-1.000000x1.096598x1.096598x1.10x16.317%16.3%17.894%17.9%22-1.000000x-1.000000x1.095440x1.095440x1.10x3.232%3.23%3.540%3.54%23-1.000000x-1.000000x1.086583x1.086583x1.09x32.102%32.1%34.882%34.9%24-1.000000x-1.000000x1.079846x1.079846x1.08x8.111%8.11%8.758%8.76%25-1.000000x-1.000000x1.078480x1.078480x1.08x14.031%14.0%15.132%15.1%26-1.000000x-1.000000x1.077209x1.077209x1.08x7.467%7.47%8.043%8.04%27-1.000000x-1.000000x1.068021x1.068021x1.07x16.372%16.4%17.486%17.5%28-1.000000x-1.000000x1.059528x1.059528x1.06x12.215%12.2%12.942%12.9%29-1.000000x-1.000000x1.052237x1.052237x1.05x11.098%11.1%11.678%11.7%30-1.000000x-1.000000x1.046686x1.046686x1.05x21.119%21.1%22.105%22.1%31-1.000000x-1.000000x1.039581x1.039581x1.04x8.233%8.23%8.559%8.56%32-1.000000x-1.000000x1.039194x1.039194x1.04x8.470%8.47%8.802%8.80%33-1.000000x-1.000000x1.037373x1.037373x1.04x15.022%15.0%15.584%15.6%34-1.000000x-1.000000x1.028857x1.028857x1.03x10.622%10.6%10.928%10.9%35-1.000000x-1.000000x1.027464x1.027464x1.03x5.391%5.39%5.539%5.54%36-1.000000x-1.000000x1.018183x1.018183x1.02x15.334%15.3%15.613%15.6%37-1.000000x-1.000000x1.013146x1.013146x1.01x6.764%6.76%6.853%6.85%38-1.000000x-1.000000x1.007207x1.007207x1.01x12.130%12.1%12.217%12.2%39-1.000000x-1.000000x1.006204x1.006204x1.01x23.095%23.1%23.239%23.2%40-1.000000x-1.000000x1.001229x1.001229x1.00x13.754%13.8%13.771%13.8%41-1.001506x-1.001506x1.00x1.000000x1.000000x13.601%13.6%13.581%13.6%42-1.031649x-1.031649x1.03x1.000000x1.000000x3.205%3.21%3.107%3.11%43-1.060004x-1.060004x1.06x1.000000x1.000000x13.651%13.7%12.879%12.9%44-1.073696x-1.073696x1.07x1.000000x1.000000x3.642%3.64%3.392%3.39%45-1.092939x-1.092939x1.09x1.000000x1.000000x11.450%11.4%10.476%10.5%46-1.131063x-1.131063x1.13x1.000000x1.000000x13.912%13.9%12.300%12.3%47-1.140051x-1.140051x1.14x1.000000x1.000000x4.269%4.27%3.744%3.74%48-1.194783x-1.194783x1.19x1.000000x1.000000x5.710%5.71%4.779%4.78%49-1.208375x-1.208375x1.21x1.000000x1.000000x12.117%12.1%10.027%10.0%50-2.520889x-2.520889x2.52x1.000000x1.000000x17.385%17.4%6.897%6.90%-10.000000x-10.000000x> 10.0x1.000000x1.000000x7.547%7.55%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#37

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in the Midwest, and entities that contain the Golden Township
Female
Male
0x5x10xFM#GoldenZIP 58270RockfordNDaytonWalshSioux FallsCapitalLawrenceDowners GrvAkronLisleWichitaYork TownshipMaineWarrenWheelingBlue TownshipSpringfieldOverland ParkMiltonGrand RapidsOlatheNorth DakotaOmahaFargoWashingtonLincolnMidwestBloomingdaleWest North CentralKansas CityAuroraUnited StatesAnn ArborTopekaSterling HtsPalatineSchaumburgColumbusMadisonKaw TownshipMilwaukeeCedar RapidsClevelandCincinnatiSt. PaulSt. LouisChicagoWayneCtrToledoMinneapolisWorthDetroitProvisoThornton-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%9.091%9.09%-1.000000x-1.000000x2.748931x2.748931x2.75x1.482%1.48%4.075%4.08%-1.000000x-1.000000x2.117268x2.117268x2.12x1.353%1.35%2.866%2.87%1-1.000000x-1.000000x2.111054x2.111054x2.11x1.033%1.03%2.181%2.18%2-1.000000x-1.000000x1.989047x1.989047x1.99x0.860%0.86%1.711%1.71%3-1.000000x-1.000000x1.971067x1.971067x1.97x0.970%0.97%1.912%1.91%-1.000000x-1.000000x1.962539x1.962539x1.96x1.928%1.93%3.783%3.78%4-1.000000x-1.000000x1.901348x1.901348x1.90x2.990%2.99%5.686%5.69%5-1.000000x-1.000000x1.879271x1.879271x1.88x2.206%2.21%4.146%4.15%6-1.000000x-1.000000x1.852471x1.852471x1.85x5.456%5.46%10.107%10.1%7-1.000000x-1.000000x1.846298x1.846298x1.85x1.550%1.55%2.862%2.86%8-1.000000x-1.000000x1.815266x1.815266x1.82x4.271%4.27%7.754%7.75%9-1.000000x-1.000000x1.792728x1.792728x1.79x1.819%1.82%3.261%3.26%10-1.000000x-1.000000x1.792314x1.792314x1.79x4.498%4.50%8.062%8.06%11-1.000000x-1.000000x1.788417x1.788417x1.79x2.939%2.94%5.256%5.26%12-1.000000x-1.000000x1.768853x1.768853x1.77x0.769%0.77%1.361%1.36%13-1.000000x-1.000000x1.766097x1.766097x1.77x2.485%2.48%4.389%4.39%14-1.000000x-1.000000x1.761699x1.761699x1.76x0.737%0.74%1.298%1.30%15-1.000000x-1.000000x1.751445x1.751445x1.75x2.146%2.15%3.759%3.76%16-1.000000x-1.000000x1.732234x1.732234x1.73x4.415%4.41%7.647%7.65%17-1.000000x-1.000000x1.695067x1.695067x1.70x4.357%4.36%7.385%7.38%18-1.000000x-1.000000x1.689013x1.689013x1.69x2.382%2.38%4.023%4.02%19-1.000000x-1.000000x1.678153x1.678153x1.68x2.131%2.13%3.576%3.58%20-1.000000x-1.000000x1.674474x1.674474x1.67x1.815%1.81%3.038%3.04%-1.000000x-1.000000x1.669388x1.669388x1.67x3.061%3.06%5.110%5.11%21-1.000000x-1.000000x1.627456x1.627456x1.63x3.304%3.30%5.377%5.38%22-1.000000x-1.000000x1.610803x1.610803x1.61x5.701%5.70%9.183%9.18%23-1.000000x-1.000000x1.596978x1.596978x1.60x3.524%3.52%5.628%5.63%24-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.586045x1.586045x1.59x1.649%1.65%2.616%2.62%25-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.570028x1.570028x1.57x1.087%1.09%1.707%1.71%26-1.000000x-1.000000x1.550495x1.550495x1.55x0.737%0.74%1.143%1.14%27-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.522839x1.522839x1.52x14.269%14.3%21.730%21.7%28-1.000000x-1.000000x1.481288x1.481288x1.48x2.641%2.64%3.912%3.91%29-1.000000x-1.000000x1.476996x1.476996x1.48x1.170%1.17%1.728%1.73%30-1.000000x-1.000000x1.457167x1.457167x1.46x2.851%2.85%4.154%4.15%31-1.000000x-1.000000x1.436165x1.436165x1.44x2.115%2.11%3.037%3.04%32-1.000000x-1.000000x1.413654x1.413654x1.41x2.801%2.80%3.959%3.96%33-1.000000x-1.000000x1.387878x1.387878x1.39x7.705%7.70%10.693%10.7%34-1.000000x-1.000000x1.373250x1.373250x1.37x4.975%4.97%6.832%6.83%35-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%36-1.000000x-1.000000x1.346658x1.346658x1.35x2.491%2.49%3.354%3.35%37-1.000000x-1.000000x1.286599x1.286599x1.29x1.699%1.70%2.186%2.19%38-1.000000x-1.000000x1.280610x1.280610x1.28x4.179%4.18%5.351%5.35%39-1.000000x-1.000000x1.274509x1.274509x1.27x5.228%5.23%6.663%6.66%40-1.000000x-1.000000x1.273345x1.273345x1.27x4.319%4.32%5.500%5.50%41-1.000000x-1.000000x1.238186x1.238186x1.24x4.026%4.03%4.985%4.99%42-1.000000x-1.000000x1.223931x1.223931x1.22x1.068%1.07%1.307%1.31%43-1.000000x-1.000000x1.213574x1.213574x1.21x2.357%2.36%2.860%2.86%44-1.000000x-1.000000x1.141406x1.141406x1.14x1.539%1.54%1.757%1.76%45-1.000000x-1.000000x1.124528x1.124528x1.12x5.872%5.87%6.603%6.60%46-1.000000x-1.000000x1.084697x1.084697x1.08x1.883%1.88%2.042%2.04%47-1.000000x-1.000000x1.046557x1.046557x1.05x1.291%1.29%1.351%1.35%48-1.000000x-1.000000x1.042857x1.042857x1.04x2.171%2.17%2.264%2.26%49-1.706858x-1.706858x1.71x1.000000x1.000000x1.072%1.07%0.628%0.63%50
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Educational Attainment by County Subdivision in the United States

There are 35,600 county subdivisions in the United States. This section compares the Golden Township to the 50 most populous county subdivisions in the United States and to those entities that contain or substantially overlap with the Golden Township. The least populous of the compared county subdivisions has a population of 547,300.

No H.S. Diploma by County Subdivision#38

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of the Golden Township, selected other county subdivisions in the United States, and entities that contain the Golden Township
0%10%20%Count#San Antonio CentralSan Antonio CntrlBronxSan BernardinoLos AngelesSouthwest DallasSW DallasEl PasoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…FresnoHoustonMiamiDetroitNortheast DallasNE DallasEast San Gabriel ValleyE San Gabriel VlyFort WorthBrooklynLong Beach-LakewoodLong Bch-LakewoodQueensSan Fernando ValleySan Fernando VlyMilwaukeePhiladelphiaOntarioChicagoBaltimoreOceanside-EscondidoOceanside-Escond…Las VegasPhoenixBostonSacramentoOrlandoAustinDenverWalsh CountyWalshUnited States of AmericaUnited StatesManhattanSan DiegoTampaSan JoseSan FranciscoZIP Code 58270ZIP 58270TucsonAlbuquerqueTownship 1:Charlotte1, CharlotteColumbusTulsaSalt Lake CitySalt Lk CityTown of HempsteadHempsteadMidwestNorthwest HarrisNW HarrisWashingtonAtlantaWest North CentralSeattleNortheast TarrantNE TarrantNorth DakotaPlanoSeattle EastGolden TownshipGolden28.998944%28.998944%29.0%126,922127k128.837398%28.837398%28.8%261,022261k227.582802%27.582802%27.6%135,754136k324.837401%24.837401%24.8%434,160434k423.987048%23.987048%24.0%141,940142k523.756417%23.756417%23.8%81,30881.3k622.645542%22.645542%22.6%248,699249k722.107626%22.107626%22.1%92,47292.5k821.922900%21.922900%21.9%457,347457k921.818150%21.818150%21.8%149,688150k1021.003600%21.003600%21.0%90,67890.7k1120.783651%20.783651%20.8%207,494207k1220.461493%20.461493%20.5%128,038128k1320.041576%20.041576%20.0%106,726107k1420.017506%20.017506%20.0%351,046351k1519.402518%19.402518%19.4%73,10573.1k1619.344706%19.344706%19.3%315,505316k1719.344408%19.344408%19.3%244,983245k1817.485105%17.485105%17.5%63,80063.8k1917.390838%17.390838%17.4%180,039180k2017.129346%17.129346%17.1%71,39771.4k2116.905373%16.905373%16.9%309,770310k2216.548703%16.548703%16.5%69,83969.8k2315.999398%15.999398%16.0%70,21470.2k2415.906523%15.906523%15.9%198,651199k2514.952077%14.952077%15.0%308,569309k2614.325556%14.325556%14.3%63,64263.6k2713.900512%13.900512%13.9%103,047103k2813.679368%13.679368%13.7%53,75853.8k2913.668915%13.668915%13.7%78,23878.2k3013.638703%13.638703%13.6%63,86563.9k3113.114754%13.114754%13.1%1,00813.020590%13.020590%13.0%27,818,38027.8M12.996284%12.996284%13.0%160,845161k3212.985760%12.985760%13.0%206,792207k3312.979618%12.979618%13.0%57,75857.8k3412.789590%12.789590%12.8%153,604154k3512.559735%12.559735%12.6%84,02484.0k3612.536232%12.536232%12.5%17312.262851%12.262851%12.3%70,40970.4k3712.089143%12.089143%12.1%52,21752.2k3811.553484%11.553484%11.6%61,37861.4k3911.376979%11.376979%11.4%60,83160.8k4011.299086%11.299086%11.3%43,65343.7k4110.435356%10.435356%10.4%64,29464.3k4210.384134%10.384134%10.4%54,23054.2k4310.244520%10.244520%10.2%4,641,8354.64M10.135583%10.135583%10.1%47,67947.7k4410.045421%10.045421%10.0%46,51046.5k459.538552%9.538552%9.5%37,37337.4k469.174633%9.174633%9.2%1,278,0511.28M8.833573%8.833573%8.8%66,22066.2k478.418271%8.418271%8.4%31,44331.4k487.963451%7.963451%8.0%38,03438.0k5.714681%5.714681%5.7%20,64420.6k494.913852%4.913852%4.9%20,32620.3k503.703704%3.703704%3.7%4
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#39

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of the Golden Township, selected other county subdivisions in the United States, and entities that contain the Golden Township
0%20%40%60%Count#ManhattanSeattle EastWashingtonSan FranciscoPlanoSan JoseAtlantaSeattleBostonDenverAustinTownship 1:Charlotte1, CharlotteTown of HempsteadHempsteadSan DiegoNortheast TarrantNE TarrantGolden TownshipGoldenNorthwest HarrisNW HarrisChicagoColumbusLos AngelesBrooklynSan Fernando ValleySan Fernando VlyNortheast DallasNE DallasSalt Lake CitySalt Lk CityAlbuquerqueTampaTucsonOceanside-EscondidoOceanside-Escond…TulsaQueensUnited States of AmericaUnited StatesHoustonWest North CentralBaltimoreLong Beach-LakewoodLong Bch-LakewoodMidwestPhoenixOrlandoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…SacramentoNorth DakotaEast San Gabriel ValleyE San Gabriel VlyOntarioPhiladelphiaMiamiFort WorthSouthwest DallasSW DallasMilwaukeeFresnoLas VegasEl PasoZIP Code 58270ZIP 58270BronxWalsh CountyWalshSan BernardinoSan Antonio CentralSan Antonio CntrlDetroit60.434640%60.434640%60.4%747,953748k157.691220%57.691220%57.7%238,638239k255.360186%55.360186%55.4%256,316256k354.793235%54.793235%54.8%366,564367k454.142756%54.142756%54.1%195,588196k549.985845%49.985845%50.0%600,334600k649.803731%49.803731%49.8%195,136195k749.626221%49.626221%49.6%372,018372k846.377869%46.377869%46.4%206,036206k945.718966%45.718966%45.7%214,085214k1044.843364%44.843364%44.8%256,674257k1141.989192%41.989192%42.0%223,068223k1238.767308%38.767308%38.8%202,458202k1338.701072%38.701072%38.7%616,296616k1438.547398%38.547398%38.5%143,978144k1537.037037%37.037037%37.0%4036.877886%36.877886%36.9%173,478173k1636.546320%36.546320%36.5%669,666670k1734.685843%34.685843%34.7%185,460185k1834.593243%34.593243%34.6%604,693605k1934.081468%34.081468%34.1%597,685598k2033.231104%33.231104%33.2%420,848421k2133.191099%33.191099%33.2%331,364331k2233.118385%33.118385%33.1%204,048204k2332.636312%32.636312%32.6%140,967141k2432.512191%32.512191%32.5%144,676145k2532.087814%32.087814%32.1%184,237184k2631.198303%31.198303%31.2%136,915137k2731.110082%31.110082%31.1%120,191120k2830.595789%30.595789%30.6%499,006499k2930.315023%30.315023%30.3%64,767,78764.8M30.080133%30.080133%30.1%627,520628k3030.056536%30.056536%30.1%4,186,9564.19M29.719611%29.719611%29.7%125,423125k3129.113198%29.113198%29.1%109,693110k3228.952816%28.952816%29.0%13,118,64213.1M28.760152%28.760152%28.8%593,529594k3328.535877%28.535877%28.5%112,142112k3428.412939%28.412939%28.4%312,038312k3528.231204%28.231204%28.2%209,283209k3628.172535%28.172535%28.2%134,554135k27.276345%27.276345%27.3%170,682171k3726.758411%26.758411%26.8%111,532112k3826.349720%26.349720%26.3%272,786273k3926.011156%26.011156%26.0%178,455178k4024.665414%24.665414%24.7%131,349131k4123.764821%23.764821%23.8%140,625141k4223.517740%23.517740%23.5%85,81285.8k4322.486319%22.486319%22.5%94,05694.1k4421.702666%21.702666%21.7%271,037271k4521.071885%21.071885%21.1%72,12072.1k4619.202899%19.202899%19.2%26519.131504%19.131504%19.1%173,169173k4716.718709%16.718709%16.7%1,28516.375473%16.375473%16.4%80,59580.6k4814.929012%14.929012%14.9%65,34165.3k4913.833543%13.833543%13.8%59,72359.7k50
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#40

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of the Golden Township, selected other county subdivisions in the United States, and entities that contain the Golden Township
0%5%10%Count#WashingtonManhattanSan FranciscoBostonAtlantaSeattleSan JoseSeattle EastDenverSan DiegoAustinBaltimoreLos AngelesAlbuquerqueGolden TownshipGoldenPlanoChicagoTucsonTown of HempsteadHempsteadTampaMiamiNortheast DallasNE DallasSalt Lake CitySalt Lk CityHoustonPhiladelphiaSan Fernando ValleySan Fernando VlyTownship 1:Charlotte1, CharlotteOceanside-EscondidoOceanside-Escond…BrooklynSacramentoColumbusUnited States of AmericaUnited StatesTulsaPhoenixLong Beach-LakewoodLong Bch-LakewoodWest North CentralMidwestOrlandoFresnoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…QueensNorthwest HarrisNW HarrisZIP Code 58270ZIP 58270Northeast TarrantNE TarrantEast San Gabriel ValleyE San Gabriel VlyNorth DakotaOntarioFort WorthLas VegasMilwaukeeSan BernardinoSan Antonio CentralSan Antonio CntrlEl PasoSouthwest DallasSW DallasBronxWalsh CountyWalshDetroit12.461852%12.461852%12.5%57,69857.7k110.788423%10.788423%10.8%133,520134k27.558053%7.558053%7.6%50,56350.6k37.340604%7.340604%7.3%32,61132.6k46.889819%6.889819%6.9%26,99527.0k56.641855%6.641855%6.6%49,79049.8k66.255912%6.255912%6.3%75,13475.1k76.036065%6.036065%6.0%24,96825.0k85.725842%5.725842%5.7%26,81226.8k95.247128%5.247128%5.2%83,55883.6k105.058711%5.058711%5.1%28,95529.0k114.934352%4.934352%4.9%20,82420.8k124.659072%4.659072%4.7%81,44181.4k134.631274%4.631274%4.6%20,00420.0k144.629630%4.629630%4.6%54.617088%4.617088%4.6%16,67916.7k154.485379%4.485379%4.5%82,18982.2k164.461087%4.461087%4.5%25,61425.6k174.441262%4.441262%4.4%23,19423.2k184.325266%4.325266%4.3%19,24719.2k194.025531%4.025531%4.0%27,61827.6k203.827408%3.827408%3.8%38,21138.2k213.792624%3.792624%3.8%23,36723.4k223.737871%3.737871%3.7%77,97878.0k233.720350%3.720350%3.7%38,51538.5k243.601468%3.601468%3.6%45,61045.6k253.490252%3.490252%3.5%18,54218.5k263.466073%3.466073%3.5%15,21115.2k273.384967%3.384967%3.4%59,36259.4k283.380196%3.380196%3.4%25,05825.1k293.357865%3.357865%3.4%17,95418.0k303.336158%3.336158%3.3%7,127,6737.13M3.140490%3.140490%3.1%12,13312.1k313.010825%3.010825%3.0%62,13562.1k323.009440%3.009440%3.0%11,33911.3k332.978981%2.978981%3.0%414,980415k2.893416%2.893416%2.9%1,311,0191.31M2.893233%2.893233%2.9%11,37011.4k342.828959%2.828959%2.8%11,83311.8k352.763004%2.763004%2.8%30,34430.3k362.756960%2.756960%2.8%44,96545.0k372.682967%2.682967%2.7%12,62112.6k382.681159%2.681159%2.7%372.630994%2.630994%2.6%9,827392.484535%2.484535%2.5%15,54715.5k402.435475%2.435475%2.4%11,63211.6k2.324315%2.324315%2.3%9,688412.173239%2.173239%2.2%11,57311.6k422.155077%2.155077%2.2%26,91426.9k432.043126%2.043126%2.0%7,455441.807306%1.807306%1.8%8,895451.792414%1.792414%1.8%7,845461.745764%1.745764%1.7%5,975471.704476%1.704476%1.7%10,08610.1k481.664253%1.664253%1.7%15,06415.1k491.444184%1.444184%1.4%1111.318660%1.318660%1.3%5,69350
Count number of people in category
# rank of county subdivision out of 50 by percentage of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).

Under-Education Sex Ratio by County Subdivision#41

Percentage more likely men are than women to not have a high school diploma (or equivalent) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in the United States, and entities that contain the Golden Township
Female
Male
10x5x0xFM#Walsh1, CharlotteDetroitNorth DakotaBaltimoreTulsaSW DallasWest North CentralMidwestFresnoMilwaukeeUnited StatesNE DallasHoustonHempsteadOrlandoPhiladelphiaOntarioSan BernardinoAustinAtlantaFort WorthPhoenixChicagoAlbuquerqueWashingtonSacramentoColumbusSalt Lk CityDenverNE TarrantTampaOceanside-EscondidoOceanside-Escond…NW HarrisBronxPlanoBrooklynSan Antonio CntrlSan Fernando VlyTucsonAnaheim-Santa Ana-Gdn GrvAnaheim-Santa An…Los AngelesLas VegasBostonE San Gabriel VlyQueensMiamiManhattanSeattleLong Bch-LakewoodSeattle EastSan JoseSan DiegoSan FranciscoEl PasoZIP 58270Golden-1.000000x-1.000000x1.357433x1.357433x1.36x11.114%11.1%15.087%15.1%-1.000000x-1.000000x1.285784x1.285784x1.29x10.185%10.2%13.096%13.1%1-1.000000x-1.000000x1.237056x1.237056x1.24x18.951%19.0%23.443%23.4%2-1.000000x-1.000000x1.216853x1.216853x1.22x7.174%7.17%8.730%8.73%-1.000000x-1.000000x1.179669x1.179669x1.18x15.289%15.3%18.036%18.0%3-1.000000x-1.000000x1.177766x1.177766x1.18x10.417%10.4%12.268%12.3%4-1.000000x-1.000000x1.160109x1.160109x1.16x22.304%22.3%25.875%25.9%5-1.000000x-1.000000x1.137506x1.137506x1.14x8.598%8.60%9.780%9.78%-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.127206x1.127206x1.13x20.830%20.8%23.479%23.5%6-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%7-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.099797x1.099797x1.10x19.814%19.8%21.791%21.8%8-1.000000x-1.000000x1.099188x1.099188x1.10x20.902%20.9%22.975%23.0%9-1.000000x-1.000000x1.092101x1.092101x1.09x9.951%9.95%10.868%10.9%10-1.000000x-1.000000x1.090284x1.090284x1.09x13.111%13.1%14.294%14.3%11-1.000000x-1.000000x1.090163x1.090163x1.09x16.700%16.7%18.206%18.2%12-1.000000x-1.000000x1.088515x1.088515x1.09x16.417%16.4%17.871%17.9%13-1.000000x-1.000000x1.081887x1.081887x1.08x26.536%26.5%28.709%28.7%14-1.000000x-1.000000x1.078150x1.078150x1.08x13.148%13.1%14.176%14.2%15-1.000000x-1.000000x1.073220x1.073220x1.07x9.215%9.21%9.889%9.89%16-1.000000x-1.000000x1.071348x1.071348x1.07x19.379%19.4%20.762%20.8%17-1.000000x-1.000000x1.069008x1.069008x1.07x14.467%14.5%15.465%15.5%18-1.000000x-1.000000x1.068021x1.068021x1.07x16.372%16.4%17.486%17.5%19-1.000000x-1.000000x1.066879x1.066879x1.07x11.714%11.7%12.497%12.5%20-1.000000x-1.000000x1.064309x1.064309x1.06x9.751%9.75%10.378%10.4%21-1.000000x-1.000000x1.053268x1.053268x1.05x13.558%13.6%14.280%14.3%22-1.000000x-1.000000x1.052237x1.052237x1.05x11.098%11.1%11.678%11.7%23-1.000000x-1.000000x1.051272x1.051272x1.05x10.176%10.2%10.697%10.7%24-1.000000x-1.000000x1.049933x1.049933x1.05x13.307%13.3%13.971%14.0%25-1.000000x-1.000000x1.040742x1.040742x1.04x8.257%8.26%8.594%8.59%26-1.000000x-1.000000x1.040003x1.040003x1.04x12.734%12.7%13.244%13.2%27-1.000000x-1.000000x1.039910x1.039910x1.04x15.694%15.7%16.321%16.3%28-1.000000x-1.000000x1.038394x1.038394x1.04x9.953%9.95%10.335%10.3%29-1.000000x-1.000000x1.030539x1.030539x1.03x28.448%28.4%29.316%29.3%30-1.000000x-1.000000x1.023151x1.023151x1.02x5.652%5.65%5.783%5.78%31-1.000000x-1.000000x1.018960x1.018960x1.02x19.845%19.8%20.221%20.2%32-1.000000x-1.000000x1.003929x1.003929x1.00x28.944%28.9%29.058%29.1%33-1.000000x-1.000000x1.003840x1.003840x1.00x19.308%19.3%19.383%19.4%34-1.006471x-1.006471x1.01x1.000000x1.000000x12.301%12.3%12.222%12.2%35-1.008020x-1.008020x1.01x1.000000x1.000000x22.734%22.7%22.553%22.6%36-1.013004x-1.013004x1.01x1.000000x1.000000x24.995%25.0%24.674%24.7%37-1.016645x-1.016645x1.02x1.000000x1.000000x16.036%16.0%15.774%15.8%38-1.022333x-1.022333x1.02x1.000000x1.000000x14.476%14.5%14.159%14.2%39-1.026902x-1.026902x1.03x1.000000x1.000000x20.723%20.7%20.180%20.2%40-1.027762x-1.027762x1.03x1.000000x1.000000x19.596%19.6%19.067%19.1%41-1.030897x-1.030897x1.03x1.000000x1.000000x22.136%22.1%21.473%21.5%42-1.044246x-1.044246x1.04x1.000000x1.000000x13.260%13.3%12.698%12.7%43-1.049689x-1.049689x1.05x1.000000x1.000000x9.047%9.05%8.619%8.62%44-1.056864x-1.056864x1.06x1.000000x1.000000x19.921%19.9%18.849%18.8%45-1.057675x-1.057675x1.06x1.000000x1.000000x5.050%5.05%4.774%4.77%46-1.127548x-1.127548x1.13x1.000000x1.000000x13.555%13.6%12.021%12.0%47-1.168454x-1.168454x1.17x1.000000x1.000000x13.975%14.0%11.960%12.0%48-1.228758x-1.228758x1.23x1.000000x1.000000x13.883%13.9%11.298%11.3%49-1.276282x-1.276282x1.28x1.000000x1.000000x26.394%26.4%20.680%20.7%50-2.520889x-2.520889x2.52x1.000000x1.000000x17.385%17.4%6.897%6.90%-10.000000x-10.000000x> 10.0x1.000000x1.000000x7.547%7.55%0.000%0%
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#42

Percentage more likely men are than women to have a professional or doctorate degree (e.g., MBA, PhD, or MD) among people aged 25 years and older.
Scope: population of the Golden Township, selected other county subdivisions in the United States, and entities that contain the Golden Township
Female
Male
0x5x10xFM#GoldenZIP 58270El PasoSalt Lk CityWalshPlanoTulsaAlbuquerqueNE DallasFort WorthNorth DakotaOrlandoPhoenixTucson1, CharlotteMidwestNW HarrisLas VegasWest North CentralSeattle EastSan Antonio CntrlSan DiegoUnited StatesSan JoseHempsteadFresnoNE TarrantTampaSan Fernando VlyE San Gabriel VlyHoustonSan BernardinoColumbusMiamiAtlantaMilwaukeeAnaheim-Santa Ana-Gdn GrvAnaheim-Santa An…SacramentoPhiladelphiaOceanside-EscondidoOceanside-Escond…Los AngelesAustinBaltimoreWashingtonQueensSW DallasBrooklynManhattanChicagoSeattleBostonBronxOntarioLong Bch-LakewoodDenverSan FranciscoDetroit-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%9.091%9.09%-1.000000x-1.000000x2.748931x2.748931x2.75x1.482%1.48%4.075%4.08%-1.000000x-1.000000x2.132933x2.132933x2.13x1.146%1.15%2.445%2.44%1-1.000000x-1.000000x2.053360x2.053360x2.05x2.488%2.49%5.109%5.11%2-1.000000x-1.000000x1.971067x1.971067x1.97x0.970%0.97%1.912%1.91%-1.000000x-1.000000x1.782054x1.782054x1.78x3.355%3.35%5.978%5.98%3-1.000000x-1.000000x1.720683x1.720683x1.72x2.338%2.34%4.022%4.02%4-1.000000x-1.000000x1.707718x1.707718x1.71x3.458%3.46%5.905%5.91%5-1.000000x-1.000000x1.692229x1.692229x1.69x2.857%2.86%4.836%4.84%6-1.000000x-1.000000x1.684272x1.684272x1.68x1.637%1.64%2.757%2.76%7-1.000000x-1.000000x1.674474x1.674474x1.67x1.815%1.81%3.038%3.04%-1.000000x-1.000000x1.660799x1.660799x1.66x2.196%2.20%3.647%3.65%8-1.000000x-1.000000x1.659024x1.659024x1.66x2.280%2.28%3.783%3.78%9-1.000000x-1.000000x1.621815x1.621815x1.62x3.429%3.43%5.561%5.56%10-1.000000x-1.000000x1.611479x1.611479x1.61x2.711%2.71%4.369%4.37%11-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.579303x1.579303x1.58x2.100%2.10%3.317%3.32%12-1.000000x-1.000000x1.576623x1.576623x1.58x1.677%1.68%2.643%2.64%13-1.000000x-1.000000x1.572017x1.572017x1.57x2.329%2.33%3.662%3.66%-1.000000x-1.000000x1.557664x1.557664x1.56x4.733%4.73%7.372%7.37%14-1.000000x-1.000000x1.540473x1.540473x1.54x1.423%1.42%2.192%2.19%15-1.000000x-1.000000x1.529565x1.529565x1.53x4.164%4.16%6.370%6.37%16-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.514156x1.514156x1.51x4.979%4.98%7.539%7.54%17-1.000000x-1.000000x1.488724x1.488724x1.49x3.608%3.61%5.371%5.37%18-1.000000x-1.000000x1.484269x1.484269x1.48x2.293%2.29%3.404%3.40%19-1.000000x-1.000000x1.481801x1.481801x1.48x2.138%2.14%3.168%3.17%20-1.000000x-1.000000x1.476874x1.476874x1.48x3.518%3.52%5.196%5.20%21-1.000000x-1.000000x1.460302x1.460302x1.46x2.944%2.94%4.299%4.30%22-1.000000x-1.000000x1.451775x1.451775x1.45x2.041%2.04%2.963%2.96%23-1.000000x-1.000000x1.424636x1.424636x1.42x3.092%3.09%4.404%4.40%24-1.000000x-1.000000x1.423621x1.423621x1.42x1.501%1.50%2.137%2.14%25-1.000000x-1.000000x1.413654x1.413654x1.41x2.801%2.80%3.959%3.96%26-1.000000x-1.000000x1.395174x1.395174x1.40x3.384%3.38%4.722%4.72%27-1.000000x-1.000000x1.391590x1.391590x1.39x5.799%5.80%8.070%8.07%28-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%29-1.000000x-1.000000x1.362600x1.362600x1.36x2.347%2.35%3.199%3.20%30-1.000000x-1.000000x1.359928x1.359928x1.36x2.888%2.89%3.927%3.93%31-1.000000x-1.000000x1.341930x1.341930x1.34x3.216%3.22%4.316%4.32%32-1.000000x-1.000000x1.310218x1.310218x1.31x3.011%3.01%3.945%3.95%33-1.000000x-1.000000x1.308108x1.308108x1.31x4.045%4.05%5.292%5.29%34-1.000000x-1.000000x1.303834x1.303834x1.30x4.384%4.38%5.716%5.72%35-1.000000x-1.000000x1.303196x1.303196x1.30x4.332%4.33%5.645%5.65%36-1.000000x-1.000000x1.300117x1.300117x1.30x10.922%10.9%14.199%14.2%37-1.000000x-1.000000x1.285200x1.285200x1.29x2.428%2.43%3.121%3.12%38-1.000000x-1.000000x1.265486x1.265486x1.27x1.515%1.51%1.917%1.92%39-1.000000x-1.000000x1.262281x1.262281x1.26x3.021%3.02%3.814%3.81%40-1.000000x-1.000000x1.245683x1.245683x1.25x9.673%9.67%12.050%12.1%41-1.000000x-1.000000x1.238186x1.238186x1.24x4.026%4.03%4.985%4.99%42-1.000000x-1.000000x1.228890x1.228890x1.23x5.961%5.96%7.326%7.33%43-1.000000x-1.000000x1.221037x1.221037x1.22x6.643%6.64%8.112%8.11%44-1.000000x-1.000000x1.202385x1.202385x1.20x1.526%1.53%1.834%1.83%45-1.000000x-1.000000x1.198236x1.198236x1.20x2.119%2.12%2.539%2.54%46-1.000000x-1.000000x1.181998x1.181998x1.18x2.766%2.77%3.269%3.27%47-1.000000x-1.000000x1.129665x1.129665x1.13x5.378%5.38%6.075%6.08%48-1.000000x-1.000000x1.098720x1.098720x1.10x7.194%7.19%7.905%7.90%49-1.000000x-1.000000x1.046557x1.046557x1.05x1.291%1.29%1.351%1.35%50
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 50 by male-to-female sex ratio

Definitions

Educational attainment is reported in terms of the highest level of education obtained by persons aged 25 years and older. On this page, we report on both detailed and coarse categorizations of the levels of education in the population. The detailed levels of educational attainment are as follows:

  • None: no formal education at all
  • Less than High School: some education, but stopped short of high school
  • Some High School: some high school education, but no high school diploma (or equivalent)
  • Some College: high school diploma (or equivalent), and some college, but no post-secondary degree
  • Associate's Degree: highest post-secondary degree is an associate's degree (including occupational or academic degrees)
  • Bachelor's Degree: highest post-secondary degree is a bachelor's degree (e.g., BA, BS, AB)
  • Master's Degree: highest post-secondary degree is a master's degree (e.g., MA, MS, MENG, MSW)
  • Professional Degree: highest post-secondary degree is a professional degree (e.g., MD, DDC, JD)
  • Doctorate Degree: highest post-secondary degree is a doctorate degree (e.g., PhD, EdD)

The coarse levels of educational attainment are as follows:

  • No High School Diploma: does not have a high school diploma or equivalent
  • High School Diploma: has a high school diploma or equivalent and possibly some college, but no post-secondary degrees
  • Higher Degree: has some post-secondary degree (associate's, bachelor's, master's, professional, doctorate)

For additional information about the data presented on this site, including our sources, please see the About Page.

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State:
North Dakota
County:
Walsh
ZIP Code:
58270
Unified School Districts:
Park River 78, Valley-Edinburg 118
Congressional District:
ND-At Large
State Senate District:
ND-10
State House District:
ND-10
Neighboring Townships:
Kensington, Lampton, Norton, Tiber, Vernon, Vesta
Nearby Townships:
Dundee, Fertile, Latona, Medford, Rushford