Educational Attainment in Brown Deer, Milwaukee County, Wisconsin (Village)

Educational Attainment#1

Highest level of education among people aged 25 years and older.
Scope: population of Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
0%10%20%30%40%CountHigher Degree1H.S. Diploma2No H.S. Diploma245.818182%37.465008%45.818182%45.8%4,03247.977273%49.442138%47.977273%48.0%4,2226.204545%13.092854%6.204545%6.2%546
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 Milwaukee County at large.
Scope: population of Milwaukee County and Brown Deer
-50%0%+50%%ref.Higher Degree1H.S. Diploma2No H.S. Diploma222.295934%22.295934%+22.3%45.818%45.8%37.465%37.5%-2.962787%-2.962787%-3.0%47.977%48.0%49.442%49.4%-52.611207%-52.611207%-52.6%6.205%6.20%13.093%13.1%
% percentage with given level in Brown Deer
ref. percentage with given level in Milwaukee 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 Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
0%5%10%15%20%25%CountDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None1.613636%1.164548%1.613636%1.6%1421.715909%1.821263%1.715909%1.7%1518.715909%7.552939%8.715909%8.7%76725.000000%19.209501%25.000000%25.0%2,2008.772727%7.716757%8.772727%8.8%77225.931818%21.525107%25.931818%25.9%2,28222.045455%27.917031%22.045455%22.0%1,9404.511364%8.488465%4.511364%4.5%3971.375000%3.119755%1.375000%1.4%1210.318182%1.484634%0.318182%0.3%28
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 Milwaukee County at large.
Scope: population of Milwaukee County and Brown Deer
-50%0%+50%%ref.Doctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None38.563317%38.563317%+38.6%1.614%1.61%1.165%1.16%-5.784637%-5.784637%-5.8%1.716%1.72%1.821%1.82%15.397576%15.397576%+15.4%8.716%8.72%7.553%7.55%30.143930%30.143930%+30.1%25.000%25.0%19.210%19.2%13.684124%13.684124%+13.7%8.773%8.77%7.717%7.72%20.472426%20.472426%+20.5%25.932%25.9%21.525%21.5%-21.032239%-21.032239%-21.0%22.045%22.0%27.917%27.9%-46.853007%-46.853007%-46.9%4.511%4.51%8.488%8.49%-55.926030%-55.926030%-55.9%1.375%1.38%3.120%3.12%-78.568334%-78.568334%-78.6%0.318%0.32%1.485%1.48%
% percentage with given level in Brown Deer
ref. percentage with given level in Milwaukee 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 Milwaukee County and Brown Deer
More Females More Males
Milwaukee County
Brown Deer
0%10%20%30%FMHigher Degree1H.S. Diploma2No H.S. Diploma2-5.736445%0.901503%0.901503%1%45.630%45.6%46.041%46.0%-4.596945%-4.596945%5%1.435259%48.962%49.0%46.811%46.8%32.185839%11.101296%32.185839%32%5.408%5.41%7.148%7.15%
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 Milwaukee County and Brown Deer
More Females More Males
Milwaukee County
Brown Deer
0%200%400%FMDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None28.864994%41.183942%28.864994%29%1.425%1.43%1.837%1.84%420.186682%54.685369%420.186682%420%0.587%0.59%3.053%3.05%-38.901915%-25.089758%-38.901915%39%9.998%10.00%7.198%7.20%-0.045791%-5.741409%-0.045791%0%25.005%25.0%24.994%25.0%-7.202553%4.010583%4.010583%4%8.615%8.61%8.960%8.96%-14.427076%-2.303568%-14.427076%14%27.520%27.5%24.051%24.1%6.146542%4.367805%6.146542%6%21.442%21.4%22.760%22.8%54.905280%14.024522%54.905280%55%3.605%3.61%5.585%5.58%-28.430885%-28.430885%28%9.179945%1.530%1.53%1.191%1.19%-0.672341%36.634401%36.634401%37%0.272%0.27%0.372%0.37%
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 Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
0%10%20%30%B.X.Total65+45-6435-4425-3416.944444%12.570578%16.944444%16.9%3662,16025.463223%17.008996%25.463223%25.5%9073,56223.554736%20.529209%23.554736%23.6%3831,62637.465565%26.449637%37.465565%37.5%5441,452
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 Milwaukee County at large.
Scope: population of Milwaukee County and Brown Deer
0%10%20%30%40%%ref.65+45-6435-4425-3434.794477%34.794477%34.8%16.944%16.9%12.571%12.6%49.704441%49.704441%49.7%25.463%25.5%17.009%17.0%14.737666%14.737666%14.7%23.555%23.6%20.529%20.5%41.648689%41.648689%41.6%37.466%37.5%26.450%26.4%
% percentage in Brown Deer with a Bachelor's degree
ref percentage in Milwaukee 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 Milwaukee County and Brown Deer
More Females More Males
Milwaukee County
Brown Deer
60%40%20%0%20%40%60%FM65+45-6435-4425-342.057792%12.604498%2.057792%2.1%16.791%16.8%17.137%17.1%-7.624074%6.571818%6.571818%6.6%24.670%24.7%26.292%26.3%-9.159450%63.902439%63.902439%63.9%18.304%18.3%30.000%30.0%-60.359749%-14.917892%-60.359749%60.4%44.366%44.4%27.667%27.7%
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 Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
$0k$20k$40k$60kCount%Graduate Degree1Bachelor's DegreeSome CollegeH.S. Diploma2Total$62,279.000000$61,819.000000$62,279.000000$62.3k1,06012.045%12.0%$41,088.000000$47,021.000000$41,088.000000$41.1k2,20025.000%25.0%$37,647.000000$31,302.000000$37,647.000000$37.6k3,05434.705%34.7%$32,298.000000$27,290.000000$32,298.000000$32.3k1,94022.045%22.0%$39,082.000000$35,364.000000$39,082.000000$39.1k8,800100.000%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 Milwaukee County and Brown Deer
Female Male
Milwaukee County
Brown Deer
Shaded bar tips show excess over facing bar.
$50k$0k$50kFMGraduate Degree1Bachelor's DegreeSome CollegeH.S. Diploma2Total$-54,799.000000$-55,738.000000$-54,799.000000$54.8k$54,799.000000$8,237.000000$55,738.000000$13,799.000000$63,036.000000$63.0k573487$-36,960.000000$-41,982.000000$-36,960.000000$37.0k$36,960.000000$7,078.000000$41,982.000000$11,670.000000$44,038.000000$44.0k1,1931,007$-29,532.000000$-27,720.000000$-29,532.000000$29.5k$29,532.000000$26,059.000000$27,720.000000$9,789.000000$55,591.000000$55.6k1,7241,330$-31,845.000000$-23,548.000000$-31,845.000000$31.8k$31,845.000000$3,189.000000$23,548.000000$7,809.000000$35,034.000000$35.0k1,023917$-32,948.000000$-31,167.000000$-32,948.000000$32.9k$32,948.000000$14,042.000000$31,167.000000$9,085.000000$46,990.000000$47.0k4,7714,029
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

Composition of the Armed Forces by Educational Attainment#12

Percentage of the armed forces aged 25 to 64 years old with various highest levels of educational attainment.
Scope: population of Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
0%20%40%60%80%100%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma30.000000%0.000000%0.0%46.538462%0100.000000%49.615385%100.000000%100.0%120.000000%0.000000%0.0%3.846154%00.000000%0.000000%0.000000%0.0%0
Count members of the armed forces with the given level of educational attainment
1or higher 2two or four year institution 3H.S. = High School

Employment by Educational Attainment#13

Percentage of population that is employed by highest level of educational attainment among the population aged 25 to 64 years old.
Scope: population of Milwaukee County and Brown Deer
Brown Deer
Milwaukee County
0%20%40%60%80%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma383.935290%86.597737%83.935290%83.9%2,23175.863470%74.714767%75.863470%75.9%1,86777.596899%65.937507%77.596899%77.6%1,00138.961039%47.259876%38.961039%39.0%90
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#14

Percent of racial or ethnic group lacking a high school diploma (or equivalent).
Scope: population of Milwaukee County and Brown Deer
Female Male
Milwaukee County
Brown Deer
Shaded bar tips show excess over facing bar.
30%20%10%0%10%20%30%MFWhite1BlackAsianHispanic2Other1-1.047120%-5.992923%-1.047120%1.0%1.047120%4.338044%5.992923%0.476947%5.385164%5.4%15128-9.756098%-18.018911%-9.756098%9.8%9.756098%1.060679%18.018911%2.722450%10.816777%10.8%98160-23.129252%-9.406633%-16.163004%-8.848287%-32.535885%32.5%23.129252%16.163004%23.129252%23.1%3468-0.000000%-33.361162%0.000000%0.0%0.000000%3.546099%33.361162%5.713973%3.546099%3.5%50-0.000000%-1.030928%-28.239203%-1.030928%1.0%0.000000%28.239203%7.870881%0.000000%0.0%02
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

College Graduates By Race#15

Percent of racial or ethnic group with a bachelor's degree or higher.
Scope: population of Milwaukee County and Brown Deer
Female Male
Milwaukee County
Brown Deer
Shaded bar tips show excess over facing bar.
40%20%0%20%40%MFWhite1BlackAsianHispanic2Other1-40.276739%-37.923789%-1.355723%-40.276739%40.3%40.276739%5.978611%37.923789%46.255350%46.3%1,2971,077-16.445916%-11.419938%-10.553804%-4.240540%-27.865854%27.9%16.445916%10.553804%16.445916%16.4%149457-21.768707%-28.470527%-39.228615%-50.239234%50.2%21.768707%39.228615%7.071751%21.768707%21.8%32105-11.347518%-8.652482%-9.265499%-4.414098%-20.000000%20.0%11.347518%9.265499%11.347518%11.3%1626-0.000000%-52.061856%-11.298928%-5.212173%-52.061856%52.1%0.000000%11.298928%0.000000%0.0%0101
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 Brown Deer

Coarse: Post-Secondary Degree Educational Attainment by Tract#16

Percentage of the population 25 years and older with given highest level of educational attainment
43.2%44.3%45.5%46.6%47.7%48.9%

Coarse: High School Diploma Educational Attainment by Tract#17

Percentage of the population 25 years and older with given highest level of educational attainment
46.97%47.35%47.72%48.10%48.47%48.85%

Coarse: No High School Diploma Educational Attainment by Tract#18

Percentage of the population 25 years and older with given highest level of educational attainment
4.2%5.0%5.7%6.4%7.2%8.0%

Detailed: Doctorate Degree Educational Attainment by Tract#19

Percentage of the population 25 years and older with given highest level of educational attainment
1.08%1.31%1.54%1.77%2.00%2.23%

Detailed: Professional Degree Educational Attainment by Tract#20

Percentage of the population 25 years and older with given highest level of educational attainment
1.39%1.52%1.63%1.75%1.87%2.00%

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

Percentage of the population 25 years and older with given highest level of educational attainment
7.75%8.17%8.58%9.00%9.41%9.83%

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

Percentage of the population 25 years and older with given highest level of educational attainment
22.3%23.5%24.6%25.8%27.0%28.2%

Detailed: Associate's Degree Educational Attainment by Tract#23

Percentage of the population 25 years and older with given highest level of educational attainment
7.2%7.8%8.4%9.0%9.5%10.1%

Detailed: Some College Educational Attainment by Tract#24

Percentage of the population 25 years and older with given highest level of educational attainment
23.1%24.2%25.2%26.3%27.3%28.4%

Detailed: High School Diploma or Equivalent Educational Attainment by Tract#25

Percentage of the population 25 years and older with given highest level of educational attainment
20.5%21.2%21.8%22.5%23.1%23.9%

Detailed: Some High School Educational Attainment by Tract#26

Percentage of the population 25 years and older with given highest level of educational attainment
2.8%3.4%4.1%4.7%5.3%6.0%

Detailed: Less than High School Educational Attainment by Tract#27

Percentage of the population 25 years and older with given highest level of educational attainment
1.07%1.19%1.30%1.41%1.52%1.64%

Detailed: None Educational Attainment by Tract#28

Percentage of the population 25 years and older with given highest level of educational attainment
0.31799%0.31808%0.31816%0.31824%0.31831%0.31840%
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Educational Attainment by County Subdivision in the Milwaukee Area

There are 96 county subdivisions in the Milwaukee Area. This section compares Brown Deer to the 50 most populous county subdivisions in the Milwaukee Area and to those entities that contain or substantially overlap with Brown Deer. The least populous of the compared county subdivisions has a population of 4,783.

No H.S. Diploma by County Subdivision#29

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Brown Deer, selected other county subdivisions in the Milwaukee Area, and entities that contain Brown Deer
0%5%10%15%Count#MilwaukeeMilwaukee CountyMilwaukeeUnited States of AmericaUnited StatesSt. FrancisCudahyEast North CentralMidwestWest AllisMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeSouth MilwaukeeS MilwaukeeWisconsinGreenfieldFranklinWaukeshaHartfordWest BendSlingerJacksonBrown DeerBrown Deer School DistrictBrown DeerBrown DeerOak CreekHales CornersHartlandGermantownGlendaleMenomonee FallsPort WashingtonTown of WaukeshaWaukeshaTown of BrookfieldBrookfieldNew BerlinOconomowocSummitSussexMuskegoGraftonTown of OconomowocOconomowocTown of LisbonLisbonWauwatosaGreendalePewaukeeRichfieldCedarburgMukwonagoDelafieldBrookfieldTown of VernonVernonTown of MukwonagoMukwonagoElm GroveTown of GeneseeGeneseeShorewoodWhitefish BayMequonFox PointTown of MertonMertonPewaukeeTown of CedarburgCedarburgTown of DelafieldDelafield17.485105%17.485105%17.5%63,80063.8k113.092854%13.092854%13.1%81,52281.5k13.020590%13.020590%13.0%27,818,38027.8M11.382006%11.382006%11.4%859211.082337%11.082337%11.1%1,455310.719464%10.719464%10.7%3,363,7843.36M10.244520%10.244520%10.2%4,641,8354.64M10.028497%10.028497%10.0%4,39949.572772%9.572772%9.6%100,754101k9.190270%9.190270%9.2%1,37958.637220%8.637220%8.6%336,096336k8.204982%8.204982%8.2%2,29667.392323%7.392323%7.4%1,92477.158983%7.158983%7.2%3,39387.103195%7.103195%7.1%67896.565400%6.565400%6.6%1,422106.437518%6.437518%6.4%221116.248676%6.248676%6.2%295126.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.204545%6.204545%6.2%546135.765641%5.765641%5.8%1,421145.418012%5.418012%5.4%302155.411725%5.411725%5.4%324165.227570%5.227570%5.2%719174.601195%4.601195%4.6%439184.484909%4.484909%4.5%1,165194.357654%4.357654%4.4%347204.353215%4.353215%4.4%281214.339461%4.339461%4.3%203224.236891%4.236891%4.2%1,267233.988090%3.988090%4.0%442243.883495%3.883495%3.9%124253.648037%3.648037%3.6%250263.554446%3.554446%3.6%598273.539607%3.539607%3.5%290283.490760%3.490760%3.5%221293.474753%3.474753%3.5%267303.390471%3.390471%3.4%1,167313.368560%3.368560%3.4%357323.203803%3.203803%3.2%310333.118254%3.118254%3.1%260343.102166%3.102166%3.1%242352.819914%2.819914%2.8%145362.818329%2.818329%2.8%139372.806979%2.806979%2.8%761382.798574%2.798574%2.8%157392.790041%2.790041%2.8%158402.671933%2.671933%2.7%115412.638727%2.638727%2.6%136422.374614%2.374614%2.4%223432.149733%2.149733%2.1%201442.107701%2.107701%2.1%355451.951017%1.951017%2.0%94461.940402%1.940402%1.9%112471.525540%1.525540%1.5%89481.091193%1.091193%1.1%42491.017592%1.017592%1.0%5950
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#30

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Brown Deer, selected other county subdivisions in the Milwaukee Area, and entities that contain Brown Deer
0%20%40%60%Count#Fox PointElm GroveWhitefish BayShorewoodMequonWauwatosaBrookfieldCedarburgTown of DelafieldDelafieldDelafieldPewaukeeGlendaleSummitTown of MertonMertonTown of CedarburgCedarburgTown of BrookfieldBrookfieldOconomowocTown of OconomowocOconomowocMenomonee FallsNew BerlinGreendaleGraftonSussexGermantownRichfieldHales CornersTown of GeneseeGeneseeHartlandFranklinPort WashingtonBrown DeerBrown Deer School DistrictBrown DeerBrown DeerTown of WaukeshaWaukeshaPewaukeeTown of MukwonagoMukwonagoWaukeshaMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeMuskegoTown of LisbonLisbonOak CreekUnited States of AmericaUnited StatesMilwaukee CountyMilwaukeeSlingerMukwonagoTown of VernonVernonSt. FrancisMidwestGreenfieldEast North CentralWisconsinWest BendJacksonHartfordWest AllisMilwaukeeCudahySouth MilwaukeeS Milwaukee76.587796%76.587796%76.6%3,690172.188662%72.188662%72.2%3,107272.160428%72.160428%72.2%6,747367.511447%67.511447%67.5%6,340463.254765%63.254765%63.3%10,65410.7k558.155142%58.155142%58.2%20,01720.0k658.053926%58.053926%58.1%15,73915.7k757.351622%57.351622%57.4%4,474856.881683%56.881683%56.9%3,298952.331711%52.331711%52.3%2,5811051.705250%51.705250%51.7%5,0031151.200084%51.200084%51.2%4,8851250.078296%50.078296%50.1%1,5991349.306999%49.306999%49.3%2,8461447.804625%47.804625%47.8%1,8401544.292433%44.292433%44.3%2,0721644.229902%44.229902%44.2%4,9021744.116253%44.116253%44.1%2,7931841.080228%41.080228%41.1%10,67110.7k1940.780498%40.780498%40.8%12,19512.2k2040.649179%40.649179%40.6%4,3082140.412547%40.412547%40.4%3,3112240.376477%40.376477%40.4%2,7672339.610295%39.610295%39.6%5,4482439.385944%39.385944%39.4%3,2842539.092214%39.092214%39.1%2,1792638.125728%38.125728%38.1%1,9652737.898781%37.898781%37.9%2,2692837.841472%37.841472%37.8%9,8492937.310059%37.310059%37.3%2,9713037.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,2603135.879163%35.879163%35.9%2,3163235.755914%35.755914%35.8%2,0863335.740773%35.740773%35.7%2,0243435.550164%35.550164%35.6%16,84916.8k3533.810164%33.810164%33.8%355,854356k33.499762%33.499762%33.5%5,6363632.053618%32.053618%32.1%2,4633731.640023%31.640023%31.6%7,7983830.315023%30.315023%30.3%64,767,78764.8M29.748251%29.748251%29.7%185,226185k29.478590%29.478590%29.5%1,0123929.443796%29.443796%29.4%1,5144029.197861%29.197861%29.2%1,6384129.177156%29.177156%29.2%2,2024228.952816%28.952816%29.0%13,118,64213.1M28.921131%28.921131%28.9%8,0934328.462853%28.462853%28.5%8,931,6868.93M28.373439%28.373439%28.4%1,104,0821.10M27.212706%27.212706%27.2%5,8944426.329168%26.329168%26.3%1,2434525.584075%25.584075%25.6%2,4424623.980394%23.980394%24.0%10,51910.5k4723.517740%23.517740%23.5%85,81285.8k4821.471552%21.471552%21.5%2,8194920.519827%20.519827%20.5%3,07950
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#31

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Brown Deer, selected other county subdivisions in the Milwaukee Area, and entities that contain Brown Deer
0%5%10%15%Count#Fox PointElm GroveShorewoodWhitefish BayMequonBrookfieldWauwatosaGlendaleTown of DelafieldDelafieldCedarburgSummitTown of CedarburgCedarburgTown of GeneseeGeneseeDelafieldOconomowocTown of OconomowocOconomowocTown of MertonMertonTown of BrookfieldBrookfieldNew BerlinGreendalePewaukeeHartlandPewaukeeTown of MukwonagoMukwonagoUnited States of AmericaUnited StatesBrown DeerBrown Deer School DistrictBrown DeerBrown DeerMenomonee FallsMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeePort WashingtonTown of WaukeshaWaukeshaFranklinGermantownMilwaukee CountyMilwaukeeMidwestHales CornersEast North CentralSt. FrancisWisconsinMuskegoRichfieldGraftonTown of VernonVernonWaukeshaMilwaukeeMukwonagoGreenfieldSussexOak CreekSlingerCudahyWest BendWest AllisTown of LisbonLisbonHartfordSouth MilwaukeeS MilwaukeeJackson19.966791%19.966791%20.0%962115.381041%15.381041%15.4%662213.736556%13.736556%13.7%1,290312.064171%12.064171%12.1%1,128411.084724%11.084724%11.1%1,86759.217661%9.217661%9.2%2,49968.094131%8.094131%8.1%2,78676.435384%6.435384%6.4%61486.364264%6.364264%6.4%36995.704397%5.704397%5.7%445105.606013%5.606013%5.6%179114.702520%4.702520%4.7%181124.520761%4.520761%4.5%233134.318735%4.318735%4.3%213144.222683%4.222683%4.2%468154.059390%4.059390%4.1%257164.054054%4.054054%4.1%234173.826422%3.826422%3.8%179183.762039%3.762039%3.8%1,125193.727118%3.727118%3.7%395203.679206%3.679206%3.7%356213.574411%3.574411%3.6%214223.359616%3.359616%3.4%196233.355112%3.355112%3.4%190243.336158%3.336158%3.3%7,127,6737.13M3.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.329545%3.329545%3.3%293253.295350%3.295350%3.3%856263.286157%3.286157%3.3%34,58734.6k3.214869%3.214869%3.2%256273.191325%3.191325%3.2%206283.100626%3.100626%3.1%807292.995492%2.995492%3.0%412302.985811%2.985811%3.0%18,59118.6k2.893416%2.893416%2.9%1,311,0191.31M2.870470%2.870470%2.9%160312.855432%2.855432%2.9%896,039896k2.756062%2.756062%2.8%208322.703886%2.703886%2.7%105,215105k2.674750%2.674750%2.7%450332.494603%2.494603%2.5%208342.441108%2.441108%2.4%200352.424242%2.424242%2.4%136362.192214%2.192214%2.2%1,039372.043126%2.043126%2.0%7,455381.905873%1.905873%1.9%98391.868992%1.868992%1.9%523401.838611%1.838611%1.8%126411.809624%1.809624%1.8%446421.747742%1.747742%1.7%60431.302460%1.302460%1.3%171441.292765%1.292765%1.3%280451.251567%1.251567%1.3%549461.093181%1.093181%1.1%84471.016239%1.016239%1.0%97480.893036%0.893036%0.9%134490.000000%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#32

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 Brown Deer, selected other county subdivisions in the Milwaukee Area, and entities that contain Brown Deer
Female
Male
2x0x2x4xFM#CedarburgPewaukeeHartlandGreendaleDelafieldWaukeshaVernonMukwonagoFranklinGeneseeSummitRichfieldBrown DeerBrown DeerBrown DeerWauwatosaWaukeshaWhitefish BayWisconsinGermantownMuskegoPewaukeeMequonMertonMidwestEast North CentralMilwaukeeWest BendMilwaukeeHartfordUnited StatesPort WashingtonMilwaukeeMenomonee FallsJacksonOak CreekGlendaleGreenfieldBrookfieldNew BerlinHales CornersWest AllisS MilwaukeeCudahyBrookfieldMukwonagoSt. FrancisFox PointOconomowocOconomowocCedarburgSussexLisbonGraftonShorewoodSlingerElm GroveDelafield-1.000000x-1.000000x5.209549x5.209549x5.21x0.356%0.36%1.857%1.86%1-1.000000x-1.000000x2.458344x2.458344x2.46x0.905%0.91%2.225%2.23%2-1.000000x-1.000000x2.349083x2.349083x2.35x3.292%3.29%7.733%7.73%3-1.000000x-1.000000x2.150369x2.150369x2.15x2.213%2.21%4.758%4.76%4-1.000000x-1.000000x2.074982x2.074982x2.07x0.660%0.66%1.370%1.37%5-1.000000x-1.000000x1.893208x1.893208x1.89x3.038%3.04%5.751%5.75%6-1.000000x-1.000000x1.851903x1.851903x1.85x1.962%1.96%3.634%3.63%7-1.000000x-1.000000x1.731706x1.731706x1.73x2.029%2.03%3.514%3.51%8-1.000000x-1.000000x1.578941x1.578941x1.58x5.723%5.72%9.036%9.04%9-1.000000x-1.000000x1.575758x1.575758x1.58x2.043%2.04%3.220%3.22%10-1.000000x-1.000000x1.574372x1.574372x1.57x3.027%3.03%4.765%4.76%11-1.000000x-1.000000x1.494002x1.494002x1.49x2.504%2.50%3.740%3.74%12-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%13-1.000000x-1.000000x1.306431x1.306431x1.31x2.969%2.97%3.879%3.88%14-1.000000x-1.000000x1.304061x1.304061x1.30x6.240%6.24%8.137%8.14%15-1.000000x-1.000000x1.284172x1.284172x1.28x1.899%1.90%2.438%2.44%16-1.000000x-1.000000x1.219291x1.219291x1.22x7.799%7.80%9.509%9.51%-1.000000x-1.000000x1.203474x1.203474x1.20x4.768%4.77%5.738%5.74%17-1.000000x-1.000000x1.159324x1.159324x1.16x3.296%3.30%3.822%3.82%18-1.000000x-1.000000x1.154088x1.154088x1.15x2.980%2.98%3.439%3.44%19-1.000000x-1.000000x1.145574x1.145574x1.15x1.970%1.97%2.257%2.26%20-1.000000x-1.000000x1.136594x1.136594x1.14x1.818%1.82%2.066%2.07%21-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.125371x1.125371x1.13x10.108%10.1%11.375%11.4%-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%22-1.000000x-1.000000x1.118245x1.118245x1.12x6.211%6.21%6.945%6.95%23-1.000000x-1.000000x1.111013x1.111013x1.11x12.441%12.4%13.822%13.8%-1.000000x-1.000000x1.109296x1.109296x1.11x6.741%6.74%7.478%7.48%24-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.103873x1.103873x1.10x4.145%4.14%4.575%4.58%25-1.000000x-1.000000x1.100654x1.100654x1.10x9.134%9.13%10.054%10.1%-1.000000x-1.000000x1.077248x1.077248x1.08x4.329%4.33%4.663%4.66%26-1.000000x-1.000000x1.052843x1.052843x1.05x6.101%6.10%6.423%6.42%27-1.000000x-1.000000x1.048827x1.048827x1.05x5.631%5.63%5.906%5.91%28-1.000000x-1.000000x1.046921x1.046921x1.05x4.505%4.51%4.717%4.72%29-1.000000x-1.000000x1.046668x1.046668x1.05x8.030%8.03%8.405%8.40%30-1.000000x-1.000000x1.013079x1.013079x1.01x4.315%4.31%4.371%4.37%31-1.000000x-1.000000x1.012610x1.012610x1.01x4.211%4.21%4.265%4.26%32-1.023703x-1.023703x1.02x1.000000x1.000000x5.476%5.48%5.349%5.35%33-1.024115x-1.024115x1.02x1.000000x1.000000x10.145%10.1%9.906%9.91%34-1.068390x-1.068390x1.07x1.000000x1.000000x9.477%9.48%8.871%8.87%35-1.082508x-1.082508x1.08x1.000000x1.000000x11.515%11.5%10.638%10.6%36-1.121159x-1.121159x1.12x1.000000x1.000000x2.957%2.96%2.637%2.64%37-1.139343x-1.139343x1.14x1.000000x1.000000x2.993%2.99%2.627%2.63%38-1.204832x-1.204832x1.20x1.000000x1.000000x12.389%12.4%10.283%10.3%39-1.271644x-1.271644x1.27x1.000000x1.000000x2.177%2.18%1.712%1.71%40-1.427909x-1.427909x1.43x1.000000x1.000000x4.664%4.66%3.266%3.27%41-1.450249x-1.450249x1.45x1.000000x1.000000x4.124%4.12%2.843%2.84%42-1.451447x-1.451447x1.45x1.000000x1.000000x3.640%3.64%2.508%2.51%43-1.456932x-1.456932x1.46x1.000000x1.000000x4.308%4.31%2.957%2.96%44-1.479136x-1.479136x1.48x1.000000x1.000000x4.148%4.15%2.804%2.80%45-1.885780x-1.885780x1.89x1.000000x1.000000x4.513%4.51%2.393%2.39%46-1.992381x-1.992381x1.99x1.000000x1.000000x3.093%3.09%1.553%1.55%47-2.473494x-2.473494x2.47x1.000000x1.000000x9.270%9.27%3.748%3.75%48-2.511825x-2.511825x2.51x1.000000x1.000000x3.655%3.66%1.455%1.46%49-2.551624x-2.551624x2.55x1.000000x1.000000x3.951%3.95%1.548%1.55%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

Over-Education Sex Ratio by County Subdivision#33

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 Brown Deer, selected other county subdivisions in the Milwaukee Area, and entities that contain Brown Deer
Female
Male
5x0x5xFM#PewaukeeOconomowocGraftonSussexCudahyDelafieldMuskegoBrown DeerBrown DeerBrown DeerMequonBrookfieldGreendaleRichfieldCedarburgBrookfieldElm GroveMukwonagoDelafieldWaukeshaMertonWhitefish BayNew BerlinWest AllisWauwatosaShorewoodWest BendEast North CentralMidwestMilwaukeeWisconsinFox PointUnited StatesCedarburgHartlandGlendaleMilwaukeeFranklinS MilwaukeeGeneseePewaukeeMenomonee FallsMilwaukeeSt. FrancisSummitGermantownWaukeshaOconomowocGreenfieldPort WashingtonOak CreekHales CornersMukwonagoVernonSlingerLisbonHartford-1.000000x-1.000000x5.376591x5.376591x5.38x1.099%1.10%5.910%5.91%1-1.000000x-1.000000x3.588364x3.588364x3.59x1.781%1.78%6.390%6.39%2-1.000000x-1.000000x3.442806x3.442806x3.44x1.151%1.15%3.962%3.96%3-1.000000x-1.000000x3.350060x3.350060x3.35x0.856%0.86%2.867%2.87%4-1.000000x-1.000000x2.631735x2.631735x2.63x0.722%0.72%1.899%1.90%5-1.000000x-1.000000x2.545378x2.545378x2.55x3.579%3.58%9.110%9.11%6-1.000000x-1.000000x2.519338x2.519338x2.52x1.531%1.53%3.858%3.86%7-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%8-1.000000x-1.000000x2.345235x2.345235x2.35x6.743%6.74%15.813%15.8%9-1.000000x-1.000000x2.342052x2.342052x2.34x2.405%2.41%5.634%5.63%10-1.000000x-1.000000x2.289014x2.289014x2.29x2.351%2.35%5.381%5.38%11-1.000000x-1.000000x2.277148x2.277148x2.28x1.526%1.53%3.475%3.47%12-1.000000x-1.000000x2.209567x2.209567x2.21x2.953%2.95%6.525%6.53%13-1.000000x-1.000000x2.050517x2.050517x2.05x6.177%6.18%12.666%12.7%14-1.000000x-1.000000x1.974359x1.974359x1.97x10.714%10.7%21.154%21.2%15-1.000000x-1.000000x1.913109x1.913109x1.91x1.330%1.33%2.545%2.55%16-1.000000x-1.000000x1.864145x1.864145x1.86x3.069%3.07%5.720%5.72%17-1.000000x-1.000000x1.855485x1.855485x1.86x2.256%2.26%4.185%4.19%18-1.000000x-1.000000x1.725152x1.725152x1.73x2.984%2.98%5.147%5.15%19-1.000000x-1.000000x1.708618x1.708618x1.71x9.075%9.07%15.505%15.5%20-1.000000x-1.000000x1.706236x1.706236x1.71x2.810%2.81%4.794%4.79%21-1.000000x-1.000000x1.693783x1.693783x1.69x0.936%0.94%1.585%1.59%22-1.000000x-1.000000x1.663311x1.663311x1.66x6.193%6.19%10.301%10.3%23-1.000000x-1.000000x1.656396x1.656396x1.66x10.517%10.5%17.420%17.4%24-1.000000x-1.000000x1.632200x1.632200x1.63x0.991%0.99%1.617%1.62%25-1.000000x-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.583700x1.583700x1.58x2.571%2.57%4.071%4.07%-1.000000x-1.000000x1.576114x1.576114x1.58x2.109%2.11%3.323%3.32%-1.000000x-1.000000x1.570537x1.570537x1.57x15.639%15.6%24.561%24.6%26-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.522931x1.522931x1.52x4.569%4.57%6.958%6.96%27-1.000000x-1.000000x1.508421x1.508421x1.51x2.876%2.88%4.339%4.34%28-1.000000x-1.000000x1.496511x1.496511x1.50x5.253%5.25%7.861%7.86%29-1.000000x-1.000000x1.492516x1.492516x1.49x2.423%2.42%3.616%3.62%-1.000000x-1.000000x1.475485x1.475485x1.48x2.501%2.50%3.691%3.69%30-1.000000x-1.000000x1.458132x1.458132x1.46x0.734%0.73%1.070%1.07%31-1.000000x-1.000000x1.442450x1.442450x1.44x3.694%3.69%5.328%5.33%32-1.000000x-1.000000x1.415052x1.415052x1.42x3.061%3.06%4.331%4.33%33-1.000000x-1.000000x1.388151x1.388151x1.39x2.789%2.79%3.872%3.87%34-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%35-1.000000x-1.000000x1.350002x1.350002x1.35x2.361%2.36%3.187%3.19%36-1.000000x-1.000000x1.244466x1.244466x1.24x5.003%5.00%6.226%6.23%37-1.000000x-1.000000x1.211592x1.211592x1.21x2.722%2.72%3.299%3.30%38-1.000000x-1.000000x1.195990x1.195990x1.20x2.002%2.00%2.394%2.39%39-1.000000x-1.000000x1.068496x1.068496x1.07x4.087%4.09%4.367%4.37%40-1.098352x-1.098352x1.10x1.000000x1.000000x1.951%1.95%1.776%1.78%41-1.124115x-1.124115x1.12x1.000000x1.000000x3.400%3.40%3.025%3.02%42-1.142247x-1.142247x1.14x1.000000x1.000000x1.927%1.93%1.687%1.69%43-1.242283x-1.242283x1.24x1.000000x1.000000x3.153%3.15%2.538%2.54%44-1.299297x-1.299297x1.30x1.000000x1.000000x3.804%3.80%2.928%2.93%45-1.387943x-1.387943x1.39x1.000000x1.000000x2.818%2.82%2.031%2.03%46-1.819215x-1.819215x1.82x1.000000x1.000000x2.273%2.27%1.249%1.25%47-2.121025x-2.121025x2.12x1.000000x1.000000x1.487%1.49%0.701%0.70%48-7.565151x-7.565151x7.57x1.000000x1.000000x1.773%1.77%0.234%0.23%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 Wisconsin

There are 1,927 county subdivisions in Wisconsin. This section compares Brown Deer to the 50 most populous county subdivisions in Wisconsin and to those entities that contain or substantially overlap with Brown Deer. The least populous of the compared county subdivisions has a population of 16,289.

No H.S. Diploma by County Subdivision#34

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Brown Deer, selected other county subdivisions in Wisconsin, and entities that contain Brown Deer
0%5%10%15%Count#BeloitRacineMilwaukeeMilwaukee CountyMilwaukeeUnited States of AmericaUnited StatesGreen BaySheboyganKenoshaManitowocCudahyWausauEast North CentralMidwestWest AllisOshkoshBeaver DamMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeSouth MilwaukeeS MilwaukeeMarshfieldFond du LacFitchburgWisconsinJanesvilleMenomonieGreenfieldPleasant PrairieAppletonFranklinWaukeshaTown of Grand ChuteGrand ChuteLa CrosseWisconsin RapidsCaledoniaWest BendSuperiorTown of MenashaMenashaBrown DeerBrown Deer School DistrictBrown DeerBrown DeerAshwaubenonEau ClaireStevens PointHowardMount PleasantMt PleasantNeenahOak CreekGermantownOnalaskaMadisonMenomonee FallsNew BerlinOconomowocSun PrairieDe PereMuskegoWauwatosaBrookfieldMiddletonMequon18.491431%18.491431%18.5%4,197117.907441%17.907441%17.9%8,710217.485105%17.485105%17.5%63,80063.8k313.092854%13.092854%13.1%81,52281.5k13.020590%13.020590%13.0%27,818,38027.8M12.535944%12.535944%12.5%8,414411.643417%11.643417%11.6%3,806511.549265%11.549265%11.5%7,360611.206563%11.206563%11.2%2,609711.082337%11.082337%11.1%1,455810.776184%10.776184%10.8%2,885910.719464%10.719464%10.7%3,363,7843.36M10.244520%10.244520%10.2%4,641,8354.64M10.028497%10.028497%10.0%4,399109.995428%9.995428%10.0%4,154119.950027%9.950027%10.0%1,115129.572772%9.572772%9.6%100,754101k9.190270%9.190270%9.2%1,379138.829004%8.829004%8.8%1,091148.713948%8.713948%8.7%2,547158.662728%8.662728%8.7%1,613168.637220%8.637220%8.6%336,096336k8.522583%8.522583%8.5%3,704178.211921%8.211921%8.2%620188.204982%8.204982%8.2%2,296197.709924%7.709924%7.7%1,111207.618904%7.618904%7.6%3,034217.392323%7.392323%7.4%1,924227.158983%7.158983%7.2%3,393237.144208%7.144208%7.1%1,133247.116644%7.116644%7.1%2,075257.048284%7.048284%7.0%889266.746719%6.746719%6.7%1,208276.565400%6.565400%6.6%1,422286.289585%6.289585%6.3%1,142296.244351%6.244351%6.2%829306.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.168099%6.168099%6.2%739316.128290%6.128290%6.1%2,398326.111996%6.111996%6.1%883336.065319%6.065319%6.1%780346.042598%6.042598%6.0%1,149355.863324%5.863324%5.9%1,015365.765641%5.765641%5.8%1,421375.227570%5.227570%5.2%719384.888290%4.888290%4.9%617394.836248%4.836248%4.8%7,351404.484909%4.484909%4.5%1,165414.236891%4.236891%4.2%1,267423.988090%3.988090%4.0%442433.955766%3.955766%4.0%812443.911088%3.911088%3.9%600453.554446%3.554446%3.6%598463.390471%3.390471%3.4%1,167472.806979%2.806979%2.8%761482.731305%2.731305%2.7%351492.107701%2.107701%2.1%35550
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#35

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Brown Deer, selected other county subdivisions in Wisconsin, and entities that contain Brown Deer
0%20%40%60%Count#MequonMiddletonWauwatosaBrookfieldMadisonFitchburgOconomowocMenomonee FallsSun PrairieNew BerlinGermantownFranklinBrown DeerBrown Deer School DistrictBrown DeerBrown DeerStevens PointDe PereWaukeshaOnalaskaMenomonieMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeMuskegoPleasant PrairieLa CrosseMount PleasantMt PleasantNeenahEau ClaireAppletonTown of Grand ChuteGrand ChuteOak CreekUnited States of AmericaUnited StatesMilwaukee CountyMilwaukeeMidwestGreenfieldCaledoniaTown of MenashaMenashaHowardEast North CentralWisconsinMarshfieldWest BendAshwaubenonWausauOshkoshGreen BayWest AllisFond du LacMilwaukeeKenoshaSuperiorJanesvilleCudahyManitowocSouth MilwaukeeS MilwaukeeSheboyganBeaver DamRacineBeloitWisconsin Rapids63.254765%63.254765%63.3%10,65410.7k158.898140%58.898140%58.9%7,569258.155142%58.155142%58.2%20,01720.0k358.053926%58.053926%58.1%15,73915.7k456.337583%56.337583%56.3%85,63285.6k546.358754%46.358754%46.4%8,632644.229902%44.229902%44.2%4,902741.080228%41.080228%41.1%10,67110.7k841.077605%41.077605%41.1%8,432940.780498%40.780498%40.8%12,19512.2k1039.610295%39.610295%39.6%5,4481137.841472%37.841472%37.8%9,8491237.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26036.471240%36.471240%36.5%5,2691336.151489%36.151489%36.2%5,5461435.550164%35.550164%35.6%16,84916.8k1535.152908%35.152908%35.2%4,4371635.139073%35.139073%35.1%2,6531733.810164%33.810164%33.8%355,854356k33.499762%33.499762%33.5%5,6361833.074254%33.074254%33.1%4,7661932.942347%32.942347%32.9%9,6052032.747831%32.747831%32.7%6,2272132.464907%32.464907%32.5%5,6202232.432916%32.432916%32.4%12,69112.7k2332.296218%32.296218%32.3%12,86112.9k2432.202535%32.202535%32.2%5,1072531.640023%31.640023%31.6%7,7982630.315023%30.315023%30.3%64,767,78764.8M29.748251%29.748251%29.7%185,226185k28.952816%28.952816%29.0%13,118,64213.1M28.921131%28.921131%28.9%8,0932728.919296%28.919296%28.9%5,1782828.909310%28.909310%28.9%3,8382928.794712%28.794712%28.8%3,7033028.462853%28.462853%28.5%8,931,6868.93M28.373439%28.373439%28.4%1,104,0821.10M28.348305%28.348305%28.3%3,5033127.212706%27.212706%27.2%5,8943226.525332%26.525332%26.5%3,1783325.519199%25.519199%25.5%6,8323424.478452%24.478452%24.5%10,17310.2k3524.139215%24.139215%24.1%16,20216.2k3623.980394%23.980394%24.0%10,51910.5k3723.606692%23.606692%23.6%6,9003823.517740%23.517740%23.5%85,81285.8k3922.927488%22.927488%22.9%14,61114.6k4022.415597%22.415597%22.4%4,0704122.035848%22.035848%22.0%9,5774221.471552%21.471552%21.5%2,8194321.017138%21.017138%21.0%4,8934420.519827%20.519827%20.5%3,0794519.530103%19.530103%19.5%6,3844618.623951%18.623951%18.6%2,0874717.525031%17.525031%17.5%8,5244816.605719%16.605719%16.6%3,7694915.880441%15.880441%15.9%2,00350
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#36

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Brown Deer, selected other county subdivisions in Wisconsin, and entities that contain Brown Deer
0%5%10%Count#MequonMiddletonBrookfieldMadisonWauwatosaFitchburgMarshfieldMenomonieLa CrosseStevens PointOnalaskaOconomowocNew BerlinUnited States of AmericaUnited StatesBrown DeerBrown Deer School DistrictBrown DeerBrown DeerMenomonee FallsSun PrairieMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeNeenahFranklinDe PereGermantownMilwaukee CountyMilwaukeeMidwestEau ClaireEast North CentralAppletonWisconsinMuskegoPleasant PrairieBeaver DamWausauTown of Grand ChuteGrand ChuteCaledoniaMount PleasantMt PleasantTown of MenashaMenashaWaukeshaMilwaukeeGreenfieldOak CreekBeloitGreen BayKenoshaWisconsin RapidsJanesvilleOshkoshSuperiorFond du LacHowardCudahyWest BendWest AllisRacineManitowocSouth MilwaukeeS MilwaukeeSheboyganAshwaubenon11.084724%11.084724%11.1%1,867110.022566%10.022566%10.0%1,28829.217661%9.217661%9.2%2,49939.171173%9.171173%9.2%13,94013.9k48.094131%8.094131%8.1%2,78657.196563%7.196563%7.2%1,34065.867120%5.867120%5.9%72574.993377%4.993377%5.0%37784.777583%4.777583%4.8%1,39394.679172%4.679172%4.7%676104.547615%4.547615%4.5%574114.222683%4.222683%4.2%468123.762039%3.762039%3.8%1,125133.336158%3.336158%3.3%7,127,6737.13M3.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.295350%3.295350%3.3%856143.288352%3.288352%3.3%675153.286157%3.286157%3.3%34,58734.6k3.188724%3.188724%3.2%552163.100626%3.100626%3.1%807173.083241%3.083241%3.1%473182.995492%2.995492%3.0%412192.985811%2.985811%3.0%18,59118.6k2.893416%2.893416%2.9%1,311,0191.31M2.869921%2.869921%2.9%1,123202.855432%2.855432%2.9%896,039896k2.794937%2.794937%2.8%1,113212.703886%2.703886%2.7%105,215105k2.674750%2.674750%2.7%450222.637058%2.637058%2.6%380232.552204%2.552204%2.6%286242.416704%2.416704%2.4%647252.396116%2.396116%2.4%380262.356884%2.356884%2.4%422272.303445%2.303445%2.3%438282.244652%2.244652%2.2%298292.192214%2.192214%2.2%1,039302.043126%2.043126%2.0%7,455311.868992%1.868992%1.9%523321.809624%1.809624%1.8%446331.784377%1.784377%1.8%405341.777440%1.777440%1.8%1,193351.762204%1.762204%1.8%1,123361.688734%1.688734%1.7%213371.555418%1.555418%1.6%676381.549604%1.549604%1.5%644391.525582%1.525582%1.5%277401.454035%1.454035%1.5%425411.384137%1.384137%1.4%178421.302460%1.302460%1.3%171431.292765%1.292765%1.3%280441.251567%1.251567%1.3%549451.155451%1.155451%1.2%562461.133972%1.133972%1.1%264470.893036%0.893036%0.9%134480.868820%0.868820%0.9%284490.784576%0.784576%0.8%9450
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#37

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 Brown Deer, selected other county subdivisions in Wisconsin, and entities that contain Brown Deer
Female
Male
1x0x1x2xFM#De PereMenashaOshkoshFranklinCaledoniaManitowocGreen BayPleasant PrairieBrown DeerBrown DeerBrown DeerWauwatosaWaukeshaJanesvilleMt PleasantSuperiorStevens PointAppletonWisconsinRacineGrand ChuteGermantownMadisonMuskegoKenoshaMequonMidwestEast North CentralMilwaukeeWest BendOnalaskaMilwaukeeUnited StatesEau ClaireWisconsin RapidsMilwaukeeMenomonee FallsBeloitBeaver DamLa CrosseHowardOak CreekGreenfieldMiddletonFond du LacMarshfieldNew BerlinSheboyganNeenahFitchburgSun PrairieWest AllisS MilwaukeeCudahyWausauBrookfieldAshwaubenonMenomonieOconomowoc-1.000000x-1.000000x2.147134x2.147134x2.15x2.553%2.55%5.481%5.48%1-1.000000x-1.000000x1.888774x1.888774x1.89x4.337%4.34%8.192%8.19%2-1.000000x-1.000000x1.812682x1.812682x1.81x7.022%7.02%12.729%12.7%3-1.000000x-1.000000x1.578941x1.578941x1.58x5.723%5.72%9.036%9.04%4-1.000000x-1.000000x1.534399x1.534399x1.53x5.333%5.33%8.182%8.18%5-1.000000x-1.000000x1.432549x1.432549x1.43x9.294%9.29%13.313%13.3%6-1.000000x-1.000000x1.408197x1.408197x1.41x10.435%10.4%14.694%14.7%7-1.000000x-1.000000x1.380848x1.380848x1.38x6.493%6.49%8.965%8.97%8-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.306431x1.306431x1.31x2.969%2.97%3.879%3.88%9-1.000000x-1.000000x1.304061x1.304061x1.30x6.240%6.24%8.137%8.14%10-1.000000x-1.000000x1.294088x1.294088x1.29x7.468%7.47%9.664%9.66%11-1.000000x-1.000000x1.293422x1.293422x1.29x5.323%5.32%6.885%6.89%12-1.000000x-1.000000x1.278895x1.278895x1.28x5.545%5.54%7.091%7.09%13-1.000000x-1.000000x1.229327x1.229327x1.23x5.499%5.50%6.760%6.76%14-1.000000x-1.000000x1.219351x1.219351x1.22x6.887%6.89%8.397%8.40%15-1.000000x-1.000000x1.219291x1.219291x1.22x7.799%7.80%9.509%9.51%-1.000000x-1.000000x1.214505x1.214505x1.21x16.245%16.2%19.730%19.7%16-1.000000x-1.000000x1.207489x1.207489x1.21x6.484%6.48%7.830%7.83%17-1.000000x-1.000000x1.203474x1.203474x1.20x4.768%4.77%5.738%5.74%18-1.000000x-1.000000x1.189278x1.189278x1.19x4.425%4.43%5.263%5.26%19-1.000000x-1.000000x1.159324x1.159324x1.16x3.296%3.30%3.822%3.82%20-1.000000x-1.000000x1.152187x1.152187x1.15x10.767%10.8%12.406%12.4%21-1.000000x-1.000000x1.145574x1.145574x1.15x1.970%1.97%2.257%2.26%22-1.000000x-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.125371x1.125371x1.13x10.108%10.1%11.375%11.4%-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%23-1.000000x-1.000000x1.118245x1.118245x1.12x6.211%6.21%6.945%6.95%24-1.000000x-1.000000x1.113721x1.113721x1.11x4.646%4.65%5.175%5.17%25-1.000000x-1.000000x1.111013x1.111013x1.11x12.441%12.4%13.822%13.8%-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.104897x1.104897x1.10x5.834%5.83%6.446%6.45%26-1.000000x-1.000000x1.104741x1.104741x1.10x6.724%6.72%7.428%7.43%27-1.000000x-1.000000x1.100654x1.100654x1.10x9.134%9.13%10.054%10.1%-1.000000x-1.000000x1.077248x1.077248x1.08x4.329%4.33%4.663%4.66%28-1.000000x-1.000000x1.069954x1.069954x1.07x17.920%17.9%19.173%19.2%29-1.000000x-1.000000x1.063348x1.063348x1.06x9.653%9.65%10.265%10.3%30-1.000000x-1.000000x1.059381x1.059381x1.06x6.918%6.92%7.329%7.33%31-1.000000x-1.000000x1.056613x1.056613x1.06x5.903%5.90%6.237%6.24%32-1.000000x-1.000000x1.048827x1.048827x1.05x5.631%5.63%5.906%5.91%33-1.000000x-1.000000x1.046668x1.046668x1.05x8.030%8.03%8.405%8.40%34-1.000000x-1.000000x1.022109x1.022109x1.02x2.704%2.70%2.764%2.76%35-1.000000x-1.000000x1.018058x1.018058x1.02x8.641%8.64%8.797%8.80%36-1.000000x-1.000000x1.015610x1.015610x1.02x8.765%8.76%8.902%8.90%37-1.000000x-1.000000x1.012610x1.012610x1.01x4.211%4.21%4.265%4.26%38-1.000000x-1.000000x1.004509x1.004509x1.00x11.618%11.6%11.670%11.7%39-1.001253x-1.001253x1.00x1.000000x1.000000x5.867%5.87%5.859%5.86%40-1.016031x-1.016031x1.02x1.000000x1.000000x8.734%8.73%8.596%8.60%41-1.022158x-1.022158x1.02x1.000000x1.000000x3.997%4.00%3.910%3.91%42-1.024115x-1.024115x1.02x1.000000x1.000000x10.145%10.1%9.906%9.91%43-1.068390x-1.068390x1.07x1.000000x1.000000x9.477%9.48%8.871%8.87%44-1.082508x-1.082508x1.08x1.000000x1.000000x11.515%11.5%10.638%10.6%45-1.083232x-1.083232x1.08x1.000000x1.000000x11.187%11.2%10.327%10.3%46-1.121159x-1.121159x1.12x1.000000x1.000000x2.957%2.96%2.637%2.64%47-1.237978x-1.237978x1.24x1.000000x1.000000x6.794%6.79%5.488%5.49%48-1.368790x-1.368790x1.37x1.000000x1.000000x9.414%9.41%6.877%6.88%49-1.427909x-1.427909x1.43x1.000000x1.000000x4.664%4.66%3.266%3.27%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

Over-Education Sex Ratio by County Subdivision#38

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 Brown Deer, selected other county subdivisions in Wisconsin, and entities that contain Brown Deer
Female
Male
1x0x1x2x3xFM#NeenahPleasant PrairieCudahyMenomonieOnalaskaMuskegoHowardBrown DeerBrown DeerBrown DeerMarshfieldMequonJanesvilleSheboyganRacineBeaver DamBrookfieldGreen BayBeloitStevens PointWausauFitchburgSun PrairieEau ClaireOshkoshAppletonFond du LacNew BerlinWest AllisWauwatosaWest BendEast North CentralMidwestMilwaukeeWisconsinMt PleasantCaledoniaUnited StatesSuperiorManitowocMilwaukeeFranklinLa CrosseS MilwaukeeMiddletonMenomonee FallsMadisonMilwaukeeDe PereGrand ChuteKenoshaGermantownWaukeshaOconomowocWisconsin RapidsMenashaGreenfieldOak CreekAshwaubenon-1.000000x-1.000000x3.129204x3.129204x3.13x1.585%1.59%4.960%4.96%1-1.000000x-1.000000x2.927208x2.927208x2.93x1.353%1.35%3.961%3.96%2-1.000000x-1.000000x2.631735x2.631735x2.63x0.722%0.72%1.899%1.90%3-1.000000x-1.000000x2.628013x2.628013x2.63x2.819%2.82%7.408%7.41%4-1.000000x-1.000000x2.571855x2.571855x2.57x2.645%2.64%6.802%6.80%5-1.000000x-1.000000x2.519338x2.519338x2.52x1.531%1.53%3.858%3.86%6-1.000000x-1.000000x2.492011x2.492011x2.49x0.802%0.80%1.999%2.00%7-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.351130x2.351130x2.35x3.588%3.59%8.437%8.44%8-1.000000x-1.000000x2.345235x2.345235x2.35x6.743%6.74%15.813%15.8%9-1.000000x-1.000000x2.320788x2.320788x2.32x0.952%0.95%2.209%2.21%10-1.000000x-1.000000x2.236989x2.236989x2.24x0.541%0.54%1.209%1.21%11-1.000000x-1.000000x2.147202x2.147202x2.15x0.747%0.75%1.604%1.60%12-1.000000x-1.000000x2.134058x2.134058x2.13x1.646%1.65%3.514%3.51%13-1.000000x-1.000000x2.050517x2.050517x2.05x6.177%6.18%12.666%12.7%14-1.000000x-1.000000x2.006236x2.006236x2.01x1.188%1.19%2.383%2.38%15-1.000000x-1.000000x2.005610x2.005610x2.01x1.223%1.22%2.453%2.45%16-1.000000x-1.000000x1.994529x1.994529x1.99x3.154%3.15%6.290%6.29%17-1.000000x-1.000000x1.957848x1.957848x1.96x1.658%1.66%3.247%3.25%18-1.000000x-1.000000x1.808864x1.808864x1.81x5.081%5.08%9.190%9.19%19-1.000000x-1.000000x1.806537x1.806537x1.81x2.381%2.38%4.302%4.30%20-1.000000x-1.000000x1.786228x1.786228x1.79x2.083%2.08%3.720%3.72%21-1.000000x-1.000000x1.747870x1.747870x1.75x1.115%1.12%1.949%1.95%22-1.000000x-1.000000x1.733790x1.733790x1.73x2.062%2.06%3.574%3.57%23-1.000000x-1.000000x1.728996x1.728996x1.73x1.083%1.08%1.873%1.87%24-1.000000x-1.000000x1.706236x1.706236x1.71x2.810%2.81%4.794%4.79%25-1.000000x-1.000000x1.693783x1.693783x1.69x0.936%0.94%1.585%1.59%26-1.000000x-1.000000x1.663311x1.663311x1.66x6.193%6.19%10.301%10.3%27-1.000000x-1.000000x1.632200x1.632200x1.63x0.991%0.99%1.617%1.62%28-1.000000x-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.583700x1.583700x1.58x2.571%2.57%4.071%4.07%-1.000000x-1.000000x1.576114x1.576114x1.58x2.109%2.11%3.323%3.32%-1.000000x-1.000000x1.557391x1.557391x1.56x1.833%1.83%2.855%2.85%29-1.000000x-1.000000x1.550147x1.550147x1.55x1.851%1.85%2.870%2.87%30-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.516809x1.516809x1.52x1.222%1.22%1.853%1.85%31-1.000000x-1.000000x1.494707x1.494707x1.49x0.918%0.92%1.372%1.37%32-1.000000x-1.000000x1.492516x1.492516x1.49x2.423%2.42%3.616%3.62%-1.000000x-1.000000x1.475485x1.475485x1.48x2.501%2.50%3.691%3.69%33-1.000000x-1.000000x1.473120x1.473120x1.47x3.888%3.89%5.727%5.73%34-1.000000x-1.000000x1.458132x1.458132x1.46x0.734%0.73%1.070%1.07%35-1.000000x-1.000000x1.452651x1.452651x1.45x8.299%8.30%12.055%12.1%36-1.000000x-1.000000x1.388151x1.388151x1.39x2.789%2.79%3.872%3.87%37-1.000000x-1.000000x1.387878x1.387878x1.39x7.705%7.70%10.693%10.7%38-1.000000x-1.000000x1.368590x1.368590x1.37x1.742%1.74%2.385%2.38%39-1.000000x-1.000000x1.363936x1.363936x1.36x2.638%2.64%3.598%3.60%40-1.000000x-1.000000x1.256412x1.256412x1.26x2.128%2.13%2.674%2.67%41-1.000000x-1.000000x1.229981x1.229981x1.23x1.588%1.59%1.953%1.95%42-1.000000x-1.000000x1.211592x1.211592x1.21x2.722%2.72%3.299%3.30%43-1.000000x-1.000000x1.195990x1.195990x1.20x2.002%2.00%2.394%2.39%44-1.000000x-1.000000x1.068496x1.068496x1.07x4.087%4.09%4.367%4.37%45-1.000000x-1.000000x1.014900x1.014900x1.01x1.677%1.68%1.702%1.70%46-1.000000x-1.000000x1.008002x1.008002x1.01x2.236%2.24%2.254%2.25%47-1.098352x-1.098352x1.10x1.000000x1.000000x1.951%1.95%1.776%1.78%48-1.142247x-1.142247x1.14x1.000000x1.000000x1.927%1.93%1.687%1.69%49-1.623043x-1.623043x1.62x1.000000x1.000000x0.961%0.96%0.592%0.59%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 Midwest

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

No H.S. Diploma by County Subdivision#39

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Brown Deer, selected other county subdivisions in the Midwest, and entities that contain Brown Deer
0%10%20%30%Count#Aurora TownshipAuroraCenter TownshipCtrKansas CityClevelandWayne TownshipWayneDetroitMilwaukeeNorth TownshipNDaytonChicagoProviso TownshipProvisoRockford TownshipRockfordSt. LouisWarrenGrand RapidsToledoBlue TownshipCincinnatiAkronSt. PaulSterling HeightsSterling HtsMilwaukee CountyMilwaukeeUnited States of AmericaUnited StatesThornton TownshipThorntonWichitaKaw TownshipOmahaBloomingdale TownshipBloomingdaleColumbusMaine TownshipMaineTopekaMinneapolisSpringfield TownshipSpringfieldEast North CentralWorth TownshipWorthMidwestLawrence TownshipLawrenceSioux FallsMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeWisconsinCapital TownshipCapitalWheeling TownshipWheelingPalatine TownshipPalatineSchaumburg TownshipSchaumburgLincolnWashington TownshipWashingtonOlatheBrown DeerBrown Deer School DistrictBrown DeerBrown DeerFargoCedar RapidsYork TownshipMilton TownshipMiltonMadisonDowners Grove TownshipDowners GrvLisle 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%12,21212.2k2113.092854%13.092854%13.1%81,52281.5k13.020590%13.020590%13.0%27,818,38027.8M12.974232%12.974232%13.0%14,37014.4k2212.568464%12.568464%12.6%31,23131.2k2312.455744%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%11,14911.1k3110.719464%10.719464%10.7%3,363,7843.36M10.678962%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%9,123349.572772%9.572772%9.6%100,754101k8.637220%8.637220%8.6%336,096336k8.623103%8.623103%8.6%6,837358.424707%8.424707%8.4%9,343368.392669%8.392669%8.4%6,695377.739434%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%5,331416.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.055621%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%4,166473.522812%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#40

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Brown Deer, selected other county subdivisions in the Midwest, and entities that contain Brown Deer
0%20%40%60%Count#Ann ArborOverland ParkMilton TownshipMiltonLisle TownshipLisleMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonPalatine TownshipPalatineYork TownshipMinneapolisOlatheWheeling TownshipWheelingSchaumburg TownshipSchaumburgKaw TownshipSt. PaulMaine TownshipMaineFargoLincolnBrown DeerBrown Deer School DistrictBrown DeerBrown DeerLawrence TownshipLawrenceChicagoCapital TownshipCapitalColumbusOmahaMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeCincinnatiSt. LouisGrand RapidsBloomingdale TownshipBloomingdaleCedar RapidsUnited States of AmericaUnited StatesSioux FallsMilwaukee CountyMilwaukeeMidwestWichitaEast North CentralWisconsinSterling HeightsSterling HtsTopekaSpringfield TownshipSpringfieldProviso TownshipProvisoWorth TownshipWorthMilwaukeeRockford TownshipRockfordAkronCenter TownshipCtrNorth TownshipNThornton TownshipThorntonToledoDaytonWarrenWayne TownshipWayneKansas 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%62,25462.3k1837.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26036.577273%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%99,56199.6k2333.810164%33.810164%33.8%355,854356k33.783048%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%28,45328.5k2929.748251%29.748251%29.7%185,226185k28.952816%28.952816%29.0%13,118,64213.1M28.877567%28.877567%28.9%71,75771.8k3028.462853%28.462853%28.5%8,931,6868.93M28.373439%28.373439%28.4%1,104,0821.10M27.532180%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%20,02920.0k4018.971090%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%14,78814.8k4516.167409%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#41

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Brown Deer, selected other county subdivisions in the Midwest, and entities that contain Brown Deer
0%5%10%15%Count#Ann ArborMadisonDowners Grove TownshipDowners GrvWashington TownshipWashingtonMinneapolisYork TownshipOverland ParkLisle TownshipLisleSt. PaulKaw TownshipMilton TownshipMiltonSt. LouisCincinnatiLincolnChicagoFargoCapital TownshipCapitalOmahaMaine TownshipMainePalatine TownshipPalatineWheeling TownshipWheelingColumbusUnited States of AmericaUnited StatesBrown DeerBrown Deer School DistrictBrown DeerBrown DeerMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeTopekaGrand RapidsLawrence TownshipLawrenceMilwaukee CountyMilwaukeeSpringfield TownshipSpringfieldCedar RapidsMidwestEast North CentralSioux FallsOlatheWisconsinCenter TownshipCtrSchaumburg TownshipSchaumburgWichitaProviso TownshipProvisoAkronBloomingdale TownshipBloomingdaleRockford TownshipRockfordMilwaukeeWorth TownshipWorthClevelandToledoNorth TownshipNSterling 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%9,034134.568292%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%7,127,6737.13M3.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.286157%3.286157%3.3%34,58734.6k3.233245%3.233245%3.2%2,748233.174629%3.174629%3.2%3,846243.083448%3.083448%3.1%2,472252.985811%2.985811%3.0%18,59118.6k2.911721%2.911721%2.9%3,015262.905776%2.905776%2.9%2,477272.893416%2.893416%2.9%1,311,0191.31M2.855432%2.855432%2.9%896,039896k2.841677%2.841677%2.8%2,672282.830666%2.830666%2.8%2,349292.703886%2.703886%2.7%105,215105k2.602914%2.602914%2.6%2,576302.551250%2.551250%2.6%2,382312.519246%2.519246%2.5%6,260322.213918%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%1,606411.439893%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#42

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 Brown Deer, selected other county subdivisions in the Midwest, and entities that contain Brown Deer
Female
Male
1x0x1xFM#Brown DeerBrown DeerBrown DeerCedar RapidsLincolnDetroitOmahaToledoWisconsinThorntonKansas CityMadisonLawrenceFargoProvisoRockfordBlue TownshipWayneMidwestTopekaEast North CentralMilwaukeeKaw TownshipDaytonMilwaukeeSioux FallsUnited StatesOlatheMilwaukeeWorthNOverland ParkAuroraWheelingGrand RapidsSchaumburgChicagoWichitaColumbusClevelandPalatineCapitalWarrenSpringfieldYork TownshipSt. LouisWashingtonBloomingdaleCtrCincinnatiAkronAnn ArborSt. PaulLisleMinneapolisSterling HtsDowners GrvMiltonMaine-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-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-1.000000x1.219291x1.219291x1.22x7.799%7.80%9.509%9.51%-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-1.000000x1.132245x1.132245x1.13x19.779%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-1.000000x1.125371x1.125371x1.13x10.108%10.1%11.375%11.4%-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-1.000000x1.111013x1.111013x1.11x12.441%12.4%13.822%13.8%-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-1.000000x1.100654x1.100654x1.10x9.134%9.13%10.054%10.1%-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
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#43

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 Brown Deer, selected other county subdivisions in the Midwest, and entities that contain Brown Deer
Female
Male
1x0x1x2xFM#Brown DeerBrown DeerBrown DeerRockfordNDaytonSioux FallsCapitalLawrenceDowners GrvAkronLisleWichitaYork TownshipMaineWarrenWheelingBlue TownshipSpringfieldOverland ParkMiltonGrand RapidsOlatheOmahaFargoWashingtonEast North CentralLincolnMidwestBloomingdaleMilwaukeeWisconsinKansas CityAuroraUnited StatesAnn ArborMilwaukeeTopekaSterling HtsPalatineSchaumburgColumbusMadisonKaw TownshipMilwaukeeCedar RapidsClevelandCincinnatiSt. PaulSt. LouisChicagoWayneCtrToledoMinneapolisWorthDetroitProvisoThornton-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-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-1.000000x1.962539x1.962539x1.96x1.928%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-1.000000x1.669388x1.669388x1.67x3.061%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-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-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-1.000000x1.583700x1.583700x1.58x2.571%2.57%4.071%4.07%-1.000000x-1.000000x1.576114x1.576114x1.58x2.109%2.11%3.323%3.32%-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-1.000000x1.492516x1.492516x1.49x2.423%2.42%3.616%3.62%-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 Brown Deer to the 50 most populous county subdivisions in the United States and to those entities that contain or substantially overlap with Brown Deer. The least populous of the compared county subdivisions has a population of 547,300.

No H.S. Diploma by County Subdivision#44

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Brown Deer, selected other county subdivisions in the United States, and entities that contain Brown Deer
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 VegasPhoenixBostonSacramentoOrlandoAustinDenverMilwaukee CountyMilwaukeeUnited States of AmericaUnited StatesManhattanSan DiegoTampaSan JoseSan FranciscoTucsonAlbuquerqueTownship 1:Charlotte1, CharlotteColumbusTulsaEast North CentralSalt Lake CitySalt Lk CityTown of HempsteadHempsteadMidwestNorthwest HarrisNW HarrisWashingtonMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeAtlantaSeattleWisconsinNortheast TarrantNE TarrantBrown DeerBrown Deer School DistrictBrown DeerBrown DeerPlanoSeattle East28.998944%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%63,86563.9k3113.092854%13.092854%13.1%81,52281.5k13.020590%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%84,02484.0k3612.262851%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%43,65343.7k4110.719464%10.719464%10.7%3,363,7843.36M10.435356%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%46,51046.5k459.572772%9.572772%9.6%100,754101k9.538552%9.538552%9.5%37,37337.4k468.833573%8.833573%8.8%66,22066.2k478.637220%8.637220%8.6%336,096336k8.418271%8.418271%8.4%31,44331.4k486.204545%6.204545%6.2%5466.204545%6.204545%6.2%5466.204545%6.204545%6.2%5465.714681%5.714681%5.7%20,64420.6k494.913852%4.913852%4.9%20,32620.3k50
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#45

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Brown Deer, selected other county subdivisions in the United States, and entities that contain Brown Deer
0%20%40%60%Count#ManhattanSeattle EastWashingtonSan FranciscoPlanoSan JoseAtlantaSeattleBostonDenverAustinTownship 1:Charlotte1, CharlotteTown of HempsteadHempsteadSan DiegoNortheast TarrantNE TarrantBrown DeerBrown Deer School DistrictBrown DeerBrown DeerNorthwest HarrisNW HarrisChicagoColumbusLos AngelesBrooklynMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeSan Fernando ValleySan Fernando VlyNortheast DallasNE DallasSalt Lake CitySalt Lk CityAlbuquerqueTampaTucsonOceanside-EscondidoOceanside-Escond…TulsaQueensUnited States of AmericaUnited StatesHoustonMilwaukee CountyMilwaukeeBaltimoreLong Beach-LakewoodLong Bch-LakewoodMidwestPhoenixOrlandoEast North CentralAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…WisconsinSacramentoEast San Gabriel ValleyE San Gabriel VlyOntarioPhiladelphiaMiamiFort WorthSouthwest DallasSW DallasMilwaukeeFresnoLas VegasEl PasoBronxSan 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%143,978144k1537.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26037.045455%37.045455%37.0%3,26036.877886%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%597,685598k2033.810164%33.810164%33.8%355,854356k33.231104%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%627,520628k3029.748251%29.748251%29.7%185,226185k29.719611%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%112,142112k3428.462853%28.462853%28.5%8,931,6868.93M28.412939%28.412939%28.4%312,038312k3528.373439%28.373439%28.4%1,104,0821.10M28.231204%28.231204%28.2%209,283209k3627.276345%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%72,12072.1k4619.131504%19.131504%19.1%173,169173k4716.375473%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#46

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Brown Deer, selected other county subdivisions in the United States, and entities that contain Brown Deer
0%5%10%Count#WashingtonManhattanSan FranciscoBostonAtlantaSeattleSan JoseSeattle EastDenverSan DiegoAustinBaltimoreLos AngelesAlbuquerquePlanoChicagoTucsonTown of HempsteadHempsteadTampaMiamiNortheast DallasNE DallasSalt Lake CitySalt Lk CityHoustonPhiladelphiaSan Fernando ValleySan Fernando VlyTownship 1:Charlotte1, CharlotteOceanside-EscondidoOceanside-Escond…BrooklynSacramentoColumbusUnited States of AmericaUnited StatesBrown DeerBrown Deer School DistrictBrown DeerBrown DeerMilwaukee, Waukesha, and West Allis Metro AreaMilwaukeeTulsaPhoenixLong Beach-LakewoodLong Bch-LakewoodMilwaukee CountyMilwaukeeMidwestOrlandoEast North CentralFresnoAnaheim-Santa Ana-Garden GroveAnaheim-Santa An…QueensWisconsinNorthwest HarrisNW HarrisNortheast TarrantNE TarrantEast San Gabriel ValleyE San Gabriel VlyOntarioFort WorthLas VegasMilwaukeeSan BernardinoSan Antonio CentralSan Antonio CntrlEl PasoSouthwest DallasSW DallasBronxDetroit12.461852%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%20,00420.0k144.617088%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%7,127,6737.13M3.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.329545%3.329545%3.3%2933.286157%3.286157%3.3%34,58734.6k3.140490%3.140490%3.1%12,13312.1k313.010825%3.010825%3.0%62,13562.1k323.009440%3.009440%3.0%11,33911.3k332.985811%2.985811%3.0%18,59118.6k2.893416%2.893416%2.9%1,311,0191.31M2.893233%2.893233%2.9%11,37011.4k342.855432%2.855432%2.9%896,039896k2.828959%2.828959%2.8%11,83311.8k352.763004%2.763004%2.8%30,34430.3k362.756960%2.756960%2.8%44,96545.0k372.703886%2.703886%2.7%105,215105k2.682967%2.682967%2.7%12,62112.6k382.630994%2.630994%2.6%9,827392.484535%2.484535%2.5%15,54715.5k402.324315%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%15,06415.1k491.318660%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#47

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 Brown Deer, selected other county subdivisions in the United States, and entities that contain Brown Deer
Female
Male
1x0x1xFM#Brown DeerBrown DeerBrown Deer1, CharlotteDetroitWisconsinBaltimoreTulsaSW DallasMidwestFresnoEast North CentralMilwaukeeMilwaukeeUnited StatesMilwaukeeNE 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 Paso-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-1.000000x-1.000000x1.321858x1.321858x1.32x5.408%5.41%7.148%7.15%-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-1.000000x1.219291x1.219291x1.22x7.799%7.80%9.509%9.51%-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-1.000000x1.127915x1.127915x1.13x9.647%9.65%10.881%10.9%-1.000000x-1.000000x1.127206x1.127206x1.13x20.830%20.8%23.479%23.5%6-1.000000x-1.000000x1.125371x1.125371x1.13x10.108%10.1%11.375%11.4%-1.000000x-1.000000x1.123984x1.123984x1.12x16.526%16.5%18.574%18.6%7-1.000000x-1.000000x1.111013x1.111013x1.11x12.441%12.4%13.822%13.8%-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.100654x1.100654x1.10x9.134%9.13%10.054%10.1%-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
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#48

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 Brown Deer, selected other county subdivisions in the United States, and entities that contain Brown Deer
Female
Male
1x0x1x2xFM#Brown DeerBrown DeerBrown DeerEl PasoSalt Lk CityPlanoTulsaAlbuquerqueNE DallasFort WorthOrlandoPhoenixTucson1, CharlotteEast North CentralMidwestMilwaukeeNW HarrisLas VegasWisconsinSeattle EastSan Antonio CntrlSan DiegoUnited StatesSan JoseMilwaukeeHempsteadFresnoNE TarrantTampaSan Fernando VlyE San Gabriel VlyHoustonSan BernardinoColumbusMiamiAtlantaMilwaukeeAnaheim-Santa Ana-Gdn GrvAnaheim-Santa An…SacramentoPhiladelphiaOceanside-EscondidoOceanside-Escond…Los AngelesAustinBaltimoreWashingtonQueensSW DallasBrooklynManhattanChicagoSeattleBostonBronxOntarioLong Bch-LakewoodDenverSan FranciscoDetroit-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-1.000000x-1.000000x2.430005x2.430005x2.43x2.012%2.01%4.890%4.89%-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-1.000000x1.782054x1.782054x1.78x3.355%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-1.000000x1.660799x1.660799x1.66x2.196%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-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.583700x1.583700x1.58x2.571%2.57%4.071%4.07%-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-1.000000x1.576114x1.576114x1.58x2.109%2.11%3.323%3.32%-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-1.000000x1.492516x1.492516x1.49x2.423%2.42%3.616%3.62%-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:
Wisconsin
County:
Milwaukee
Metro Area:
Milwaukee Area
Village:
Brown Deer
ZIP Codes:
53209, 53223
Unified School District:
Brown Deer
Neighborhood:
Brown Deer Park
Congressional District:
WI-4
State Senate District:
WI-8
Assembly District:
WI-24