Educational Attainment in Montgomery County, Ohio (County)

Educational Attainment#1

Highest level of education among people aged 25 years and older.
Scope: population of Ohio and Montgomery County
Montgomery County
Ohio
0%10%20%30%40%50%CountHigher Degree1H.S. Diploma2No H.S. Diploma235.923972%35.063314%35.923972%35.9%130,242130k53.706396%54.422540%53.706396%53.7%194,712195k10.369633%10.514146%10.369633%10.4%37,59537.6k
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 Ohio at large.
Scope: population of Ohio and Montgomery County
-2%-1%0%+1%+2%%ref.Higher Degree1H.S. Diploma2No H.S. Diploma22.454582%2.454582%+2.5%35.924%35.9%35.063%35.1%-1.315897%-1.315897%-1.3%53.706%53.7%54.423%54.4%-1.374463%-1.374463%-1.4%10.370%10.4%10.514%10.5%
% percentage with given level in Montgomery County
ref. percentage with given level in Ohio
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 Ohio and Montgomery County
Montgomery County
Ohio
0%5%10%15%20%25%30%CountDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None0.994624%1.076166%0.994624%1.0%3,6061.450287%1.607075%1.450287%1.5%5,2588.013813%7.279494%8.013813%8.0%29,05429.1k15.684501%16.704731%15.684501%15.7%56,86456.9k9.780747%8.395848%9.780747%9.8%35,46035.5k25.334782%20.603422%25.334782%25.3%91,85191.9k28.371613%33.819119%28.371613%28.4%102,861103k7.580217%7.545636%7.580217%7.6%27,48227.5k1.992007%2.095069%1.992007%2.0%7,2220.797409%0.873440%0.797409%0.8%2,891
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 Ohio at large.
Scope: population of Ohio and Montgomery County
-20%0%+20%%ref.Doctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None-7.577065%-7.577065%-7.6%0.995%0.99%1.076%1.08%-9.756138%-9.756138%-9.8%1.450%1.45%1.607%1.61%10.087512%10.087512%+10.1%8.014%8.01%7.279%7.28%-6.107436%-6.107436%-6.1%15.685%15.7%16.705%16.7%16.495047%16.495047%+16.5%9.781%9.78%8.396%8.40%22.963957%22.963957%+23.0%25.335%25.3%20.603%20.6%-16.107769%-16.107769%-16.1%28.372%28.4%33.819%33.8%0.458285%0.458285%+0.5%7.580%7.58%7.546%7.55%-4.919297%-4.919297%-4.9%1.992%1.99%2.095%2.10%-8.704761%-8.704761%-8.7%0.797%0.80%0.873%0.87%
% percentage with given level in Montgomery County
ref. percentage with given level in Ohio
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 Ohio and Montgomery County
More Females More Males
Ohio
Montgomery County
5%0%5%10%FMHigher Degree1H.S. Diploma2No H.S. Diploma2-2.101518%-7.885240%-2.101518%2%36.274%36.3%35.527%35.5%0.689994%2.984493%0.689994%1%53.533%53.5%53.903%53.9%3.700935%10.536340%3.700935%4%10.193%10.2%10.570%10.6%
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 Ohio and Montgomery County
More Females More Males
Ohio
Montgomery County
0%50%100%150%FMDoctorate1Professional1Master's1Bachelor's1Associate's1Some CollegeHigh School2Some H.S.3Less than H.S.3None154.434915%65.373714%154.434915%154%0.577%0.58%1.468%1.47%91.979239%68.015763%91.979239%92%1.014%1.01%1.946%1.95%-10.707514%-21.244079%-10.707514%11%8.394%8.39%7.582%7.58%4.698319%0.726598%4.698319%5%15.347%15.3%16.068%16.1%-29.294695%-39.383112%-29.294695%29%10.942%10.9%8.463%8.46%-2.789340%-0.103999%-2.789340%3%25.661%25.7%24.965%25.0%3.823603%4.911287%3.823603%4%27.872%27.9%28.938%28.9%3.815935%10.693269%3.815935%4%7.447%7.45%7.731%7.73%5.912297%11.385964%5.912297%6%1.938%1.94%2.053%2.05%-2.740880%-2.740880%3%7.203042%0.808%0.81%0.786%0.79%
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 Ohio and Montgomery County
Montgomery County
Ohio
0%5%10%15%20%B.X.Total65+45-6435-4425-3412.214499%11.168673%12.214499%12.2%10,82410.8k88,61688.6k15.478148%15.925332%15.478148%15.5%22,12422.1k142,937143k17.523909%19.733657%17.523909%17.5%10,75610.8k61,37961.4k18.903429%22.259142%18.903429%18.9%13,16013.2k69,61769.6k
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 Ohio at large.
Scope: population of Ohio and Montgomery County
15%10%5%0%5%%ref.65+45-6435-4425-349.363922%9.363922%9.4%12.214%12.2%11.169%11.2%-2.808003%-2.808003%2.8%15.478%15.5%15.925%15.9%-11.197865%-11.197865%11.2%17.524%17.5%19.734%19.7%-15.075663%-15.075663%15.1%18.903%18.9%22.259%22.3%
% percentage in Montgomery County with a Bachelor's degree
ref percentage in Ohio 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 Ohio and Montgomery County
More Females More Males
Ohio
Montgomery County
10%0%10%20%30%40%FM65+45-6435-4425-3448.018831%36.828803%48.018831%48.0%10.163%10.2%15.044%15.0%-0.054549%2.133245%2.133245%2.1%15.322%15.3%15.648%15.6%-19.093409%-14.012939%-19.093409%19.1%19.004%19.0%15.957%16.0%-8.640759%-14.499975%-8.640759%8.6%19.675%19.7%18.110%18.1%
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 Ohio and Montgomery County
Montgomery County
Ohio
$0k$20k$40k$60kCount%Graduate Degree1Bachelor's DegreeSome CollegeH.S. Diploma2No H.S. Diploma2Total$64,295.000000$63,821.000000$64,295.000000$64.3k37,91837.9k10.459%10.5%$46,659.000000$50,107.000000$46,659.000000$46.7k56,86456.9k15.685%15.7%$31,290.000000$32,706.000000$31,290.000000$31.3k127,311127k35.116%35.1%$26,078.000000$28,919.000000$26,078.000000$26.1k102,861103k28.372%28.4%$18,645.000000$20,344.000000$18,645.000000$18.6k37,59537.6k10.370%10.4%$33,622.000000$35,841.000000$33,622.000000$33.6k362,549363k100.000%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 Ohio and Montgomery County
Female Male
Ohio
Montgomery County
Shaded bar tips show excess over facing bar.
$50k$0k$50kFMGraduate Degree1Bachelor's DegreeSome CollegeH.S. Diploma2No H.S. Diploma2Total$-56,381.000000$-55,571.000000$-56,381.000000$56.4k$56,381.000000$20,933.000000$55,571.000000$22,109.000000$77,314.000000$77.3k19,24019.2k18,67818.7k$-40,185.000000$-40,856.000000$-40,185.000000$40.2k$40,185.000000$14,888.000000$40,856.000000$20,405.000000$55,073.000000$55.1k29,57229.6k27,29227.3k$-26,764.000000$-27,361.000000$-26,764.000000$26.8k$26,764.000000$10,727.000000$27,361.000000$13,780.000000$37,491.000000$37.5k70,53270.5k56,77956.8k$-21,068.000000$-22,081.000000$-21,068.000000$21.1k$21,068.000000$10,319.000000$22,081.000000$13,410.000000$31,387.000000$31.4k53,70853.7k49,15349.2k$-15,635.000000$-15,062.000000$-15,635.000000$15.6k$15,635.000000$6,288.000000$15,062.000000$9,409.000000$21,923.000000$21.9k19,64119.6k17,95418.0k$-28,928.000000$-29,749.000000$-28,928.000000$28.9k$28,928.000000$11,556.000000$29,749.000000$12,566.000000$40,484.000000$40.5k192,693193k169,856170k
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 Ohio and Montgomery County
Montgomery County
Ohio
0%10%20%30%40%50%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma354.139194%51.152074%54.139194%54.1%73941.831502%41.442873%41.831502%41.8%5712.344322%7.039568%2.344322%2.3%321.684982%0.365485%1.684982%1.7%23
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 Ohio and Montgomery County
Montgomery County
Ohio
0%20%40%60%80%CountBachelor's Degree1Some College2H.S. Diploma3No H.S. Diploma384.147917%84.712194%84.147917%84.1%63,16963.2k73.184747%75.098475%73.184747%73.2%75,65475.7k64.520690%67.335954%64.520690%64.5%46,54246.5k40.774995%44.096181%40.774995%40.8%9,523
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 Ohio and Montgomery County
Female Male
Ohio
Montgomery County
Shaded bar tips show excess over facing bar.
20%10%0%10%20%MFWhite1BlackAsianHispanic2Other1-8.957880%-8.849949%-8.957880%9.0%8.957880%0.144171%8.849949%0.838407%9.102050%9.1%11,80911.8k12,83712.8k-13.269718%-14.710486%-13.269718%13.3%13.269718%1.613927%14.710486%2.396424%14.883645%14.9%4,5735,187-11.156123%-6.490935%-10.995328%-3.238870%-17.647059%17.6%11.156123%10.995328%11.156123%11.2%358714-19.697834%-23.269744%-19.697834%19.7%19.697834%6.828186%23.269744%5.740390%26.526020%26.5%943691-9.596879%-16.869350%-9.596879%9.6%9.596879%5.938404%16.869350%3.904344%15.535283%15.5%579369
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 Ohio and Montgomery County
Female Male
Ohio
Montgomery County
Shaded bar tips show excess over facing bar.
40%20%0%20%40%60%MFWhite1BlackAsianHispanic2Other1-26.972031%-27.409880%-26.972031%27.0%26.972031%1.706094%27.409880%0.383981%28.678125%28.7%37,20737.2k38,65238.7k-16.654190%-1.159005%-14.337282%-3.326272%-17.813196%17.8%16.654190%14.337282%16.654190%16.7%5,1176,963-45.328720%-55.571247%-45.328720%45.3%45.328720%21.047098%55.571247%11.025101%66.375818%66.4%2,1301,834-18.272520%-15.825016%-4.164425%-18.272520%18.3%18.272520%4.596678%15.825016%22.869198%22.9%813641-23.302991%-18.226192%-3.481943%-23.302991%23.3%23.302991%0.120674%18.226192%23.423665%23.4%873896
M number of males in category
F number of females in category
1non-Hispanic 2excluding black and Asian Hispanics

Map of Educational Attainment by County Subdivision in Montgomery County

Coarse: Post-Secondary Degree Educational Attainment by County Subdivision#16

Percentage of the population 25 years and older with given highest level of educational attainment
21%33%43%54%65%76%

Coarse: High School Diploma Educational Attainment by County Subdivision#17

Percentage of the population 25 years and older with given highest level of educational attainment
22%31%40%49%58%67%

Coarse: No High School Diploma Educational Attainment by County Subdivision#18

Percentage of the population 25 years and older with given highest level of educational attainment
1.7%4.8%7.8%10.9%13.9%17.0%

Detailed: Doctorate Degree Educational Attainment by County Subdivision#19

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%1.0%2.1%3.1%4.1%5.2%

Detailed: Professional Degree Educational Attainment by County Subdivision#20

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%2.1%4.2%6.4%8.5%10.7%

Detailed: Master's Degree Educational Attainment by County Subdivision#21

Percentage of the population 25 years and older with given highest level of educational attainment
1.3%5.4%9.4%13.4%17.5%21.6%

Detailed: Bachelor's Degree Educational Attainment by County Subdivision#22

Percentage of the population 25 years and older with given highest level of educational attainment
5%11%16%22%28%34%

Detailed: Associate's Degree Educational Attainment by County Subdivision#23

Percentage of the population 25 years and older with given highest level of educational attainment
5.3%7.0%8.7%10.3%12.0%13.7%

Detailed: Some College Educational Attainment by County Subdivision#24

Percentage of the population 25 years and older with given highest level of educational attainment
15.3%18.2%21.1%24.0%26.9%29.8%

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

Percentage of the population 25 years and older with given highest level of educational attainment
7%15%24%32%41%50%

Detailed: Some High School Educational Attainment by County Subdivision#26

Percentage of the population 25 years and older with given highest level of educational attainment
1.0%3.6%6.2%8.7%11.3%13.9%

Detailed: Less than High School Educational Attainment by County Subdivision#27

Percentage of the population 25 years and older with given highest level of educational attainment
0.2%0.9%1.5%2.2%2.8%3.5%

Detailed: None Educational Attainment by County Subdivision#28

Percentage of the population 25 years and older with given highest level of educational attainment
0.00%0.30%0.59%0.89%1.18%1.48%
Road Data ©OpenStreetMap

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Map of Educational Attainment by Tract in Montgomery County

Coarse: Post-Secondary Degree Educational Attainment by Tract#29

Percentage of the population 25 years and older with given highest level of educational attainment
5%21%37%52%68%84%

Coarse: High School Diploma Educational Attainment by Tract#30

Percentage of the population 25 years and older with given highest level of educational attainment
15%27%39%51%63%76%

Coarse: No High School Diploma Educational Attainment by Tract#31

Percentage of the population 25 years and older with given highest level of educational attainment
0%8%16%23%30%38%

Detailed: Doctorate Degree Educational Attainment by Tract#32

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%1.4%2.8%4.2%5.6%7.1%

Detailed: Professional Degree Educational Attainment by Tract#33

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%3.4%6.9%10.3%13.7%17.2%

Detailed: Master's Degree Educational Attainment by Tract#34

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%5.0%9.9%14.9%19.9%24.9%

Detailed: Bachelor's Degree Educational Attainment by Tract#35

Percentage of the population 25 years and older with given highest level of educational attainment
1%8%15%21%28%35%

Detailed: Associate's Degree Educational Attainment by Tract#36

Percentage of the population 25 years and older with given highest level of educational attainment
1.8%5.3%8.7%12.1%15.5%19.0%

Detailed: Some College Educational Attainment by Tract#37

Percentage of the population 25 years and older with given highest level of educational attainment
11%18%26%33%40%48%

Detailed: High School Diploma or Equivalent Educational Attainment by Tract#38

Percentage of the population 25 years and older with given highest level of educational attainment
4%13%23%32%41%50%

Detailed: Some High School Educational Attainment by Tract#39

Percentage of the population 25 years and older with given highest level of educational attainment
0%6%12%18%23%30%

Detailed: Less than High School Educational Attainment by Tract#40

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%2.1%4.2%6.3%8.4%10.6%

Detailed: None Educational Attainment by Tract#41

Percentage of the population 25 years and older with given highest level of educational attainment
0.0%3.0%6.0%9.0%12.0%15.0%
Road Data ©OpenStreetMap

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Educational Attainment by Place in Montgomery County

There are 23 places that are fully or partially contained within Montgomery County (16 fully and 7 partially). This section compares all 23 of those to each other, Montgomery County, and other entities that contain or substantially overlap with Montgomery County.

No H.S. Diploma by Place#42

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Montgomery County, selected places in Montgomery County, and entities that contain Montgomery County
0%5%10%15%20%Count#DrexelDaytonPhillipsburgMoraineTrotwoodRiversideUnited States of AmericaUnited StatesCarlisleNew LebanonEast North CentralOhioWest CarrolltonW CarrolltonMontgomery CountyMontgomeryMidwestDayton Metro AreaDayton AreaBrookvilleVandaliaFarmersvilleMiamisburgVeronaGermantownHuber HeightsEnglewoodKetteringClaytonUnionCentervilleSpringboroOakwood23.841699%23.841699%23.8%247116.928359%16.928359%16.9%14,88914.9k215.436242%15.436242%15.4%46314.342166%14.342166%14.3%617413.774203%13.774203%13.8%2,312513.107180%13.107180%13.1%2,156613.020590%13.020590%13.0%27,818,38027.8M11.533888%11.533888%11.5%388711.491713%11.491713%11.5%312810.719464%10.719464%10.7%3,363,7843.36M10.514146%10.514146%10.5%825,155825k10.412768%10.412768%10.4%946910.369633%10.369633%10.4%37,59537.6k10.244520%10.244520%10.2%4,641,8354.64M9.844056%9.844056%9.8%53,48053.5k9.799555%9.799555%9.8%440109.616815%9.616815%9.6%1,034118.689840%8.689840%8.7%65128.403004%8.403004%8.4%1,186137.971014%7.971014%8.0%22147.089766%7.089766%7.1%248157.076638%7.076638%7.1%1,832166.503982%6.503982%6.5%637175.903575%5.903575%5.9%2,340184.952094%4.952094%5.0%460194.748040%4.748040%4.7%212204.207157%4.207157%4.2%736213.606042%3.606042%3.6%413221.721876%1.721876%1.7%10523
Count number of people in category
# rank of place out of 23 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by Place#43

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Montgomery County, selected places in Montgomery County, and entities that contain Montgomery County
0%20%40%60%Count#OakwoodCentervilleSpringboroClaytonKetteringUnited States of AmericaUnited StatesMidwestEnglewoodEast North CentralDayton Metro AreaDayton AreaOhioMontgomery CountyMontgomeryVandaliaHuber HeightsMiamisburgGermantownPhillipsburgRiversideDaytonBrookvilleUnionTrotwoodFarmersvilleWest CarrolltonW CarrolltonMoraineCarlisleNew LebanonVeronaDrexel70.580518%70.580518%70.6%4,304151.434778%51.434778%51.4%8,998248.651008%48.651008%48.7%5,572334.298633%34.298633%34.3%3,186433.630194%33.630194%33.6%13,33013.3k530.315023%30.315023%30.3%64,767,78764.8M28.952816%28.952816%29.0%13,118,64213.1M28.599142%28.599142%28.6%2,801628.462853%28.462853%28.5%8,931,6868.93M27.783136%27.783136%27.8%150,938151k26.667466%26.667466%26.7%2,092,8752.09M26.143225%26.143225%26.1%94,78294.8k23.818824%23.818824%23.8%2,561723.578492%23.578492%23.6%6,104822.615842%22.615842%22.6%3,192921.069182%21.069182%21.1%7371019.127517%19.127517%19.1%571118.043650%18.043650%18.0%2,9681217.635555%17.635555%17.6%15,51115.5k1317.015590%17.015590%17.0%7641416.707727%16.707727%16.7%7461515.102770%15.102770%15.1%2,5351614.037433%14.037433%14.0%1051713.780958%13.780958%13.8%1,2521813.598326%13.598326%13.6%5851912.990488%12.990488%13.0%437206.593002%6.593002%6.6%179215.797101%5.797101%5.8%16224.922780%4.922780%4.9%5123
Count number of people in category
# rank of place out of 23 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by Place#44

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Montgomery County, selected places in Montgomery County, and entities that contain Montgomery County
0%5%10%15%Count#OakwoodCentervilleSpringboroKetteringUnited States of AmericaUnited StatesMidwestClaytonEast North CentralGermantownOhioDayton Metro AreaDayton AreaMontgomery CountyMontgomeryVandaliaMiamisburgHuber HeightsDaytonEnglewoodRiversideBrookvillePhillipsburgMoraineTrotwoodCarlisleUnionWest CarrolltonW CarrolltonNew LebanonDrexelFarmersvilleVerona15.759265%15.759265%15.8%96115.710529%5.710529%5.7%99925.282459%5.282459%5.3%60533.829755%3.829755%3.8%1,51843.336158%3.336158%3.3%7,127,6737.13M2.893416%2.893416%2.9%1,311,0191.31M2.885133%2.885133%2.9%26852.855432%2.855432%2.9%896,039896k2.715838%2.715838%2.7%9562.683241%2.683241%2.7%210,582211k2.681530%2.681530%2.7%14,56814.6k2.444911%2.444911%2.4%8,8641.432292%1.432292%1.4%15471.374522%1.374522%1.4%19481.290173%1.290173%1.3%33491.257490%1.257490%1.3%1,106101.163978%1.163978%1.2%114111.161165%1.161165%1.2%191121.024499%1.024499%1.0%46131.006711%1.006711%1.0%3140.790330%0.790330%0.8%34150.613643%0.613643%0.6%103160.416171%0.416171%0.4%14170.403135%0.403135%0.4%18180.363236%0.363236%0.4%33190.000000%0.000000%0.0%0200.000000%0.000000%0.0%0210.000000%0.000000%0.0%0220.000000%0.000000%0.0%023
Count number of people in category
# rank of place out of 23 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 Place#45

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 Montgomery County, selected places in Montgomery County, and entities that contain Montgomery County
Female
Male
1x0x1x2xFM#UnionEnglewoodPhillipsburgDrexelNew LebanonTrotwoodFarmersvilleClaytonMidwestEast North CentralDaytonUnited StatesOhioRiversideOakwoodVandaliaMontgomeryHuber HeightsDayton AreaSpringboroBrookvilleCarlisleCentervilleVeronaMiamisburgW CarrolltonMoraineKetteringGermantown-1.000000x-1.000000x2.064321x2.064321x2.06x3.108%3.11%6.417%6.42%1-1.000000x-1.000000x1.662684x1.662684x1.66x4.982%4.98%8.283%8.28%2-1.000000x-1.000000x1.471324x1.471324x1.47x12.319%12.3%18.125%18.1%3-1.000000x-1.000000x1.436361x1.436361x1.44x19.619%19.6%28.180%28.2%4-1.000000x-1.000000x1.426444x1.426444x1.43x9.686%9.69%13.816%13.8%5-1.000000x-1.000000x1.202006x1.202006x1.20x12.700%12.7%15.265%15.3%6-1.000000x-1.000000x1.180260x1.180260x1.18x8.057%8.06%9.509%9.51%7-1.000000x-1.000000x1.168343x1.168343x1.17x4.575%4.58%5.345%5.35%8-1.000000x-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-1.000000x1.114342x1.114342x1.11x16.070%16.1%17.907%17.9%9-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105363x1.105363x1.11x10.008%10.0%11.063%11.1%-1.000000x-1.000000x1.063703x1.063703x1.06x12.715%12.7%13.525%13.5%10-1.000000x-1.000000x1.053352x1.053352x1.05x1.681%1.68%1.771%1.77%11-1.000000x-1.000000x1.041405x1.041405x1.04x9.427%9.43%9.818%9.82%12-1.000000x-1.000000x1.037009x1.037009x1.04x10.193%10.2%10.570%10.6%-1.000000x-1.000000x1.020978x1.020978x1.02x7.008%7.01%7.155%7.16%13-1.000000x-1.000000x1.000795x1.000795x1.00x9.840%9.84%9.848%9.85%-1.014972x-1.014972x1.01x1.000000x1.000000x3.631%3.63%3.577%3.58%14-1.163498x-1.163498x1.16x1.000000x1.000000x10.462%10.5%8.992%8.99%15-1.240992x-1.240992x1.24x1.000000x1.000000x12.673%12.7%10.212%10.2%16-1.287786x-1.287786x1.29x1.000000x1.000000x4.676%4.68%3.631%3.63%17-1.300000x-1.300000x1.30x1.000000x1.000000x9.167%9.17%7.051%7.05%18-1.320263x-1.320263x1.32x1.000000x1.000000x9.483%9.48%7.183%7.18%19-1.326146x-1.326146x1.33x1.000000x1.000000x11.777%11.8%8.881%8.88%20-1.430388x-1.430388x1.43x1.000000x1.000000x16.703%16.7%11.677%11.7%21-1.504519x-1.504519x1.50x1.000000x1.000000x6.979%6.98%4.639%4.64%22-1.988250x-1.988250x1.99x1.000000x1.000000x9.124%9.12%4.589%4.59%23
F percentage of females in category
M percentage of males in category
# rank of place out of 23 by male-to-female sex ratio

Over-Education Sex Ratio by Place#46

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 Montgomery County, selected places in Montgomery County, and entities that contain Montgomery County
Female
Male
10x5x0x5x10xFM#CarlislePhillipsburgCentervilleGermantownClaytonMiamisburgKetteringDayton AreaMontgomeryOakwoodDaytonW CarrolltonEnglewoodOhioHuber HeightsSpringboroUnionEast North CentralMidwestTrotwoodUnited StatesRiversideVandaliaBrookvilleMoraine-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%0.899%0.90%1-1.000000x-1.000000x10.000000x10.000000x> 10.0x0.000%0%1.875%1.88%2-1.000000x-1.000000x3.004250x3.004250x3.00x3.007%3.01%9.033%9.03%3-1.000000x-1.000000x2.420471x2.420471x2.42x1.659%1.66%4.015%4.02%4-1.000000x-1.000000x2.408687x2.408687x2.41x1.708%1.71%4.114%4.11%5-1.000000x-1.000000x2.242163x2.242163x2.24x0.868%0.87%1.947%1.95%6-1.000000x-1.000000x2.230703x2.230703x2.23x2.446%2.45%5.457%5.46%7-1.000000x-1.000000x2.159743x2.159743x2.16x1.731%1.73%3.739%3.74%-1.000000x-1.000000x2.146385x2.146385x2.15x1.591%1.59%3.414%3.41%-1.000000x-1.000000x2.018680x2.018680x2.02x10.778%10.8%21.756%21.8%8-1.000000x-1.000000x1.989047x1.989047x1.99x0.860%0.86%1.711%1.71%9-1.000000x-1.000000x1.965529x1.965529x1.97x0.250%0.25%0.491%0.49%10-1.000000x-1.000000x1.930562x1.930562x1.93x0.815%0.81%1.573%1.57%11-1.000000x-1.000000x1.669500x1.669500x1.67x2.031%2.03%3.391%3.39%-1.000000x-1.000000x1.635346x1.635346x1.64x0.996%1.00%1.629%1.63%12-1.000000x-1.000000x1.615084x1.615084x1.62x4.105%4.11%6.630%6.63%13-1.000000x-1.000000x1.599122x1.599122x1.60x0.311%0.31%0.497%0.50%14-1.000000x-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-1.000000x1.529593x1.529593x1.53x0.502%0.50%0.768%0.77%15-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.510836x1.510836x1.51x0.931%0.93%1.407%1.41%16-1.000000x-1.000000x1.378684x1.378684x1.38x1.210%1.21%1.668%1.67%17-1.696242x-1.696242x1.70x1.000000x1.000000x1.257%1.26%0.741%0.74%18-10.000000x-10.000000x> 10.0x1.000000x1.000000x1.491%1.49%0.000%0%19
F percentage of females in category
M percentage of males in category
# rank of place out of 19 by male-to-female sex ratio

Educational Attainment by County Subdivision in Montgomery County

There are 22 county subdivisions in Montgomery County. This section compares all 22 of those to each other, Montgomery County, and other entities that contain or substantially overlap with Montgomery County.

No H.S. Diploma by County Subdivision#47

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Montgomery County, selected county subdivisions in Montgomery County, and entities that contain Montgomery County
0%5%10%15%Count#Harrison TownshipHarrisonDaytonJefferson TownshipJeffersonMoraineTrotwoodRiversideUnited States of AmericaUnited StatesEast North CentralPerry TownshipPerryOhioJackson TownshipJacksonWest CarrolltonW CarrolltonMontgomery CountyMontgomeryMidwestDayton Metro AreaDayton AreaVandaliaMiami TownshipMiamiGerman TownshipGermanHuber HeightsClay TownshipEnglewoodKetteringButler TownshipButlerClaytonUnion City TownshipUnion CityWashington TownshipWashingtonOakwood16.956522%16.956522%17.0%2,574116.928359%16.928359%16.9%14,88914.9k216.831479%16.831479%16.8%817314.342166%14.342166%14.3%617413.774203%13.774203%13.8%2,312513.107180%13.107180%13.1%2,156613.020590%13.020590%13.0%27,818,38027.8M10.719464%10.719464%10.7%3,363,7843.36M10.566948%10.566948%10.6%438710.514146%10.514146%10.5%825,155825k10.428894%10.428894%10.4%462810.412768%10.412768%10.4%946910.369633%10.369633%10.4%37,59537.6k10.244520%10.244520%10.2%4,641,8354.64M9.844056%9.844056%9.8%53,48053.5k9.616815%9.616815%9.6%1,034108.614662%8.614662%8.6%3,080117.443946%7.443946%7.4%415127.261193%7.261193%7.3%1,797137.101582%7.101582%7.1%467146.503982%6.503982%6.5%637155.953744%5.953744%6.0%2,340165.715722%5.715722%5.7%341174.952094%4.952094%5.0%460184.832460%4.832460%4.8%212193.637690%3.637690%3.6%1,496201.721876%1.721876%1.7%10521
Count number of people in category
# rank of county subdivision out of 21 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County Subdivision#48

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Montgomery County, selected county subdivisions in Montgomery County, and entities that contain Montgomery County
0%20%40%60%Count#OakwoodWashington TownshipWashingtonClaytonButler TownshipButlerKetteringUnited States of AmericaUnited StatesMidwestEnglewoodEast North CentralDayton Metro AreaDayton AreaMiami TownshipMiamiOhioMontgomery CountyMontgomeryVandaliaGerman TownshipGermanHuber HeightsClay TownshipRiversideDaytonUnion City TownshipUnion CityTrotwoodJefferson TownshipJeffersonWest CarrolltonW CarrolltonMoraineHarrison TownshipHarrisonJackson TownshipJacksonPerry TownshipPerry70.580518%70.580518%70.6%4,304152.483891%52.483891%52.5%21,58421.6k234.298633%34.298633%34.3%3,186334.059671%34.059671%34.1%2,032433.391853%33.391853%33.4%13,12413.1k530.315023%30.315023%30.3%64,767,78764.8M28.952816%28.952816%29.0%13,118,64213.1M28.599142%28.599142%28.6%2,801628.462853%28.462853%28.5%8,931,6868.93M27.783136%27.783136%27.8%150,938151k26.713842%26.713842%26.7%9,551726.667466%26.667466%26.7%2,092,8752.09M26.143225%26.143225%26.1%94,78294.8k23.818824%23.818824%23.8%2,561823.426009%23.426009%23.4%1,306922.834168%22.834168%22.8%5,6511019.784063%19.784063%19.8%1,3011118.043650%18.043650%18.0%2,9681217.635555%17.635555%17.6%15,51115.5k1315.933440%15.933440%15.9%6991415.102770%15.102770%15.1%2,5351514.153276%14.153276%14.2%6871613.780958%13.780958%13.8%1,2521713.598326%13.598326%13.6%5851813.570487%13.570487%13.6%2,0601912.731377%12.731377%12.7%5642012.545235%12.545235%12.5%52021
Count number of people in category
# rank of county subdivision out of 21 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County Subdivision#49

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Montgomery County, selected county subdivisions in Montgomery County, and entities that contain Montgomery County
0%5%10%15%Count#OakwoodWashington TownshipWashingtonKetteringUnited States of AmericaUnited StatesButler TownshipButlerMidwestClaytonEast North CentralGerman TownshipGermanOhioDayton Metro AreaDayton AreaMontgomery CountyMontgomeryMiami TownshipMiamiVandaliaJackson TownshipJacksonHuber HeightsDaytonEnglewoodRiversidePerry TownshipPerryClay TownshipMoraineHarrison TownshipHarrisonJefferson TownshipJeffersonTrotwoodUnion City TownshipUnion CityWest CarrolltonW Carrollton15.759265%15.759265%15.8%96116.820669%6.820669%6.8%2,80523.778338%3.778338%3.8%1,48533.336158%3.336158%3.3%7,127,6737.13M3.050620%3.050620%3.1%18242.893416%2.893416%2.9%1,311,0191.31M2.885133%2.885133%2.9%26852.855432%2.855432%2.9%896,039896k2.798206%2.798206%2.8%15662.683241%2.683241%2.7%210,582211k2.681530%2.681530%2.7%14,56814.6k2.444911%2.444911%2.4%8,8641.762090%1.762090%1.8%63071.432292%1.432292%1.4%15481.422122%1.422122%1.4%6391.349604%1.349604%1.3%334101.257490%1.257490%1.3%1,106111.163978%1.163978%1.2%114121.161165%1.161165%1.2%191131.013269%1.013269%1.0%42140.821168%0.821168%0.8%54150.790330%0.790330%0.8%34160.665349%0.665349%0.7%101170.618047%0.618047%0.6%30180.613643%0.613643%0.6%103190.410303%0.410303%0.4%18200.363236%0.363236%0.4%3321
Count number of people in category
# rank of county subdivision out of 21 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#50

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 Montgomery County, selected county subdivisions in Montgomery County, and entities that contain Montgomery County
Female
Male
1x0x1x2xFM#Union CityJeffersonEnglewoodHarrisonTrotwoodClay TownshipClaytonPerryMidwestEast North CentralDaytonUnited StatesOhioRiversideOakwoodVandaliaButlerMontgomeryHuber HeightsDayton AreaGermanMiamiJacksonW CarrolltonMoraineKetteringWashington-1.000000x-1.000000x2.074399x2.074399x2.07x3.156%3.16%6.547%6.55%1-1.000000x-1.000000x1.824560x1.824560x1.82x12.037%12.0%21.962%22.0%2-1.000000x-1.000000x1.662684x1.662684x1.66x4.982%4.98%8.283%8.28%3-1.000000x-1.000000x1.228308x1.228308x1.23x15.280%15.3%18.769%18.8%4-1.000000x-1.000000x1.202006x1.202006x1.20x12.700%12.7%15.265%15.3%5-1.000000x-1.000000x1.169610x1.169610x1.17x6.579%6.58%7.695%7.69%6-1.000000x-1.000000x1.168343x1.168343x1.17x4.575%4.58%5.345%5.35%7-1.000000x-1.000000x1.158773x1.158773x1.16x9.787%9.79%11.341%11.3%8-1.000000x-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-1.000000x1.114342x1.114342x1.11x16.070%16.1%17.907%17.9%9-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105363x1.105363x1.11x10.008%10.0%11.063%11.1%-1.000000x-1.000000x1.063703x1.063703x1.06x12.715%12.7%13.525%13.5%10-1.000000x-1.000000x1.053352x1.053352x1.05x1.681%1.68%1.771%1.77%11-1.000000x-1.000000x1.041405x1.041405x1.04x9.427%9.43%9.818%9.82%12-1.000000x-1.000000x1.038221x1.038221x1.04x5.615%5.62%5.830%5.83%13-1.000000x-1.000000x1.037009x1.037009x1.04x10.193%10.2%10.570%10.6%-1.000000x-1.000000x1.021443x1.021443x1.02x7.189%7.19%7.344%7.34%14-1.000000x-1.000000x1.000795x1.000795x1.00x9.840%9.84%9.848%9.85%-1.036785x-1.036785x1.04x1.000000x1.000000x7.569%7.57%7.300%7.30%15-1.123601x-1.123601x1.12x1.000000x1.000000x9.091%9.09%8.091%8.09%16-1.128565x-1.128565x1.13x1.000000x1.000000x11.021%11.0%9.765%9.77%17-1.326146x-1.326146x1.33x1.000000x1.000000x11.777%11.8%8.881%8.88%18-1.430388x-1.430388x1.43x1.000000x1.000000x16.703%16.7%11.677%11.7%19-1.505212x-1.505212x1.51x1.000000x1.000000x7.040%7.04%4.677%4.68%20-1.641786x-1.641786x1.64x1.000000x1.000000x4.457%4.46%2.715%2.71%21
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 21 by male-to-female sex ratio

Over-Education Sex Ratio by County Subdivision#51

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 Montgomery County, selected county subdivisions in Montgomery County, and entities that contain Montgomery County
Female
Male
10x5x0x5xFM#ButlerHarrisonJacksonWashingtonGermanClaytonDayton AreaKetteringMontgomeryOakwoodDaytonW CarrolltonEnglewoodOhioHuber HeightsUnion CityEast North CentralMidwestTrotwoodUnited StatesRiversideVandaliaMiamiClay TownshipPerryJeffersonMoraine-1.000000x-1.000000x5.744397x5.744397x5.74x0.946%0.95%5.436%5.44%1-1.000000x-1.000000x4.666070x4.666070x4.67x0.241%0.24%1.124%1.12%2-1.000000x-1.000000x3.032298x3.032298x3.03x0.726%0.73%2.202%2.20%3-1.000000x-1.000000x2.977263x2.977263x2.98x3.534%3.53%10.523%10.5%4-1.000000x-1.000000x2.595017x2.595017x2.60x1.608%1.61%4.171%4.17%5-1.000000x-1.000000x2.408687x2.408687x2.41x1.708%1.71%4.114%4.11%6-1.000000x-1.000000x2.159743x2.159743x2.16x1.731%1.73%3.739%3.74%-1.000000x-1.000000x2.155653x2.155653x2.16x2.468%2.47%5.319%5.32%7-1.000000x-1.000000x2.146385x2.146385x2.15x1.591%1.59%3.414%3.41%-1.000000x-1.000000x2.018680x2.018680x2.02x10.778%10.8%21.756%21.8%8-1.000000x-1.000000x1.989047x1.989047x1.99x0.860%0.86%1.711%1.71%9-1.000000x-1.000000x1.965529x1.965529x1.97x0.250%0.25%0.491%0.49%10-1.000000x-1.000000x1.930562x1.930562x1.93x0.815%0.81%1.573%1.57%11-1.000000x-1.000000x1.669500x1.669500x1.67x2.031%2.03%3.391%3.39%-1.000000x-1.000000x1.627164x1.627164x1.63x1.044%1.04%1.699%1.70%12-1.000000x-1.000000x1.606929x1.606929x1.61x0.316%0.32%0.507%0.51%13-1.000000x-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-1.000000x1.529593x1.529593x1.53x0.502%0.50%0.768%0.77%14-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.510836x1.510836x1.51x0.931%0.93%1.407%1.41%15-1.000000x-1.000000x1.378684x1.378684x1.38x1.210%1.21%1.668%1.67%16-1.000000x-1.000000x1.246667x1.246667x1.25x1.577%1.58%1.966%1.97%17-1.762014x-1.762014x1.76x1.000000x1.000000x1.030%1.03%0.584%0.58%18-2.016473x-2.016473x2.02x1.000000x1.000000x1.357%1.36%0.673%0.67%19-6.075130x-6.075130x6.08x1.000000x1.000000x1.036%1.04%0.171%0.17%20-10.000000x-10.000000x> 10.0x1.000000x1.000000x1.491%1.49%0.000%0%21
F percentage of females in category
M percentage of males in category
# rank of county subdivision out of 21 by male-to-female sex ratio

Educational Attainment by County in the Dayton Area

There are 3 counties in the Dayton Area. This section compares Montgomery County to all of the counties in the Dayton Area and to those entities that contain or substantially overlap with Montgomery County.

No H.S. Diploma by County#52

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Montgomery County, selected other counties in the Dayton Area, and entities that contain Montgomery County
0%5%10%Count#United States of AmericaUnited StatesMiami CountyMiamiEast North CentralOhioMontgomery CountyMontgomeryMidwestDayton Metro AreaDayton AreaGreene CountyGreene13.020590%13.020590%13.0%27,818,38027.8M10.912923%10.912923%10.9%7,819110.719464%10.719464%10.7%3,363,7843.36M10.514146%10.514146%10.5%825,155825k10.369633%10.369633%10.4%37,59537.6k210.244520%10.244520%10.2%4,641,8354.64M9.844056%9.844056%9.8%53,48053.5k7.394980%7.394980%7.4%8,0663
Count number of people in category
# rank of county out of 3 by percentage of population 25 years of age and older without a high school diploma (or equivalent).

Bachelor's Degrees by County#53

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Montgomery County, selected other counties in the Dayton Area, and entities that contain Montgomery County
0%10%20%30%Count#Greene CountyGreeneUnited States of AmericaUnited StatesMidwestEast North CentralDayton Metro AreaDayton AreaOhioMontgomery CountyMontgomeryMiami CountyMiami37.647835%37.647835%37.6%41,06441.1k130.315023%30.315023%30.3%64,767,78764.8M28.952816%28.952816%29.0%13,118,64213.1M28.462853%28.462853%28.5%8,931,6868.93M27.783136%27.783136%27.8%150,938151k26.667466%26.667466%26.7%2,092,8752.09M26.143225%26.143225%26.1%94,78294.8k221.063797%21.063797%21.1%15,09215.1k3
Count number of people in category
# rank of county out of 3 by percentage of population 25 years of age and older with a bachelor's degree or higher..

Very Advanced Degrees by County#54

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Montgomery County, selected other counties in the Dayton Area, and entities that contain Montgomery County
0%1%2%3%4%Count#Greene CountyGreeneUnited States of AmericaUnited StatesMidwestEast North CentralOhioDayton Metro AreaDayton AreaMontgomery CountyMontgomeryMiami CountyMiami4.454774%4.454774%4.5%4,85913.336158%3.336158%3.3%7,127,6737.13M2.893416%2.893416%2.9%1,311,0191.31M2.855432%2.855432%2.9%896,039896k2.683241%2.683241%2.7%210,582211k2.681530%2.681530%2.7%14,56814.6k2.444911%2.444911%2.4%8,86421.179360%1.179360%1.2%8453
Count number of people in category
# rank of county out of 3 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#55

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 Montgomery County, selected other counties in the Dayton Area, and entities that contain Montgomery County
Female
Male
1x0x1xFM#MidwestEast North CentralUnited StatesOhioMontgomeryDayton AreaGreeneMiami-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-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105363x1.105363x1.11x10.008%10.0%11.063%11.1%-1.000000x-1.000000x1.037009x1.037009x1.04x10.193%10.2%10.570%10.6%1-1.000000x-1.000000x1.000795x1.000795x1.00x9.840%9.84%9.848%9.85%-1.030592x-1.030592x1.03x1.000000x1.000000x7.503%7.50%7.280%7.28%2-1.131015x-1.131015x1.13x1.000000x1.000000x11.559%11.6%10.220%10.2%3
F percentage of females in category
M percentage of males in category
# rank of county out of 3 by male-to-female sex ratio

Over-Education Sex Ratio by County#56

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 Montgomery County, selected other counties in the Dayton Area, and entities that contain Montgomery County
Female
Male
1x0x1x2x3xFM#MiamiDayton AreaMontgomeryGreeneOhioEast North CentralMidwestUnited States-1.000000x-1.000000x3.501312x3.501312x3.50x0.534%0.53%1.871%1.87%1-1.000000x-1.000000x2.159743x2.159743x2.16x1.731%1.73%3.739%3.74%-1.000000x-1.000000x2.146385x2.146385x2.15x1.591%1.59%3.414%3.41%2-1.000000x-1.000000x2.003204x2.003204x2.00x2.999%3.00%6.008%6.01%3-1.000000x-1.000000x1.669500x1.669500x1.67x2.031%2.03%3.391%3.39%-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-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%
F percentage of females in category
M percentage of males in category
# rank of county out of 3 by male-to-female sex ratio

Educational Attainment by County in Ohio

There are 88 counties in Ohio. This section compares Montgomery County to the 50 most populous counties in Ohio and to those entities that contain or substantially overlap with Montgomery County. The least populous of the compared counties has a population of 48,949.

No H.S. Diploma by County#57

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Montgomery County, selected other counties in Ohio, and entities that contain Montgomery County
0%5%10%15%Count#Scioto CountySciotoWayne CountyWayneAshtabula CountyAshtabulaLawrence CountyLawrenceRoss CountyRossTuscarawas CountyTuscarawasMuskingum CountyMuskingumMarion CountyMarionUnited States of AmericaUnited StatesPickaway CountyPickawayRichland CountyRichlandClark CountyClarkAshland CountyAshlandColumbiana CountyColumbianaHuron CountyHuronCuyahoga CountyCuyahogaLucas CountyLucasTrumbull CountyTrumbullDarke CountyDarkeMiami CountyMiamiLorain CountyLorainEast North CentralAllen CountyAllenSandusky CountySanduskyOhioKnox CountyKnoxClermont CountyClermontMontgomery CountyMontgomeryAthens CountyAthensButler CountyButlerMidwestBelmont CountyBelmontJefferson CountyJeffersonMahoning CountyMahoningHamilton CountyHamiltonDayton Metro AreaDayton AreaWashington CountyWashingtonFranklin CountyFranklinLicking CountyLickingStark CountyStarkErie CountyErieShelby CountyShelbySeneca CountySenecaSummit CountySummitGeauga CountyGeaugaFairfield CountyFairfieldPortage CountyPortageUnion CountyUnionLake CountyLakeHancock CountyHancockGreene CountyGreeneWarren CountyWarrenMedina CountyMedinaWood CountyWoodDelaware CountyDelaware15.880426%15.880426%15.9%8,436114.573422%14.573422%14.6%11,02811.0k214.566860%14.566860%14.6%10,02210.0k314.348435%14.348435%14.3%6,144414.253056%14.253056%14.3%7,731514.068037%14.068037%14.1%9,011613.498115%13.498115%13.5%7,842713.413630%13.413630%13.4%6,192813.020590%13.020590%13.0%27,818,38027.8M12.917789%12.917789%12.9%5,036912.809922%12.809922%12.8%10,88610.9k1012.792826%12.792826%12.8%11,88311.9k1112.567763%12.567763%12.6%4,4281212.374124%12.374124%12.4%9,3021312.286515%12.286515%12.3%4,8201411.473065%11.473065%11.5%100,034100k1511.204220%11.204220%11.2%32,51732.5k1611.138115%11.138115%11.1%16,17816.2k1711.073863%11.073863%11.1%3,9311810.912923%10.912923%10.9%7,8191910.838036%10.838036%10.8%22,53022.5k2010.719464%10.719464%10.7%3,363,7843.36M10.716657%10.716657%10.7%7,4262110.672042%10.672042%10.7%4,3942210.514146%10.514146%10.5%825,155825k10.433639%10.433639%10.4%4,1242310.407526%10.407526%10.4%14,16114.2k2410.369633%10.369633%10.4%37,59537.6k2510.342892%10.342892%10.3%3,6862610.314030%10.314030%10.3%24,52824.5k2710.244520%10.244520%10.2%4,641,8354.64M10.147068%10.147068%10.1%5,1542810.108764%10.108764%10.1%4,833299.896656%9.896656%9.9%16,29916.3k309.873275%9.873275%9.9%53,29153.3k319.844056%9.844056%9.8%53,48053.5k9.795325%9.795325%9.8%4,245329.640656%9.640656%9.6%78,29978.3k339.609394%9.609394%9.6%10,95011.0k349.565083%9.565083%9.6%24,75324.8k359.415905%9.415905%9.4%5,057369.300022%9.300022%9.3%3,004379.244310%9.244310%9.2%3,424389.016560%9.016560%9.0%33,82433.8k398.928599%8.928599%8.9%5,691408.257364%8.257364%8.3%8,306418.081706%8.081706%8.1%8,364428.064021%8.064021%8.1%2,892438.051408%8.051408%8.1%13,15613.2k447.505079%7.505079%7.5%3,842457.394980%7.394980%7.4%8,066467.119778%7.119778%7.1%10,53310.5k476.360906%6.360906%6.4%7,689485.919879%5.919879%5.9%4,714493.378461%3.378461%3.4%4,15050
Count number of people in category
# rank of county 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#58

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Montgomery County, selected other counties in Ohio, and entities that contain Montgomery County
0%10%20%30%40%50%Count#Delaware CountyDelawareWarren CountyWarrenFranklin CountyFranklinGreene CountyGreeneGeauga CountyGeaugaHamilton CountyHamiltonWood CountyWoodMedina CountyMedinaCuyahoga CountyCuyahogaSummit CountySummitUnited States of AmericaUnited StatesButler CountyButlerAthens CountyAthensUnion CountyUnionMidwestEast North CentralDayton Metro AreaDayton AreaClermont CountyClermontPortage CountyPortageLake CountyLakeOhioFairfield CountyFairfieldMontgomery CountyMontgomeryHancock CountyHancockLucas CountyLucasLorain CountyLorainLicking CountyLickingMahoning CountyMahoningStark CountyStarkKnox CountyKnoxErie CountyErieWayne CountyWayneMiami CountyMiamiAshland CountyAshlandWashington CountyWashingtonClark CountyClarkTrumbull CountyTrumbullAllen CountyAllenPickaway CountyPickawayShelby CountyShelbyBelmont CountyBelmontRichland CountyRichlandJefferson CountyJeffersonRoss CountyRossMuskingum CountyMuskingumScioto CountySciotoTuscarawas CountyTuscarawasSandusky CountySanduskySeneca CountySenecaLawrence CountyLawrenceColumbiana CountyColumbianaHuron CountyHuronAshtabula CountyAshtabulaDarke CountyDarkeMarion CountyMarion52.455693%52.455693%52.5%64,43564.4k140.956469%40.956469%41.0%60,59160.6k238.352941%38.352941%38.4%311,493311k337.647835%37.647835%37.6%41,06441.1k437.269176%37.269176%37.3%23,75523.8k535.595553%35.595553%35.6%192,127192k631.682783%31.682783%31.7%25,22925.2k731.455422%31.455422%31.5%38,02338.0k830.891280%30.891280%30.9%269,342269k930.713722%30.713722%30.7%115,217115k1030.315023%30.315023%30.3%64,767,78764.8M29.134358%29.134358%29.1%69,28569.3k1129.126214%29.126214%29.1%10,38010.4k1229.091264%29.091264%29.1%10,43310.4k1328.952816%28.952816%29.0%13,118,64213.1M28.462853%28.462853%28.5%8,931,6868.93M27.783136%27.783136%27.8%150,938151k27.618418%27.618418%27.6%37,57937.6k1427.280106%27.280106%27.3%28,23328.2k1527.039780%27.039780%27.0%44,18344.2k1626.667466%26.667466%26.7%2,092,8752.09M26.410442%26.410442%26.4%26,56626.6k1726.143225%26.143225%26.1%94,78294.8k1825.838022%25.838022%25.8%13,22713.2k1925.090190%25.090190%25.1%72,81772.8k2023.488664%23.488664%23.5%48,82848.8k2123.329326%23.329326%23.3%26,58426.6k2222.789814%22.789814%22.8%37,53337.5k2322.631528%22.631528%22.6%58,56758.6k2421.891919%21.891919%21.9%8,6532521.593088%21.593088%21.6%11,59711.6k2621.467650%21.467650%21.5%16,24516.2k2721.063797%21.063797%21.1%15,09215.1k2819.637840%19.637840%19.6%6,9192918.409212%18.409212%18.4%7,9783017.966799%17.966799%18.0%16,68916.7k3117.959504%17.959504%18.0%26,08626.1k3217.386787%17.386787%17.4%12,04812.0k3317.306656%17.306656%17.3%6,7473417.089254%17.089254%17.1%5,5203516.407773%16.407773%16.4%8,3343616.395430%16.395430%16.4%13,93313.9k3715.429826%15.429826%15.4%7,3773815.303921%15.303921%15.3%8,3013915.066182%15.066182%15.1%8,7534014.993788%14.993788%15.0%7,9654114.979782%14.979782%15.0%9,5954214.815534%14.815534%14.8%6,1004314.711520%14.711520%14.7%5,4494414.142924%14.142924%14.1%6,0564513.935855%13.935855%13.9%10,47610.5k4613.479480%13.479480%13.5%5,2884713.119186%13.119186%13.1%9,0264813.093695%13.093695%13.1%4,6484912.295828%12.295828%12.3%5,67650
Count number of people in category
# rank of county 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#59

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Montgomery County, selected other counties in Ohio, and entities that contain Montgomery County
0%1%2%3%4%5%Count#Athens CountyAthensDelaware CountyDelawareGreene CountyGreeneFranklin CountyFranklinHamilton CountyHamiltonCuyahoga CountyCuyahogaWarren CountyWarrenWood CountyWoodUnited States of AmericaUnited StatesGeauga CountyGeaugaSummit CountySummitLucas CountyLucasMidwestEast North CentralOhioDayton Metro AreaDayton AreaUnion CountyUnionPortage CountyPortageKnox CountyKnoxButler CountyButlerMontgomery CountyMontgomeryLicking CountyLickingErie CountyErieMedina CountyMedinaWayne CountyWayneMahoning CountyMahoningLake CountyLakeAllen CountyAllenLorain CountyLorainStark CountyStarkHancock CountyHancockAshland CountyAshlandClermont CountyClermontFairfield CountyFairfieldJefferson CountyJeffersonWashington CountyWashingtonClark CountyClarkTrumbull CountyTrumbullRoss CountyRossScioto CountySciotoLawrence CountyLawrenceMiami CountyMiamiRichland CountyRichlandShelby CountyShelbyPickaway CountyPickawayBelmont CountyBelmontMuskingum CountyMuskingumAshtabula CountyAshtabulaColumbiana CountyColumbianaSandusky CountySanduskySeneca CountySenecaMarion CountyMarionHuron CountyHuronTuscarawas CountyTuscarawasDarke CountyDarke5.390314%5.390314%5.4%1,92115.031057%5.031057%5.0%6,18024.454774%4.454774%4.5%4,85934.335519%4.335519%4.3%35,21235.2k44.198425%4.198425%4.2%22,66122.7k54.184410%4.184410%4.2%36,48436.5k63.623766%3.623766%3.6%5,36173.600402%3.600402%3.6%2,86783.336158%3.336158%3.3%7,127,6737.13M3.260170%3.260170%3.3%2,07893.147959%3.147959%3.1%11,80911.8k102.925012%2.925012%2.9%8,489112.893416%2.893416%2.9%1,311,0191.31M2.855432%2.855432%2.9%896,039896k2.683241%2.683241%2.7%210,582211k2.681530%2.681530%2.7%14,56814.6k2.506762%2.506762%2.5%899122.505483%2.505483%2.5%2,593132.474321%2.474321%2.5%978142.445209%2.445209%2.4%5,815152.444911%2.444911%2.4%8,864162.283438%2.283438%2.3%2,602172.256689%2.256689%2.3%1,212182.239430%2.239430%2.2%2,707192.226715%2.226715%2.2%1,685202.131251%2.131251%2.1%3,510212.086903%2.086903%2.1%3,410222.046353%2.046353%2.0%1,418232.027141%2.027141%2.0%4,214241.963406%1.963406%2.0%5,081251.906548%1.906548%1.9%976261.867567%1.867567%1.9%658271.832213%1.832213%1.8%2,493281.755659%1.755659%1.8%1,766291.604267%1.604267%1.6%767301.589865%1.589865%1.6%689311.506115%1.506115%1.5%1,399321.396223%1.396223%1.4%2,028331.281319%1.281319%1.3%695341.278190%1.278190%1.3%679351.268099%1.268099%1.3%543361.179360%1.179360%1.2%845371.166143%1.166143%1.2%991381.111421%1.111421%1.1%359391.095293%1.095293%1.1%427401.025732%1.025732%1.0%521410.984560%0.984560%1.0%572420.899709%0.899709%0.9%619430.895268%0.895268%0.9%673440.871931%0.871931%0.9%359450.861254%0.861254%0.9%319460.847017%0.847017%0.8%391470.830997%0.830997%0.8%326480.771236%0.771236%0.8%494490.546510%0.546510%0.5%19450
Count number of people in category
# rank of county 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#60

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 Montgomery County, selected other counties in Ohio, and entities that contain Montgomery County
Female
Male
1x0x1xFM#PickawayWarrenRichlandShelbyAthensHancockMarionClermontLorainTuscarawasDelawareHuronKnoxAshlandMuskingumLucasMahoningTrumbullWayneGeaugaClarkLawrenceDarkeLakeMidwestEast North CentralBelmontAshtabulaWashingtonUnited StatesOhioMedinaRossSanduskyErieCuyahogaAllenFranklinFairfieldButlerLickingMontgomeryPortageSciotoStarkSenecaHamiltonJeffersonDayton AreaColumbianaGreeneSummitWoodMiamiUnion-1.000000x-1.000000x1.597241x1.597241x1.60x9.831%9.83%15.703%15.7%1-1.000000x-1.000000x1.457031x1.457031x1.46x5.811%5.81%8.466%8.47%2-1.000000x-1.000000x1.428027x1.428027x1.43x10.554%10.6%15.071%15.1%3-1.000000x-1.000000x1.355020x1.355020x1.36x7.910%7.91%10.718%10.7%4-1.000000x-1.000000x1.239662x1.239662x1.24x9.239%9.24%11.453%11.5%5-1.000000x-1.000000x1.228711x1.228711x1.23x6.756%6.76%8.301%8.30%6-1.000000x-1.000000x1.222802x1.222802x1.22x11.988%12.0%14.658%14.7%7-1.000000x-1.000000x1.219059x1.219059x1.22x9.410%9.41%11.471%11.5%8-1.000000x-1.000000x1.216863x1.216863x1.22x9.813%9.81%11.941%11.9%9-1.000000x-1.000000x1.212065x1.212065x1.21x12.766%12.8%15.474%15.5%10-1.000000x-1.000000x1.204437x1.204437x1.20x3.074%3.07%3.703%3.70%11-1.000000x-1.000000x1.201209x1.201209x1.20x11.198%11.2%13.451%13.5%12-1.000000x-1.000000x1.196265x1.196265x1.20x9.533%9.53%11.404%11.4%13-1.000000x-1.000000x1.194357x1.194357x1.19x11.488%11.5%13.721%13.7%14-1.000000x-1.000000x1.190045x1.190045x1.19x12.386%12.4%14.740%14.7%15-1.000000x-1.000000x1.169306x1.169306x1.17x10.373%10.4%12.129%12.1%16-1.000000x-1.000000x1.158437x1.158437x1.16x9.203%9.20%10.662%10.7%17-1.000000x-1.000000x1.157733x1.157733x1.16x10.358%10.4%11.992%12.0%18-1.000000x-1.000000x1.151379x1.151379x1.15x13.576%13.6%15.631%15.6%19-1.000000x-1.000000x1.151241x1.151241x1.15x8.319%8.32%9.577%9.58%20-1.000000x-1.000000x1.150715x1.150715x1.15x11.939%11.9%13.739%13.7%21-1.000000x-1.000000x1.140157x1.140157x1.14x13.451%13.5%15.336%15.3%22-1.000000x-1.000000x1.139879x1.139879x1.14x10.372%10.4%11.823%11.8%23-1.000000x-1.000000x1.134887x1.134887x1.13x7.564%7.56%8.584%8.58%24-1.000000x-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-1.000000x1.122752x1.122752x1.12x9.558%9.56%10.731%10.7%25-1.000000x-1.000000x1.121822x1.121822x1.12x13.739%13.7%15.412%15.4%26-1.000000x-1.000000x1.116980x1.116980x1.12x9.270%9.27%10.354%10.4%27-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105363x1.105363x1.11x10.008%10.0%11.063%11.1%-1.000000x-1.000000x1.104033x1.104033x1.10x6.055%6.06%6.685%6.69%28-1.000000x-1.000000x1.090568x1.090568x1.09x13.607%13.6%14.839%14.8%29-1.000000x-1.000000x1.079648x1.079648x1.08x10.276%10.3%11.095%11.1%30-1.000000x-1.000000x1.070676x1.070676x1.07x9.106%9.11%9.749%9.75%31-1.000000x-1.000000x1.068330x1.068330x1.07x11.121%11.1%11.881%11.9%32-1.000000x-1.000000x1.067772x1.067772x1.07x10.371%10.4%11.074%11.1%33-1.000000x-1.000000x1.059046x1.059046x1.06x9.376%9.38%9.930%9.93%34-1.000000x-1.000000x1.047665x1.047665x1.05x8.071%8.07%8.455%8.46%35-1.000000x-1.000000x1.042157x1.042157x1.04x10.109%10.1%10.535%10.5%36-1.000000x-1.000000x1.041562x1.041562x1.04x9.421%9.42%9.813%9.81%37-1.000000x-1.000000x1.037009x1.037009x1.04x10.193%10.2%10.570%10.6%38-1.000000x-1.000000x1.036905x1.036905x1.04x7.939%7.94%8.232%8.23%39-1.000000x-1.000000x1.033630x1.033630x1.03x15.627%15.6%16.153%16.2%40-1.000000x-1.000000x1.031161x1.031161x1.03x9.426%9.43%9.720%9.72%41-1.000000x-1.000000x1.020140x1.020140x1.02x9.155%9.15%9.339%9.34%42-1.000000x-1.000000x1.012905x1.012905x1.01x9.814%9.81%9.941%9.94%43-1.000000x-1.000000x1.004137x1.004137x1.00x10.089%10.1%10.131%10.1%44-1.000000x-1.000000x1.000795x1.000795x1.00x9.840%9.84%9.848%9.85%-1.022047x-1.022047x1.02x1.000000x1.000000x12.509%12.5%12.239%12.2%45-1.030592x-1.030592x1.03x1.000000x1.000000x7.503%7.50%7.280%7.28%46-1.057764x-1.057764x1.06x1.000000x1.000000x9.256%9.26%8.751%8.75%47-1.088790x-1.088790x1.09x1.000000x1.000000x6.164%6.16%5.661%5.66%48-1.131015x-1.131015x1.13x1.000000x1.000000x11.559%11.6%10.220%10.2%49-1.420116x-1.420116x1.42x1.000000x1.000000x9.358%9.36%6.590%6.59%50
F percentage of females in category
M percentage of males in category
# rank of county out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County#61

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 Montgomery County, selected other counties in Ohio, and entities that contain Montgomery County
Female
Male
1x0x1x2x3xFM#MiamiColumbianaClarkJeffersonLawrenceAshtabulaMahoningWashingtonDayton AreaMontgomeryErieLorainBelmontAthensGreeneHancockGeaugaSciotoRichlandPortageDarkeButlerClermontTrumbullAllenCuyahogaOhioStarkWayneWoodAshlandSenecaTuscarawasDelawareFairfieldSummitHamiltonEast North CentralMuskingumMidwestFranklinHuronUnited StatesShelbyUnionRossKnoxWarrenLickingLakeLucasMedinaPickawaySanduskyMarion-1.000000x-1.000000x3.501312x3.501312x3.50x0.534%0.53%1.871%1.87%1-1.000000x-1.000000x3.345227x3.345227x3.35x0.412%0.41%1.379%1.38%2-1.000000x-1.000000x2.838033x2.838033x2.84x0.805%0.80%2.284%2.28%3-1.000000x-1.000000x2.291361x2.291361x2.29x0.992%0.99%2.274%2.27%4-1.000000x-1.000000x2.274678x2.274678x2.27x0.789%0.79%1.795%1.79%5-1.000000x-1.000000x2.235629x2.235629x2.24x0.558%0.56%1.248%1.25%6-1.000000x-1.000000x2.218047x2.218047x2.22x1.350%1.35%2.994%2.99%7-1.000000x-1.000000x2.165620x2.165620x2.17x1.016%1.02%2.200%2.20%8-1.000000x-1.000000x2.159743x2.159743x2.16x1.731%1.73%3.739%3.74%-1.000000x-1.000000x2.146385x2.146385x2.15x1.591%1.59%3.414%3.41%9-1.000000x-1.000000x2.105141x2.105141x2.11x1.473%1.47%3.100%3.10%10-1.000000x-1.000000x2.086342x2.086342x2.09x1.331%1.33%2.776%2.78%11-1.000000x-1.000000x2.063493x2.063493x2.06x0.669%0.67%1.380%1.38%12-1.000000x-1.000000x2.046436x2.046436x2.05x3.542%3.54%7.249%7.25%13-1.000000x-1.000000x2.003204x2.003204x2.00x2.999%3.00%6.008%6.01%14-1.000000x-1.000000x1.977220x1.977220x1.98x1.293%1.29%2.557%2.56%15-1.000000x-1.000000x1.957255x1.957255x1.96x2.227%2.23%4.360%4.36%16-1.000000x-1.000000x1.939604x1.939604x1.94x0.880%0.88%1.706%1.71%17-1.000000x-1.000000x1.868071x1.868071x1.87x0.813%0.81%1.520%1.52%18-1.000000x-1.000000x1.859489x1.859489x1.86x1.765%1.77%3.283%3.28%19-1.000000x-1.000000x1.849233x1.849233x1.85x0.387%0.39%0.716%0.72%20-1.000000x-1.000000x1.799448x1.799448x1.80x1.767%1.77%3.179%3.18%21-1.000000x-1.000000x1.797099x1.797099x1.80x1.322%1.32%2.376%2.38%22-1.000000x-1.000000x1.771358x1.771358x1.77x1.021%1.02%1.808%1.81%23-1.000000x-1.000000x1.732122x1.732122x1.73x1.505%1.50%2.606%2.61%24-1.000000x-1.000000x1.683297x1.683297x1.68x3.179%3.18%5.351%5.35%25-1.000000x-1.000000x1.669500x1.669500x1.67x2.031%2.03%3.391%3.39%-1.000000x-1.000000x1.666317x1.666317x1.67x1.492%1.49%2.486%2.49%26-1.000000x-1.000000x1.658480x1.658480x1.66x1.687%1.69%2.798%2.80%27-1.000000x-1.000000x1.654546x1.654546x1.65x2.731%2.73%4.519%4.52%28-1.000000x-1.000000x1.646276x1.646276x1.65x1.423%1.42%2.343%2.34%29-1.000000x-1.000000x1.645493x1.645493x1.65x0.656%0.66%1.079%1.08%30-1.000000x-1.000000x1.642114x1.642114x1.64x0.589%0.59%0.968%0.97%31-1.000000x-1.000000x1.634773x1.634773x1.63x3.849%3.85%6.292%6.29%32-1.000000x-1.000000x1.623998x1.623998x1.62x1.348%1.35%2.189%2.19%33-1.000000x-1.000000x1.609994x1.609994x1.61x2.442%2.44%3.932%3.93%34-1.000000x-1.000000x1.601589x1.601589x1.60x3.275%3.27%5.245%5.24%35-1.000000x-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-1.000000x-1.000000x1.590424x1.590424x1.59x0.770%0.77%1.224%1.22%36-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.567965x1.567965x1.57x3.409%3.41%5.346%5.35%37-1.000000x-1.000000x1.532147x1.532147x1.53x0.661%0.66%1.013%1.01%38-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.522652x1.522652x1.52x0.883%0.88%1.344%1.34%39-1.000000x-1.000000x1.506598x1.506598x1.51x2.027%2.03%3.053%3.05%40-1.000000x-1.000000x1.490387x1.490387x1.49x1.019%1.02%1.519%1.52%41-1.000000x-1.000000x1.481268x1.481268x1.48x2.009%2.01%2.976%2.98%42-1.000000x-1.000000x1.471795x1.471795x1.47x2.940%2.94%4.327%4.33%43-1.000000x-1.000000x1.469621x1.469621x1.47x1.863%1.86%2.738%2.74%44-1.000000x-1.000000x1.461851x1.461851x1.46x1.709%1.71%2.499%2.50%45-1.000000x-1.000000x1.435534x1.435534x1.44x2.425%2.43%3.481%3.48%46-1.000000x-1.000000x1.406400x1.406400x1.41x1.871%1.87%2.631%2.63%47-1.000000x-1.000000x1.250193x1.250193x1.25x0.968%0.97%1.210%1.21%48-1.000000x-1.000000x1.242747x1.242747x1.24x0.780%0.78%0.970%0.97%49-1.000000x-1.000000x1.220919x1.220919x1.22x0.758%0.76%0.925%0.93%50
F percentage of females in category
M percentage of males in category
# rank of county out of 50 by male-to-female sex ratio

Educational Attainment by County in the United States

There are 3,142 counties in the United States. This section compares Montgomery County to the 50 most populous counties in the United States and to those entities that contain or substantially overlap with Montgomery County. The least populous of the compared counties has a population of 941,618.

No H.S. Diploma by County#62

Percent of population 25 years of age and older without a high school diploma (or equivalent).
Scope: population of Montgomery County, selected other counties in the United States, and entities that contain Montgomery County
0%10%20%Count#Bronx CountyBronxFresno CountyFresnoLos Angeles CountyLos AngelesDallas CountyDallasSan Bernardino CountySan BernardinoKings CountyKingsHarris CountyHarrisRiverside CountyRiversideMiami-Dade CountyMiami-DadeQueens CountyQueensPhiladelphia CountyPhiladelphiaBexar CountyBexarOrange CountyOrangeClark CountyClarkWayne CountyWayneTarrant CountyTarrantCook CountyCookSan Diego CountySan DiegoSacramento CountySacramentoMilwaukee CountyMilwaukeeMaricopa CountyMaricopaUnited States of AmericaUnited StatesNew York CountyNew YorkSanta Clara CountySanta ClaraAlameda CountyAlamedaWestchester CountyWestchesterOrange CountyOrangePima CountyPimaHillsborough CountyHillsboroughPalm Beach CountyPalm BeachTravis CountyTravisBroward CountyBrowardCuyahoga CountyCuyahogaContra Costa CountyContra CostaEast North CentralOhioMecklenburg CountyMecklenburgMontgomery CountyMontgomeryMidwestSalt Lake CountySalt LakeFairfield CountyFairfieldSuffolk CountySuffolkDayton Metro AreaDayton AreaFranklin CountyFranklinNassau CountyNassauHonolulu CountyHonoluluMontgomery CountyMontgomeryFulton CountyFultonFairfax CountyFairfaxWake CountyWakeKing CountyKingMiddlesex CountyMiddlesexHennepin CountyHennepinSt. Louis CountySt. LouisOakland CountyOaklandAllegheny CountyAllegheny28.837398%28.837398%28.8%261,022261k126.224410%26.224410%26.2%151,875152k222.282470%22.282470%22.3%1,495,6171.50M321.975765%21.975765%22.0%349,434349k421.207718%21.207718%21.2%274,381274k520.017506%20.017506%20.0%351,046351k619.839074%19.839074%19.8%554,024554k719.521191%19.521191%19.5%286,746287k819.375775%19.375775%19.4%362,252362k919.344706%19.344706%19.3%315,505316k1017.390838%17.390838%17.4%180,039180k1116.576437%16.576437%16.6%194,124194k1215.490423%15.490423%15.5%325,372325k1315.108430%15.108430%15.1%210,261210k1414.881687%14.881687%14.9%173,869174k1514.774230%14.774230%14.8%182,543183k1614.189351%14.189351%14.2%503,252503k1713.598133%13.598133%13.6%293,959294k1813.220947%13.220947%13.2%129,386129k1913.092854%13.092854%13.1%81,52281.5k2013.051927%13.051927%13.1%348,801349k2113.020590%13.020590%13.0%27,818,38027.8M12.996284%12.996284%13.0%160,845161k2212.928824%12.928824%12.9%166,105166k2312.700590%12.700590%12.7%141,393141k2412.397264%12.397264%12.4%81,98182.0k2512.319191%12.319191%12.3%101,664102k2612.252238%12.252238%12.3%81,21881.2k2712.196236%12.196236%12.2%108,416108k2812.054893%12.054893%12.1%122,162122k2911.841956%11.841956%11.8%90,92190.9k3011.688725%11.688725%11.7%152,462152k3111.473065%11.473065%11.5%100,034100k3210.940787%10.940787%10.9%82,37182.4k3310.719464%10.719464%10.7%3,363,7843.36M10.514146%10.514146%10.5%825,155825k10.377895%10.377895%10.4%69,51069.5k3410.369633%10.369633%10.4%37,59537.6k10.244520%10.244520%10.2%4,641,8354.64M10.238253%10.238253%10.2%69,21169.2k3510.103521%10.103521%10.1%64,29864.3k369.992975%9.992975%10.0%102,414102k379.844056%9.844056%9.8%53,48053.5k9.640656%9.640656%9.6%78,29978.3k389.121758%9.121758%9.1%85,26785.3k398.945780%8.945780%8.9%60,28460.3k408.836226%8.836226%8.8%62,20562.2k418.677323%8.677323%8.7%57,57157.6k428.282040%8.282040%8.3%63,67163.7k437.696387%7.696387%7.7%50,50850.5k447.543133%7.543133%7.5%110,490110k457.360151%7.360151%7.4%80,37880.4k467.173605%7.173605%7.2%59,66059.7k477.049887%7.049887%7.0%48,46648.5k486.540987%6.540987%6.5%56,30256.3k496.132273%6.132273%6.1%53,97454.0k50
Count number of people in category
# rank of county 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#63

Percent of population 25 years of age and older with a bachelor's degree or higher..
Scope: population of Montgomery County, selected other counties in the United States, and entities that contain Montgomery County
0%20%40%60%Count#New York CountyNew YorkFairfax CountyFairfaxMontgomery CountyMontgomeryMiddlesex CountyMiddlesexWake CountyWakeFulton CountyFultonSanta Clara CountySanta ClaraKing CountyKingHennepin CountyHennepinWestchester CountyWestchesterTravis CountyTravisFairfield CountyFairfieldOakland CountyOaklandAlameda CountyAlamedaNassau CountyNassauMecklenburg CountyMecklenburgSt. Louis CountySt. LouisContra Costa CountyContra CostaAllegheny CountyAlleghenyOrange CountyOrangeFranklin CountyFranklinSan Diego CountySan DiegoCook CountyCookSuffolk CountySuffolkPalm Beach CountyPalm BeachKings CountyKingsHonolulu CountyHonoluluSalt Lake CountySalt LakeOrange CountyOrangeHillsborough CountyHillsboroughBroward CountyBrowardMaricopa CountyMaricopaCuyahoga CountyCuyahogaPima CountyPimaLos Angeles CountyLos AngelesTarrant CountyTarrantQueens CountyQueensUnited States of AmericaUnited StatesHarris CountyHarrisMilwaukee CountyMilwaukeeDallas CountyDallasSacramento CountySacramentoMidwestEast North CentralDayton Metro AreaDayton AreaMiami-Dade CountyMiami-DadeBexar CountyBexarOhioPhiladelphia CountyPhiladelphiaMontgomery CountyMontgomeryClark CountyClarkWayne CountyWayneRiverside CountyRiversideFresno CountyFresnoSan Bernardino CountySan BernardinoBronx CountyBronx60.434640%60.434640%60.4%747,953748k160.260099%60.260099%60.3%463,270463k258.105449%58.105449%58.1%409,049409k353.004569%53.004569%53.0%578,847579k450.070856%50.070856%50.1%328,593329k549.806094%49.806094%49.8%330,446330k649.103299%49.103299%49.1%630,862631k749.055555%49.055555%49.1%718,554719k847.581343%47.581343%47.6%395,715396k947.303651%47.303651%47.3%312,811313k1046.446606%46.446606%46.4%356,611357k1146.362462%46.362462%46.4%295,047295k1245.020952%45.020952%45.0%387,521388k1343.887830%43.887830%43.9%488,594489k1443.543885%43.543885%43.5%407,033407k1543.097901%43.097901%43.1%288,665289k1642.382090%42.382090%42.4%291,365291k1740.269631%40.269631%40.3%303,182303k1839.069581%39.069581%39.1%343,876344k1938.447168%38.447168%38.4%807,572808k2038.352941%38.352941%38.4%311,493311k2136.494430%36.494430%36.5%788,922789k2236.471153%36.471153%36.5%1,293,5181.29M2334.384794%34.384794%34.4%352,396352k2434.188819%34.188819%34.2%346,463346k2534.081468%34.081468%34.1%597,685598k2633.350646%33.350646%33.4%224,744225k2732.800693%32.800693%32.8%221,734222k2831.922850%31.922850%31.9%263,443263k2931.433296%31.433296%31.4%279,420279k3031.015731%31.015731%31.0%404,554405k3130.968863%30.968863%31.0%827,615828k3230.891280%30.891280%30.9%269,342269k3330.800609%30.800609%30.8%204,172204k3430.764105%30.764105%30.8%2,064,9112.06M3530.734653%30.734653%30.7%379,742380k3630.595789%30.595789%30.6%499,006499k3730.315023%30.315023%30.3%64,767,78764.8M30.076273%30.076273%30.1%839,907840k3829.748251%29.748251%29.7%185,226185k3929.683200%29.683200%29.7%471,989472k4029.283682%29.283682%29.3%286,583287k4128.952816%28.952816%29.0%13,118,64213.1M28.462853%28.462853%28.5%8,931,6868.93M27.783136%27.783136%27.8%150,938151k27.281635%27.281635%27.3%510,061510k4226.926164%26.926164%26.9%315,328315k4326.667466%26.667466%26.7%2,092,8752.09M26.349720%26.349720%26.3%272,786273k4426.143225%26.143225%26.1%94,78294.8k22.823853%22.823853%22.8%317,635318k4522.338065%22.338065%22.3%260,985261k4621.189043%21.189043%21.2%311,245311k4719.731980%19.731980%19.7%114,275114k4819.317210%19.317210%19.3%249,922250k4919.131504%19.131504%19.1%173,169173k50
Count number of people in category
# rank of county 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#64

Percent of population 25 years of age and older with a professional or doctorate degree (e.g., MBA, PhD, or MD).
Scope: population of Montgomery County, selected other counties in the United States, and entities that contain Montgomery County
0%5%10%Count#Montgomery CountyMontgomeryNew York CountyNew YorkMiddlesex CountyMiddlesexFairfax CountyFairfaxWestchester CountyWestchesterSanta Clara CountySanta ClaraNassau CountyNassauAlameda CountyAlamedaFulton CountyFultonKing CountyKingFairfield CountyFairfieldSt. Louis CountySt. LouisOakland CountyOaklandHennepin CountyHennepinTravis CountyTravisAllegheny CountyAlleghenyContra Costa CountyContra CostaWake CountyWakeSan Diego CountySan DiegoPalm Beach CountyPalm BeachOrange CountyOrangeFranklin CountyFranklinCook CountyCookCuyahoga CountyCuyahogaPima CountyPimaHonolulu CountyHonoluluSuffolk CountySuffolkMiami-Dade CountyMiami-DadeSalt Lake CountySalt LakePhiladelphia CountyPhiladelphiaLos Angeles CountyLos AngelesMecklenburg CountyMecklenburgBroward CountyBrowardHillsborough CountyHillsboroughKings CountyKingsHarris CountyHarrisUnited States of AmericaUnited StatesSacramento CountySacramentoMaricopa CountyMaricopaDallas CountyDallasOrange CountyOrangeMilwaukee CountyMilwaukeeMidwestEast North CentralBexar CountyBexarQueens CountyQueensOhioDayton Metro AreaDayton AreaMontgomery CountyMontgomeryFresno CountyFresnoTarrant CountyTarrantClark CountyClarkRiverside CountyRiversideWayne CountyWayneSan Bernardino CountySan BernardinoBronx CountyBronx12.399695%12.399695%12.4%87,29187.3k110.788423%10.788423%10.8%133,520134k28.541485%8.541485%8.5%93,27993.3k37.784891%7.784891%7.8%59,84959.8k47.604460%7.604460%7.6%50,28750.3k56.177550%6.177550%6.2%79,36779.4k65.833659%5.833659%5.8%54,53154.5k75.762347%5.762347%5.8%64,15164.2k85.719518%5.719518%5.7%37,94737.9k95.673632%5.673632%5.7%83,10683.1k105.463614%5.463614%5.5%34,77034.8k115.244723%5.244723%5.2%36,05636.1k125.214131%5.214131%5.2%44,88144.9k135.193709%5.193709%5.2%43,19443.2k145.061951%5.061951%5.1%38,86538.9k155.039180%5.039180%5.0%44,35344.4k164.956168%4.956168%5.0%37,31437.3k174.892146%4.892146%4.9%32,10532.1k184.749001%4.749001%4.7%102,662103k194.475020%4.475020%4.5%45,34945.3k204.348784%4.348784%4.3%91,34591.3k214.335519%4.335519%4.3%35,21235.2k224.239617%4.239617%4.2%150,366150k234.184410%4.184410%4.2%36,48436.5k244.139192%4.139192%4.1%27,43827.4k253.970725%3.970725%4.0%26,75826.8k263.962200%3.962200%4.0%40,60740.6k273.784527%3.784527%3.8%70,75670.8k283.768025%3.768025%3.8%25,47225.5k293.720350%3.720350%3.7%38,51538.5k303.604427%3.604427%3.6%241,932242k313.598297%3.598297%3.6%24,10124.1k323.517075%3.517075%3.5%45,87545.9k333.474402%3.474402%3.5%30,88530.9k343.384967%3.384967%3.4%59,36259.4k353.344637%3.344637%3.3%93,40293.4k363.336158%3.336158%3.3%7,127,6737.13M3.266254%3.266254%3.3%31,96532.0k373.113482%3.113482%3.1%83,20583.2k383.037379%3.037379%3.0%48,29748.3k392.990491%2.990491%3.0%24,67924.7k402.985811%2.985811%3.0%18,59118.6k412.893416%2.893416%2.9%1,311,0191.31M2.855432%2.855432%2.9%896,039896k2.818073%2.818073%2.8%33,00233.0k422.756960%2.756960%2.8%44,96545.0k432.683241%2.683241%2.7%210,582211k2.681530%2.681530%2.7%14,56814.6k2.444911%2.444911%2.4%8,8642.392530%2.392530%2.4%13,85613.9k442.368743%2.368743%2.4%29,26729.3k452.315475%2.315475%2.3%32,22432.2k462.260473%2.260473%2.3%33,20433.2k472.178386%2.178386%2.2%25,45125.5k481.852789%1.852789%1.9%23,97124.0k491.664253%1.664253%1.7%15,06415.1k50
Count number of people in category
# rank of county 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#65

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 Montgomery County, selected other counties in the United States, and entities that contain Montgomery County
Female
Male
1x0x1xFM#MecklenburgPalm BeachWakeWestchesterFairfieldContra CostaHillsboroughFresnoMidwestEast North CentralDallasSuffolkMilwaukeeWayneUnited StatesOhioOrangeHarrisFultonPhiladelphiaSan BernardinoTarrantCookMaricopaCuyahogaTravisBrowardRiversideOaklandNassauSalt LakeFranklinSacramentoMontgomeryMontgomeryBronxMiddlesexKingsFairfaxBexarPimaDayton AreaKingLos AngelesClarkOrangeSt. LouisHennepinMiami-DadeQueensAlamedaAlleghenyNew YorkSan DiegoSanta ClaraHonolulu-1.000000x-1.000000x1.282326x1.282326x1.28x9.163%9.16%11.750%11.7%1-1.000000x-1.000000x1.244689x1.244689x1.24x10.804%10.8%13.447%13.4%2-1.000000x-1.000000x1.180227x1.180227x1.18x7.089%7.09%8.367%8.37%3-1.000000x-1.000000x1.161379x1.161379x1.16x11.523%11.5%13.383%13.4%4-1.000000x-1.000000x1.158198x1.158198x1.16x9.397%9.40%10.884%10.9%5-1.000000x-1.000000x1.145263x1.145263x1.15x10.231%10.2%11.718%11.7%6-1.000000x-1.000000x1.132218x1.132218x1.13x11.472%11.5%12.989%13.0%7-1.000000x-1.000000x1.130389x1.130389x1.13x24.647%24.6%27.860%27.9%8-1.000000x-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-1.000000x1.120951x1.120951x1.12x20.762%20.8%23.273%23.3%9-1.000000x-1.000000x1.117209x1.117209x1.12x9.458%9.46%10.567%10.6%10-1.000000x-1.000000x1.111013x1.111013x1.11x12.441%12.4%13.822%13.8%11-1.000000x-1.000000x1.109962x1.109962x1.11x14.154%14.2%15.710%15.7%12-1.000000x-1.000000x1.109070x1.109070x1.11x12.369%12.4%13.719%13.7%-1.000000x-1.000000x1.105363x1.105363x1.11x10.008%10.0%11.063%11.1%-1.000000x-1.000000x1.101852x1.101852x1.10x11.740%11.7%12.936%12.9%13-1.000000x-1.000000x1.098530x1.098530x1.10x18.925%18.9%20.790%20.8%14-1.000000x-1.000000x1.090368x1.090368x1.09x8.321%8.32%9.073%9.07%15-1.000000x-1.000000x1.090163x1.090163x1.09x16.700%16.7%18.206%18.2%16-1.000000x-1.000000x1.084622x1.084622x1.08x20.369%20.4%22.092%22.1%17-1.000000x-1.000000x1.079174x1.079174x1.08x14.234%14.2%15.361%15.4%18-1.000000x-1.000000x1.079067x1.079067x1.08x13.676%13.7%14.757%14.8%19-1.000000x-1.000000x1.072566x1.072566x1.07x12.608%12.6%13.523%13.5%20-1.000000x-1.000000x1.068330x1.068330x1.07x11.121%11.1%11.881%11.9%21-1.000000x-1.000000x1.066137x1.066137x1.07x11.462%11.5%12.220%12.2%22-1.000000x-1.000000x1.065180x1.065180x1.07x11.337%11.3%12.076%12.1%23-1.000000x-1.000000x1.064463x1.064463x1.06x18.923%18.9%20.143%20.1%24-1.000000x-1.000000x1.062700x1.062700x1.06x6.352%6.35%6.750%6.75%25-1.000000x-1.000000x1.061865x1.061865x1.06x8.863%8.86%9.411%9.41%26-1.000000x-1.000000x1.061606x1.061606x1.06x9.934%9.93%10.546%10.5%27-1.000000x-1.000000x1.059046x1.059046x1.06x9.376%9.38%9.930%9.93%28-1.000000x-1.000000x1.056584x1.056584x1.06x12.873%12.9%13.601%13.6%29-1.000000x-1.000000x1.048432x1.048432x1.05x8.640%8.64%9.059%9.06%30-1.000000x-1.000000x1.037009x1.037009x1.04x10.193%10.2%10.570%10.6%-1.000000x-1.000000x1.030539x1.030539x1.03x28.448%28.4%29.316%29.3%31-1.000000x-1.000000x1.025105x1.025105x1.03x7.273%7.27%7.455%7.46%32-1.000000x-1.000000x1.018960x1.018960x1.02x19.845%19.8%20.221%20.2%33-1.000000x-1.000000x1.016052x1.016052x1.02x8.218%8.22%8.350%8.35%34-1.000000x-1.000000x1.009187x1.009187x1.01x16.504%16.5%16.655%16.7%35-1.000000x-1.000000x1.001235x1.001235x1.00x12.245%12.2%12.260%12.3%36-1.000000x-1.000000x1.000795x1.000795x1.00x9.840%9.84%9.848%9.85%-1.004399x-1.004399x1.00x1.000000x1.000000x7.560%7.56%7.526%7.53%37-1.004399x-1.004399x1.00x1.000000x1.000000x22.330%22.3%22.232%22.2%38-1.008231x-1.008231x1.01x1.000000x1.000000x15.170%15.2%15.046%15.0%39-1.015422x-1.015422x1.02x1.000000x1.000000x15.605%15.6%15.368%15.4%40-1.015859x-1.015859x1.02x1.000000x1.000000x7.101%7.10%6.990%6.99%41-1.016401x-1.016401x1.02x1.000000x1.000000x7.230%7.23%7.114%7.11%42-1.022059x-1.022059x1.02x1.000000x1.000000x19.576%19.6%19.154%19.2%43-1.027762x-1.027762x1.03x1.000000x1.000000x19.596%19.6%19.067%19.1%44-1.029280x-1.029280x1.03x1.000000x1.000000x12.877%12.9%12.511%12.5%45-1.033862x-1.033862x1.03x1.000000x1.000000x6.229%6.23%6.025%6.02%46-1.044246x-1.044246x1.04x1.000000x1.000000x13.260%13.3%12.698%12.7%47-1.097207x-1.097207x1.10x1.000000x1.000000x14.217%14.2%12.958%13.0%48-1.110312x-1.110312x1.11x1.000000x1.000000x13.602%13.6%12.250%12.3%49-1.156494x-1.156494x1.16x1.000000x1.000000x9.584%9.58%8.287%8.29%50
F percentage of females in category
M percentage of males in category
# rank of county out of 50 by male-to-female sex ratio

Over-Education Sex Ratio by County#66

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 Montgomery County, selected other counties in the United States, and entities that contain Montgomery County
Female
Male
1x0x1x2xFM#Dayton AreaMontgomeryPalm BeachSalt LakeSt. LouisOaklandMaricopaCuyahogaOrangeOhioBexarMontgomeryPimaSuffolkRiversideDallasWestchesterTarrantFairfieldEast North CentralAlleghenyMidwestNassauBrowardClarkFranklinMecklenburgFairfaxWayneUnited StatesSanta ClaraFultonContra CostaMilwaukeeSan DiegoOrangeFresnoHillsboroughHarrisMiami-DadeHennepinTravisLos AngelesCookWakeAlamedaMiddlesexKingPhiladelphiaSan BernardinoHonoluluQueensSacramentoKingsNew YorkBronx-1.000000x-1.000000x2.159743x2.159743x2.16x1.731%1.73%3.739%3.74%-1.000000x-1.000000x2.146385x2.146385x2.15x1.591%1.59%3.414%3.41%-1.000000x-1.000000x2.101230x2.101230x2.10x2.942%2.94%6.181%6.18%1-1.000000x-1.000000x2.085261x2.085261x2.09x2.448%2.45%5.105%5.11%2-1.000000x-1.000000x1.898944x1.898944x1.90x3.714%3.71%7.052%7.05%3-1.000000x-1.000000x1.827444x1.827444x1.83x3.743%3.74%6.840%6.84%4-1.000000x-1.000000x1.685640x1.685640x1.69x2.336%2.34%3.937%3.94%5-1.000000x-1.000000x1.683297x1.683297x1.68x3.179%3.18%5.351%5.35%6-1.000000x-1.000000x1.678378x1.678378x1.68x2.251%2.25%3.778%3.78%7-1.000000x-1.000000x1.669500x1.669500x1.67x2.031%2.03%3.391%3.39%-1.000000x-1.000000x1.660749x1.660749x1.66x2.139%2.14%3.552%3.55%8-1.000000x-1.000000x1.654377x1.654377x1.65x9.490%9.49%15.700%15.7%9-1.000000x-1.000000x1.652714x1.652714x1.65x3.148%3.15%5.202%5.20%10-1.000000x-1.000000x1.626700x1.626700x1.63x3.043%3.04%4.950%4.95%11-1.000000x-1.000000x1.619416x1.619416x1.62x1.734%1.73%2.808%2.81%12-1.000000x-1.000000x1.609547x1.609547x1.61x2.346%2.35%3.776%3.78%13-1.000000x-1.000000x1.607280x1.607280x1.61x5.915%5.92%9.508%9.51%14-1.000000x-1.000000x1.602206x1.602206x1.60x1.838%1.84%2.945%2.95%15-1.000000x-1.000000x1.600562x1.600562x1.60x4.251%4.25%6.804%6.80%16-1.000000x-1.000000x1.597848x1.597848x1.60x2.216%2.22%3.541%3.54%-1.000000x-1.000000x1.596070x1.596070x1.60x3.932%3.93%6.276%6.28%17-1.000000x-1.000000x1.589934x1.589934x1.59x2.251%2.25%3.579%3.58%-1.000000x-1.000000x1.577713x1.577713x1.58x4.582%4.58%7.229%7.23%18-1.000000x-1.000000x1.575810x1.575810x1.58x2.760%2.76%4.350%4.35%19-1.000000x-1.000000x1.574188x1.574188x1.57x1.803%1.80%2.838%2.84%20-1.000000x-1.000000x1.567965x1.567965x1.57x3.409%3.41%5.346%5.35%21-1.000000x-1.000000x1.566566x1.566566x1.57x2.842%2.84%4.452%4.45%22-1.000000x-1.000000x1.562801x1.562801x1.56x6.111%6.11%9.551%9.55%23-1.000000x-1.000000x1.545600x1.545600x1.55x1.736%1.74%2.683%2.68%24-1.000000x-1.000000x1.526215x1.526215x1.53x2.660%2.66%4.060%4.06%-1.000000x-1.000000x1.513974x1.513974x1.51x4.919%4.92%7.447%7.45%25-1.000000x-1.000000x1.510882x1.510882x1.51x4.604%4.60%6.956%6.96%26-1.000000x-1.000000x1.494007x1.494007x1.49x4.011%4.01%5.992%5.99%27-1.000000x-1.000000x1.492516x1.492516x1.49x2.423%2.42%3.616%3.62%28-1.000000x-1.000000x1.488172x1.488172x1.49x3.830%3.83%5.700%5.70%29-1.000000x-1.000000x1.475755x1.475755x1.48x3.533%3.53%5.214%5.21%30-1.000000x-1.000000x1.471877x1.471877x1.47x1.943%1.94%2.859%2.86%31-1.000000x-1.000000x1.467166x1.467166x1.47x2.841%2.84%4.168%4.17%32-1.000000x-1.000000x1.442161x1.442161x1.44x2.749%2.75%3.965%3.96%33-1.000000x-1.000000x1.414670x1.414670x1.41x3.162%3.16%4.473%4.47%34-1.000000x-1.000000x1.413446x1.413446x1.41x4.324%4.32%6.112%6.11%35-1.000000x-1.000000x1.407975x1.407975x1.41x4.203%4.20%5.918%5.92%36-1.000000x-1.000000x1.390984x1.390984x1.39x3.031%3.03%4.216%4.22%37-1.000000x-1.000000x1.383306x1.383306x1.38x3.586%3.59%4.961%4.96%38-1.000000x-1.000000x1.367936x1.367936x1.37x4.164%4.16%5.696%5.70%39-1.000000x-1.000000x1.362517x1.362517x1.36x4.905%4.91%6.683%6.68%40-1.000000x-1.000000x1.361790x1.361790x1.36x7.280%7.28%9.914%9.91%41-1.000000x-1.000000x1.345994x1.345994x1.35x4.845%4.84%6.521%6.52%42-1.000000x-1.000000x1.341930x1.341930x1.34x3.216%3.22%4.316%4.32%43-1.000000x-1.000000x1.317072x1.317072x1.32x1.605%1.61%2.114%2.11%44-1.000000x-1.000000x1.300801x1.300801x1.30x3.459%3.46%4.499%4.50%45-1.000000x-1.000000x1.285200x1.285200x1.29x2.428%2.43%3.121%3.12%46-1.000000x-1.000000x1.281324x1.281324x1.28x2.879%2.88%3.689%3.69%47-1.000000x-1.000000x1.262281x1.262281x1.26x3.021%3.02%3.814%3.81%48-1.000000x-1.000000x1.245683x1.245683x1.25x9.673%9.67%12.050%12.1%49-1.000000x-1.000000x1.202385x1.202385x1.20x1.526%1.53%1.834%1.83%50
F percentage of females in category
M percentage of males in category
# rank of county 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.

More Topics to Explore

Demographics

Income

Education

More Maps to Explore

Principal City:
Dayton
Cities:
Brookville, Centerville, Clayton, Dayton, Englewood, Germantown, Huber Heights, Kettering, Miamisburg, Moraine, Oakwood, Riverside, Springboro, Trotwood, Union, Vandalia, West Carrollton
Townships:
Butler, Clay, German, Harrison, Jackson, Jefferson, Miami, Perry, Union City, Washington, Wayne
Unincorporated Places:
Drexel
Villages:
Carlisle, Farmersville, New Lebanon, Phillipsburg, Verona
ZIP Codes:
45005, 45066, 45309, 45315, 45322, 45325, 45327, 45337, 45342, 45344, 45345, 45354, 45371, 45377, 45381, 45402, 45403, 45404, 45405, 45406, 45409, 45410, 45414, 45415, 45416, 45417, 45418, 45419, 45420, 45424, 45426, 45428, 45429, 45431, 45432, 45433, 45439, 45440, 45449, 45458, 45459
Unified School Districts:
Beavercreek, Brookville, Carlisle, Centerville, Dayton, Fairborn, Huber Heights, Jefferson Township, Kettering, Mad River, Miamisburg, New Lebanon, Northmont, Northridge, Oakwood, Preble Shawnee, Springboro Community, Tri-County North, Trotwood-Madison, Valley View, Vandalia-Butler, West Carrollton
Congressional District:
OH-10
State Senate Districts:
OH-5, OH-6
State House Districts:
OH-39, OH-40, OH-41, OH-42, OH-43
Neighboring Counties:
Butler, Clark, Darke, Greene, Miami, Preble, Warren
Nearby Counties:
Champaign, Clinton, Fayette, Fayette, Franklin, Hamilton, Randolph, Shelby, Union, Wayne