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Brown County, MN
Welcome to Brown County.
Brown County has a population of 25.9 thousand and a total area (land and water combined) of 618.3 square miles.
Total GDP for the county increased 16%, in the five years ending in 2022, to reach $1.7 billion.
Between 2000 and 2024 presidential elections, Brown County voted for Republican candidates six out of six times and never voted for a Democratic candidate.
Brown County comprises New Ulm, MN micropolitan area.
The largest city in the county is New Ulm.
Surrounding counties and county equivalents include: Blue Earth County, Cottonwood County, Nicollet County, Redwood County, Renville County, and Watonwan County.
Presidential Election
🔵 Democrat 🔴 Republican
| Election Year Candidates (Party)* | Votes (% Total) | Landslide Scale** |
|
|---|---|---|---|
| 🔵 | 🔴 | ||
| 2000 🔴George W. Bush 🔵Al Gore | 36.23% | 57.43% | ++ |
| 2004 🔴George W. Bush 🔵John Kerry | 37.44% | 60.93% | ++ |
| 2008 🔵Barack Obama 🔴John McCain | 42.65% | 54.74% | + |
| 2012 🔵Barack Obama 🔴Mitt Romney | 40.42% | 56.99% | + |
| 2016 🔴Donald Trump 🔵Hillary Clinton | 27.31% | 63.2% | ++ |
| 2020 🔵Joe Biden 🔴Donald Trump | 32.48% | 65.27% | ++ |
| 2024 🔴Donald Trump 🔵Kamala Harris | |||
| Notes: *Presidential Tickets 2000 (R) George W. Bush & Dick Cheney; (D) Al Gore & Joe Lieberman 2004 (R) George W. Bush & Dick Cheney; (D)John Kerry & John Edwards 2008 (D) Barack Obama & Joe Biden; (R) John McCain & Sarah Palin 2012 (D) Barack Obama & Joe Biden; (R) Mitt Romney & Paul Ryan 2016 (R) Donald Trump & Mike Pence; (D) Hillary Clinton & Tim Kaine 2020 (D) Joe Biden & Kamala Harris; (R) Donald Trump & Mike Pence 2024 (R) Donald Trump & JD Vance; (D) Kamala Harris & Tim Waltz **Landslide Scale (county) + Victory margin was less than 20 percent ++ Victory margin was between 20 to 49 percentage points of more votes +++ Victory margin was more than half but below three quarters ++++ Victory margin was three quarters or more |
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Economic Significance
| Key Industry | As % County GDP | GDP Growth* |
|---|---|---|
| County GDP1 | 100% | 16% |
| Government Enterprises | 9% | 15% |
| Private Industries | 91% | 16% |
| Agriculture | 12% | 26% |
| Mining | <1% | 0% |
| Utilities | <1% | 0% |
| Construction | 5% | 28% |
| Manufacturing | 21% | -5% |
| – Durable | 11% | -22% |
| – Nondurable | 10% | 24% |
| Trade | <1% | 0% |
| – Wholesale | <1% | 0% |
| – Retail | 6% | 23% |
| Transportation | 5% | 77% |
| Information | 5% | 41% |
| Finance & Insurance | 4% | 28% |
| Real Estate | 7% | 0% |
| Professional & Business Services | 7% | 64% |
| Educational Services | 1% | -48% |
| Healthcare & Social Services | 9% | 13% |
| Arts, Recreation & Entertainment | <1% | -10% |
| Accommodation & Food Services | 2% | 22% |
| Other Services2 | 2% | 31% |
| Notes: *Growth rates are based on most recent five-year value available and are updated dynamically 1. All industry total. 2. Includes various repairs, maintenance, personal care, religious organizations, social and professional organizations etc. |
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Local Vibrancy
| Total* | Vibrancy Indicator (1-100)** |
|
|---|---|---|
| 🚙 Automobile Dealers | 6 | 35 |
| 🪑 Furniture & Home Furnishings | 4 | 22 |
| 📺 Electronics & Appliance | 3 | 18 |
| 🛒 Grocery Stores | 5 | 28 |
| 🍾 Beer, Wine & Liquor Stores | 3 | 19 |
| ⚕️ Health & Personal Care | 6 | 32 |
| ⛽ Gasoline Stations | 13 | 42 |
| 👕 Clothing Stores | 6 | 34 |
| 👠 Shoe Stores | ||
| 🧳 Luggage, Leather Goods & Jewelry | ||
| 🏸 Sporting Goods | ||
| 📚 Book Stores & News Dealers | ||
| 🏬 Department Stores | 8 | 38 |
| 💐 Florists | ||
| 🎁 Gift, Novelty & Souvenir Shops | ||
| 🐕 Pet & Pet Supplies | ||
| 🍺 Bars & Taverns | 11 | 59 |
| 🍽️ Full-service restaurants | 22 | 54 |
| 🍔 Limited-service restaurants | 18 | 45 |
| 🏠 Offices of Real Estate Agents & Brokers | 3 | 12 |
| 🛏️ Traveler Accommodation | 3 | 14 |
| Notes: * Total counts of establishments are places of business. ** Vibrancy score is based on comparisons of relevant establishments per square mile of land area among all counties and county equivalents in the United States; A score of 100 means the county is in the top one percentile. |
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Wealth Indicators
| Household Income by Distribution | ||||
|---|---|---|---|---|
| As % County’s Total Household Income | Average Household Income | Maximum Household Income |
||
| Top 5%1 | 20% | $346,300 | N/A | |
| Top 20% | 47% | $206,200 | $206,600 | |
| Next highest 20% | 24% | $104,800 | $129,100 | |
| Middle 20% | 16% | $71,000 | $86,200 | |
| Second lowest 20% | 9% | $41,700 | $55,700 | |
| Bottom 20% | 4% | $16,000 | $29,200 | |
| Notes: 1. Households with aggregate income above 95% of all households in the county |
||||
| % Total Households | Average Household Income* |
|||
| Wages & Salaries | 80% | $83,300 | ||
| Self-Employment | 11% | $36,700 | ||
| Investment Income1 | 19% | $23,300 | ||
| Social Security | 26% | $23,400 | ||
| Supplemental Security | 4% | $10,600 | ||
| Public Assistance | 3% | Below $10,000 | ||
| Retirement | 18% | $26,800 | ||
| Notes: * Calculation based on households claim to have specific source of income; Households may have more than one source of income 1Includes interest, dividends, and net rental income |
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| Under 25* | Age 25 to 49* | Age 50 to 64* | Age 65 & Older* |
|
| Below $25K | 1% | 2% | 4% | 6% |
| $25K – $49K | <1% | 5% | 6% | 10% |
| $50K – $99K | 2% | 11% | 12% | 11% |
| $100K – $199K | <1% | 9% | 12% | 5% |
| $200K & more | <1% | 1% | 2% | <1% |
| Notes: * Age of heads of households |
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| Median Earnings by Worker Characteristics | ||
|---|---|---|
| Race/Ethnicity | Male | Female |
| Non-Hispanic White | $41,900 | $29,200 |
| African-American | $23,400 | |
| Hispanic | $35,000 | $25,400 |
| Asian | $18,000 | $24,900 |
| Level of Education | Male | Female |
| Below Highschool | $40,000 | $22,500 |
| Highschool & GED | $45,900 | $29,300 |
| Some College to Associate’s | $49,800 | $39,300 |
| College/Bachelor’s Degree | $59,600 | $48,300 |
| Graduate Degree | $86,600 | $65,600 |
| Occupation | Male | Female |
| White-Collar1 | $68,200 | $50,300 |
| STEM2 | $71,000 | $60,600 |
| Gray-Collar3 | $36,700 | $34,400 |
| Pink-Collar4 | Below $10,000 | $23,600 |
| Sales5 | $45,100 | $14,000 |
| Blue-Collar6 | $34,100 | $26,300 |
| Notes: 1. Management, business, science, and arts occupations 2. Computer, engineering, and science occupations 3. Protective service occupations including firefighters and law enforcement 4. Personal care and service occupations 5. Sales and sales related 6.Production, transportation, and material moving occupations |
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Climate Risks
| Disaster Category | Natural Hazard Risk Rating | Annual Loss Per Capita |
|---|---|---|
| Avalanche | ||
| Coastal Flooding | ||
| Cold Waves | +++ | $ |
| Drought | +++ | $ |
| Earthquake | + | |
| Hail | + | $ |
| Heat Waves | + | $ |
| Hurricane | ||
| Ice Storm | + | $ |
| Landslide | + | $ |
| Lightning | + | $ |
| Riverine Flooding | + | $ |
| Strong Wind | ++ | $ |
| Tornado | + | $ |
| Tsunami | ||
| Volcanic Activity | ||
| Wildfires | + | |
| Winter Weather | ++ | $ |
| Notes: Locations with insufficient data or not rated are left blank. +: Very low to Relatively low risk ratings ++: Relatively moderate +++: Relatively high ++++: Very high $: Expected per capita annual loss from the natural disaster is below $100 $$: $100-499 $$$: $500-999 $$$$: $1,000 and more |
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ZIP Codes in Brown County
Hanska, MN 56041
Comfrey, MN 56019
Springfield, MN 56087
New Ulm, MN 56073
Sleepy Eye, MN 56085
ZIP codes are dynamically and randomly generated at no more than ten per click. Refresh to see more.