Income inequality is an increasingly dominant theme in American culture and politics. Data from the IRS covering mean and median income of filing households for 2012 by zipcode allow us to map and interpret the fascinating geography of income differences. Where are the richest areas, the poorest and the most unequal? The IRS data do not give us the distributions of incomes, so this report does not tell us where the largest numbers of rich or poor populations will be found; this can be done from the American Community Survey for large enough units of geography. With the IRS data, the median is the income of the household halfway between poorest to richest after all are ranked by income. The mean, or average income, is the aggregate income of all households divided by the number of households filing a return. Most of the over 44,000 US zip codes have a sufficient mix of lower to higher income households that they do not stand out as extremely rich or poor.

Even many zips with very low mean or median incomes are not so extreme since most of the poor population actually lives in more mixed income areas. Very unequal areas are defined here as having a far higher mean than median income, indicating an imbalance of incomes, e.g. A few very high income households inflate the average over the more typical, median income. The Richest Zip Codes Figure 1 maps the 170 zip codes with more than 1000 people and median incomes over $150,000 or mean incomes over $200,000. The most astounding thing about the map (which shows the number of rich zip codes by the county they are part of) is their concentration in a few areas, led by the country’s premier global city, greater New York city, with 75 of the 170. New York is followed by Washington DC with 23, another sign of the growing wealth of the national capital.

Boston follows with 10, Los Angeles, 18, San Francisco (14), and Chicago (6) and then a scattering in other leading metropolitan areas. There is no such concentration of the super-rich in any rural or small town area. But many are quasi-rural suburban and exurban.

Poverty in ZIP Code 53206: Milwaukee's Poorest Neighborhood. The 53206 ZIP code neighborhood serves as a bellwether for poverty changes in Milwaukee and nationally. In the 1990s prior to welfare reform in Wisconsin it had the largest number of families receiving AFDC (Aid to Families with Dependent Children). Ista P Bmw V55. Most of the African-Americans jailed in Wisconsin come from Milwaukee's poorest ZIP codes, 53206 in particular. It is politically acceptable to attribute the problems of 53206 to an inner-city culture that has abandoned a sense of personal responsibility.

Richest Zip Codes State County Place Zipcode Mean (thousands) NY Westchester Purchase 10577 363 NY Nassau Westbury 11568 351 IL Cook Kenilworth 60043 342 NY Westchester Pound Ridge 10576 338 CA San Mateo Atherton 94027 337 PA Montgomery Gladwyne 19035 333 CA Los Angeles Bel Air 90077 327 NJ Essex Short Hills 07078 322 NY Nassau Glen Head 11548 316 CT Fairfield Weston 06883 286 CT Fairfield New Canaan 06840 308 IL Cook Glencoe 60022 297 But, the reader will protest, there are huge numbers of rich folk in Texas, Florida, Ohio, Pennsylvania, and other states. The reason is that these many rich households are “diluted” in impact because the zip codes are more variable in income. There really is something remarkable about the overwhelming affluence of the key suburban areas of Westchester and Nassau, New York; Fairfield, CT; Fairfax, VA; and Howard and Montgomery, MD. But I believe the map is telling and accurate at highlighting the utter dominance of the economic power of New York and then Washington. Boston retains power beyond its size, while Los Angeles, Chicago, San Francisco, and upstarts in the South scramble for a place. The Richest Areas The zip code with the highest and the 4th highest incomes are in Westchester County, close to the Connecticut border.

The second richest, Westbury, is in Nassau county, New York, which also has the 9th richest. Also in the NYC suburbs are the 8th, in New Jersey just 20 miles west of New York, while 10th and 11th richest are both located in Fairfield County, CT.

Chicago’s north Cook county has the 3rd (Kenilworth) and 12th (Glencoe) richest areas. Los Angeles is home to the 7th richest, Bel Air (northwest of Beverly Hills), Atherton, in San Mateo county, is the 5th richest, and Gladwyne in Montgomery County, PA is the 6th richest. Greater New York then is home to 7 of the 12 richest, followed by Chicago with 2. Quite a concentration. The Poorest Zip Codes The list and map (Figure 2) of counties with poor zip codes may surprise the reader more. I divide the 94 poorest areas into five types. • minority population domination, 35 areas, • college or university student majorities, with 25 places, • rural (in the sense of small communities in these counties having been left behind or declined) some 25 areas, • five inner city areas dominated by single men, 5, and • two areas dominated by a large military base.

The poor college areas are zip codes for student dormitory housing, people who are temporarily poor; some military base areas are similarly poor because of barrack housing of single people. The poorest minority dominated areas are mainly Black and in the rural to small city South, except for a few Hispanic dominated areas in the west.

The college poor areas are scattered across the country, especially in the East, the military base communities in Texas and Oklahoma. The rural set is surprisingly concentrated mainly in the north, especially in Michigan. The few inner city poor areas are in Los Angeles, Waterbury, CT: Portland, OR; Youngstown and Canton, OH; an odd set. A few of the rural areas also have correctional institutions. Poorest Zip Codes State County Place Zipcode Median NE Douglas Omaha 68178 $2,499 KY Elliott Burke 41171 $3,494 GA Clinch Cogdell 31634 $3,886 FL Gulf Wawahitchka 32465 $4,481 CT Tolland Storrs 06269 $6,124 WI Dane Madison 53706 $6,359 VA Nottoway Blackstone 23824 $6,421 MI Clare LeRoy 49665 $6,639 TN Rutherford Murfreesboro 37132 $7,125 IN Delaware Muncie 47306 $6,750 NY Cattaraugus Salamanca 14779 $7,395 If I had relaxed limit by including more smaller population areas, or not quite such low incomes, many more college, military base, minority majority counties would appear on the map. But as noted up front, virtually none of these poorest zip codes are in big cities or their metropolitan areas, where millions of poor households live, simply because these metro zip codes tend to be large and more heterogeneous.

This also does not factor in the cost of living, which can be high in some regions, particularly on the east and west coasts. The Poorest Areas The 12 poorest zip codes are different and quite varied in character. Five of the zip codes are essentially college or university student housing, and thus not indicative of an adult working population. Three areas are in part poor because of the presence of correctional institutions or adult care institutions. Two of these also have a significant minority (Black) population. Two rural areas, in GA and VA have high Black shares. This leaves two northern rural areas in Michigan (high seasonal dependency) and in New York, Salamanca, also a seasonal resort, as well as an Indian reservation.

Unequal Zip Code The unequal zip codes (67) are mainly areas where the mean is at least twice the median, showing the disproportionate effect of a few very wealthy households. One critical area for high inequality are primarily beach or mountain communities with richer retirees serviced by lower-paid workers; these include 13 areas in California, South Carolina, Florida, New York, Nevada, North Carolina, and Colorado.

Downtowns (8 areas) include a few actual downtown CBD zip codes with an older poor population and newer rich folk. Rural here identifies mainly small Kentucky zip codes with a very imbalanced income pattern (7 areas). Finally I note a few zip codes in exurban areas where there appears to be a juxtaposition of an older resident population, and newer wealthier households (3 areas).

This pattern may become more common in both exurban and rural small-town environmental amenity areas. Informative piece, but someone seriously needs to copy edit this. I'm not a nitpicky internet troll, but this article is out of control, and an embarassement both to Mr. Morrill and this website.

-the '0' is dropped when it is the first digit of a zip code, in all the zip code lists -in the section under the 'Richest Areas' heading: it is WestBURY, not WestBORO, NY. You have it right in the list, wrong in the text. Also, Short Hills, NJ is 20 miles west of New York, not Newark (it is west of Newark, but not nearly as far) -the article both misspells, and fails to capitalize, Montgomery County, PA, in two separate place -random and inconsistent use of quotations when stating the heading ['The Poorest' Zip Codes] -terrible grammer: 'the rural set is surprisingly concentration.' I am an avid reader and fan of this site, so I know this is a rare aberration. I point this all out in hopes that Newgeography.com can maintain its normally high standards going forward.

The Institute for Research on Poverty (IRP) at the University of Wisconsin–Madison is a center for interdisciplinary research into the causes and consequences of poverty and inequality and the impact of related policies and programs. As the National Poverty Research Center sponsored by the U.S. Department of Health and Human Services, IRP coordinates the U.S. Collaborative of Poverty Centers in an integrated set of activities with the ultimate goal of improving the effectiveness of public policies to reduce poverty and inequality and their impacts on the well-being of the American people.

In order to identify who is poor in Wisconsin, and how earnings and public policy affect poverty, a poverty measure that accounts for market income and the full range of social welfare transfers is most useful. The official poverty measure (OPM) used by the Census Bureau is useful for looking at long-term trends in cash income, but does not provide a timely, inclusive analysis of needs and resources that is essential to an accurate assessment of poverty.

The OPM does not indicate, for example, the antipoverty effects of noncash benefits such as the Supplemental Nutrition Assistance Program (SNAP; called FoodShare in Wisconsin), nor of tax transfers such as the Earned Income Tax Credit (EITC). Without the right tool, state policymakers cannot gauge the antipoverty effectiveness of FoodShare, on which the state spent more than $1 billion in 2015, according to the Wisconsin Department of Health Services. Nor does the OPM indicate to state legislators the antipoverty effects of the Wisconsin Earned Income Tax Credit (EITC), which 252,918 tax filers claimed in tax year 2014 for a total of about $100 million in credits (the average credit was $394), according to the Wisconsin Department of Revenue. To address some of the official measure's shortcomings, the Census Bureau introduced an additional measure, the Supplemental Poverty Measure or SPM.

The SPM is designed to account for the contemporary needs and resources of American families, counting noncash benefits and tax credits and using a poverty threshold that represents modern standards of living. The SPM considers childcare, work, and variation in medical expenses, and adjusts for geographic differences in prices. But the SPM is designed to provide information on combined levels of economic need at the national level or within large subpopulations or areas, and it uses data from the Current Population Survey, which is best used for large-area estimates.

For the most accurate estimates of Wisconsin poverty, researchers developed the Wisconsin Poverty Measure, described in the next section. The Wisconsin Poverty Project To better understand how earnings, benefits, and certain expenditures (e.g., childcare, work transportation, medical costs) affect poverty in Wisconsin, and with the goal of providing a model for other states and localities seeking to better understand economic need in their area by building their own place-specific poverty measure, researchers at the Institute for Research on Poverty (IRP) launched the Wisconsin Poverty Project and started developing the Wisconsin Poverty Measure (WPM) in 2008. The WPM follows the SPM design in many respects, but goes beyond it to reflect the characteristics and policy interests of the state, and it uses data from the American Community Survey (ACS), which has relatively large sample sizes and is recommended by researchers for more accurate state- and sub-state-level poverty estimates than can be obtained by the SPM or OPM. WPM researchers' most recent findings, for 2014, are detailed below. The Wisconsin Poverty Report Findings In June 2016 researchers released the eighth annual, by Timothy M.

Smeeding and Katherine A. The report charts the antipoverty effects—for the entire population overall, for children, and for the elderly—of taxes, SNAP, housing programs, and energy assistance from 2008 through 2014.

The report also shows the poverty-increasing effects of work expenses such as childcare, and out-of-pocket medical costs for the same time period. The Wisconsin Poverty Report shows that using the Wisconsin Poverty Measure—which counts market income, cash benefits, and noncash benefits such as FoodShare and tax credits such as the EITC—overall poverty in the state in 2014 was 10.8 percent, which is not statistically different from the overall poverty rate of 10.9 percent in 2013. Market-income-only poverty—which counts as resources only earnings, investment income, private retirement income, and child support (excluding federal and state noncash antipoverty benefits such as FoodShare and tax credits such as the EITC)—dropped slightly from 24.4 percent in 2013 to 24.2 percent in 2014, not a statistically significant decrease. By contrast, poverty in Wisconsin using the OPM, which adds in the value of public cash benefits (excluding FoodShare and tax credits such as the EITC), dropped significantly, from 13.4 percent in 2013 to 12.1 percent in 2014, but still remained more than a percentage point above the WPM rate of 10.8 percent. Comparing the WPM, market-income-only, and OPM poverty measures provides a nuanced picture of economic hardship in the state (see Figure 1). Wisconsin Poverty Measure researchers' main conclusions from analyzing the data are as follows: • The Wisconsin poverty rate for the overall population as measured by the WPM was flat between 2013 and 2014 at about 10.8 percent, up from 10.2 percent in 2012. • Child poverty under the WPM also remained flat from 2013 to 2014, at 11.8 percent.

• Elderly poverty rates using the WPM dropped from 10.0 percent in 2013 to 8.3 percent in 2014, mostly due to cost-of-living increases in Social Security benefits. • Although Wisconsin added almost 60,000 jobs from January 2013 through November 2014, there was no reduction in poverty as measured by the WPM. • Despite the rise in employment, decreases in benefit levels from programs that help people who would otherwise be poor, such as FoodShare assistance and the EITC refundable tax credit, in 2014, resulted in no change in overall or child poverty with the WPM. • Market-income poverty (which reflects employment levels and wages, and is therefore a helpful gauge of economic health) decreased overall by 0.2 percentage points and the official poverty rate fell significantly, from 13.4 percent in 2013 to 12.1 percent in 2014.

The next section explores Wisconsin Poverty Report findings on poverty among children in Wisconsin. Child Poverty in Wisconsin Child poverty rates, shown from 2008 to 2014 in Figure 2, decreased significantly from 2013 to 2014 under the market-income-only (24.4 to 23.0 percent) and official (19.2 to 17.6 percent) poverty measures, while the WPM for children (11.8 percent) remained flat. Wisconsin Poverty Project researchers note that changes in market income, which are essentially changes in employment and earnings, appear to account for the trends in market and official child poverty between 2010 and 2014. Families with children benefited somewhat from the recovering economy in 2014, and using the official poverty measure that translated into a significant decline in official child poverty.

However, the WPM child poverty rate stayed the same despite an increase in employment, which researchers think is because the WPM counts benefit levels of antipoverty programs such as FoodShare and the EITC refundable tax credit, which dropped in 2014. The official Wisconsin child poverty rate in 2014 was 17.6 percent. However, under the WPM, child poverty in Wisconsin was significantly lower (11.8 percent). Researchers suggest several primary reasons why the child poverty was lower under the WPM than in official statistics. The first is that the WPM, unlike the official measure, counts the income of unmarried partners as contributing to family resources, which makes a substantial difference in estimating child poverty because many poor children live with single mothers and their unmarried partners. Another reason why the WPM finds a lower child poverty rate than the OPM is because families with kids are eligible for a broader range of tax credits (e.g., the Earned Income Tax Credit is primarily for families with children) and also have high take-up rates of SNAP and other noncash safety net programs, which are counted by the WPM but not the OPM. In other words, the WPM shows that Wisconsin's safety net enhances low earnings for families with children, puts food on the table, and encourages self-reliance, and in doing so makes a big difference in combatting poverty.

The next section looks at elderly poverty in Wisconsin. Elderly Poverty in Wisconsin Elderly poverty in the state, depicted from 2008 through 2014 in Figure 3, is consistently higher under the WPM (8.3 percent in 2014) than the official measure (6.8 percent in 2014), mainly because elderly individuals often have out-of-pocket medical expenses that are not considered by the official measure and which exceed the noncash benefits they receive. However, despite high medical expenditures, the poverty rate among the elderly under both the official and WPM measures dropped significantly from 2013 to 2014, mainly due to cost-of-living adjustments (COLA) in Social Security benefits, and WPM inflation adjustments to the WPM poverty threshold, which were less than the COLA. The significant increases and decreases in elderly poverty in the past few years shown in Figure 3, are explained by a number of factors. First, there are a fairly large number of elderly individuals and couples whose incomes are just slightly above or below the poverty line, which causes small changes in inflation adjustments to move them from one side of the poverty line to the other, as appears to have happened in 2013 and 2014 in Wisconsin. In addition, the 2014 rise in medical out-of-pocket expenses was less than the benefit increase in 2013, hence using a smaller fraction of elder incomes. These factors contributed to the WPM poverty rate among the elderly bouncing jaggedly from 2012 to 2014, rising to its highest level since 2009 under the WPM in 2013 (10.0 percent), but then falling back in line with the 2011 WPM poverty rate in 2014 (8.3 percent).

In all cases, the WPM rate is higher than the OPM. The next section explores what the WPM indicates about the effects of Wisconsin policies on poverty. Using the WPM to Assess the Effect of Policies on Wisconsin Poverty This section describes what poverty rates would have been if noncash and tax benefits or work-related resources/expenses and medical resources/expenses had not been taken into account. Noncash and tax benefits lower poverty rates under the WPM by increasing disposable income. Meanwhile, higher expenses for childcare, work, and medical care move in the opposite direction to raise poverty. Thus it is important to consider the impact of policies designed to reduce these expenses on poverty, because they are as important as safety net programs in improving the economic well-being of low-income families, as well as unavoidable work-related expenses, which may decrease the economic well-being of these families.

Among the benefit programs examined in this analysis and depicted in Figure 4, SNAP/FoodShare benefits had the greatest impact in 2014, reducing the percentage of people in poverty by approximately 1.9 percentage points. However, this effect has fallen over the past few years as FoodShare benefits have contracted in Wisconsin. The second largest antipoverty effect was from tax provisions such as the EITC. These effects were smaller in 2014 than in 2010/2011. In earlier years, there was the Making Work Pay tax credit (which boosted earnings for the majority of workers and was in effect in 2009 and 2010) and a 2 percentage point reduction in payroll taxes (which was in effect in 2011 and 2012).

Neither the work tax credit nor the cut in payroll taxes were in effect in 2012/2013 or 2014, and as a result, the net effect of taxes and tax credits was less likely to lift the working poor out of poverty than in 2010/2011. The WPM indicates that both SNAP/FoodShare and taxes had a larger effect on reducing child poverty than overall poverty, but the impact of both programs was smaller in 2012 to 2014 than 2010/2011, as can be seen by comparing Figures 4 and 5. In 2014, tax-related provisions reduced child poverty by 4.5 percentage points and SNAP/FoodShare benefits reduced child poverty by 3.3 percentage points. Although the net impact of the EITC and other tax provisions decreased in 2014 as compared to earlier years shown in Figure 5, it was still substantial, reducing child poverty by 4.5 percentage points. The relative increased impact of work-related expenses on poverty since 2010/2011 is consistent with rising costs for work-related expenses like childcare in an economy with more people working but earning flat or falling wages, especially low-skill workers.

Researchers noted a steady decline in public spending on childcare subsidies under the Wisconsin Shares program since 2008, which also may contribute to families' rising out-of-pocket work expenses. Taxes had a negligible effect on elderly poverty, as shown in Figure 6, and SNAP/FoodShare reduced elderly poverty by 0.9 percentage points in 2014, much less than for children (Figure 5). This pattern is expected because the largest tax credits are focused on working individuals who are parents of minor children. SNAP and housing and energy assistance provide modest assistance to all groups, each of them reducing poverty by 1.0 percentage point or less in any year. Medical expenses increased poverty for all groups, but the effects of medical expenses were felt most acutely by the elderly, who are more likely to be in need of costlier and sustained medical care. In general, out-of-pocket medical expenses (e.g., insurance premiums, co-payments for medical services, prescription and over-the-counter drugs, and uninsured medical expenses) present a significant challenge for the low-income elderly and these costs continue to rise in Wisconsin and elsewhere. Medical costs increased elderly poverty rates by 2.8 percentage points in 2014, less than in earlier years, but still by a large amount (Figure 6).

For elders, medical cost increases swamped the sum of all noncash benefits and led to a 1.5 percentage point higher WPM rate than that found in the official measure (Figure 3, compare 8.3 percent to 6.8 percent in 2014).This suggests that public policies designed to increase the coverage of medical expenses for the low-income elderly can do more to alleviate the economic hardship felt by this group than most any other policy. Poverty Rates by County or Multicounty Area A significant strength of the WPM is its ability to portray poverty across regions within the state. In 2014, researchers found high poverty rates in some areas, especially central Milwaukee and Kenosha, but with many more substate areas doing much better than the rest of Wisconsin compared to previous reports, as shown in Table 1. WPM researchers' labeling of substate areas includes 13 large counties and 15 multicounty areas that encompass the remaining areas of the state. While some of the multicounty areas comprise only two counties (e.g., Sauk and Columbia), others require as many as 7 to 10 of the more-rural counties in order to reach a sufficient sample size to obtain reliable estimates. Source: IRP tabulations of 2014 American Community Survey data. Notes: NS = Not statistically significant.

In this analysis, each region's difference from the state average was assessed as not statistically significant if the 90% confidence intervals for each region's statistics and the state's overall statistics overlap. Estimates for poverty rates using the WPM for these substate areas (Table 1) range from 17.3 percent in Milwaukee County (and 16.7 percent in Kenosha) to 4.4 percent in the Washington/Ozaukee multicounty area. As shown in Map 1, Milwaukee, Dane, Kenosha and Walworth counties were the places with rates significantly higher than the state average of 10.8 percent. Meanwhile, eight areas have rates that are significantly lower than the statewide rate, including the counties of Washington/Ozaukee, Fond du Lac/Calumet, St.

Croix/Dunn, Marathon and Sheboygan, which Table 1 shows are all below 6 percent, Waukesha at 6.4 percent, and with several others in northeastern Wisconsin below 8 percent. Poverty estimates for some regions within the state's largest counties (not shown) can also be assessed by taking advantage of relatively large sample sizes for ACS data. Poverty rates examined across subcounty regions show variations that are more dramatic within counties than across the 28 areas in the state. For instance, within Milwaukee County, overall poverty rates ranged from about 8.0 percent in one southern subcounty area to 33.5 percent in the central city of Milwaukee in 2014, suggesting significant segregation by income within that county. Furthermore, Milwaukee is surrounded by wealthy suburban counties to the north and west, where overall poverty rates are also notably below the state average (e.g., Waukesha County at 6.4 percent and Washington/Ozaukee counties at 4.4 percent). The next section summarizes the antipoverty policy recommendations of Wisconsin Poverty Report author Timothy Smeeding, Lee Rainwater Distinguished Professor of Public Affairs and Economics and former Director of IRP at the University of Wisconsin–Madison. Antipoverty Policy Recommendations 'We believe that the long-term solution to poverty for the able-bodied non-elderly is a secure job that pays well, not an indefinite income support program,' notes Wisconsin Poverty Report author Timothy Smeeding.

However, much employment is on a low-wage, part-time basis, and does not provide enough income for low-educated parents to stay out of poverty. This reality, Smeeding says, 'underscores the importance of a safety net that enhances low earnings for families with children, puts food on the table, and encourages self-reliance—as Wisconsin's safety net does—and in so doing makes a big difference in combatting poverty.'

The setting, consequences and social issues surrounding shootings and homicides in Wisconsin's largest city are the focus of ',' a new Wisconsin Public Television documentary. Produced by Frederica Freyberg, the piece examines how poverty, educational disparities and decades of in Milwaukee shape the largely African-American neighborhoods that suffer most from gun violence. In 2015, Milwaukee's homicides were overwhelmingly concentrated in majority-black zip codes in central and northwest portions of the city. One of those zip codes, 53206 — bounded generally by I-43 on the east, West Capitol Drive on the north, North 27th Street on the west, and West North Avenue on the south — has become social-science and media shorthand for the violence, hunger, lack of opportunity and sense of hopelessness plaguing Milwaukee's poorest residents, most of them people of color.

Although 53206 has the highest poverty rate of any zip code in the city and, homicide numbers show how social and economic factors collide across a much broader swath of Milwaukee, which has attracted increasing notoriety for being among the nation's. Although crime, poverty and other social factors often overlap, mapping these factors in Milwaukee shows dramatic divisions that align with the city's racial segregation.

Of course, analyzing U.S. Census data by zip code is not perfect, and as some journalists and the makers of the recent documentary ' have stressed in pushing back against the zip code trope, no community should be defined solely by its ills. But the data do shed light on pressures facing victims (including the families and communities of those killed and wounded in violent crimes) and perpetrators of gun violence in Milwaukee. In zip codes where the highest numbers of homicides occurred in 2015, residents are less likely to have high-school degrees. These are also zip codes where the population tends to be strongly dominated by a single racial identity. As 'Too Many Candles' notes, Milwaukee Public Schools are undertaking an effort to provide trauma resources for children — yet another instance in a of schools trying to fill gaps in mental health services. In much of northern Milwaukee, even people who have health insurance or can afford out-of-pocket health-care costs may have trouble finding convenient access to therapy and other mental-health services, federal statistics show.

A large area of the city is a mental health professional shortage area, a the U.S. Department of Health and Human Services confers on geographic areas that have high ratios of residents to providers (like psychiatrists) and/or have unusually high mental-health service needs.

Milwaukee shares this problem with most of Wisconsin's rural areas. Of Wisconsin's 72 counties, 44, in their entirety, were designated shortage areas in 2013, in addition to portions of Milwaukee and Rock counties. Not all shortage areas in the city of Milwaukee are exactly the same in their ratios of residents to providers or in their demand for services, but geographically they correspond closely with the sections of north and western Milwaukee that align with the zip codes that experience the highest rates of poverty and violence. The blue shaded region indicates the portions of the city of Milwaukee designated as a mental health professional shortage area. Department of Health and Human Services These areas of Milwaukee are also where renters are the most financially strained. Poor Americans 'are facing one of the worst affordable housing crises in generations,' Harvard sociologist wrote in a.

A University of Wisconsin-Madison graduate, he spent about 15 months living in Milwaukee to conduct research on housing insecurity and eviction, yielding many data and publications, including the book '.' Published in March, 'Evicted' unites statistical analyses of eviction patterns with journalistic accounts of the lives of individual Milwaukeeans experiencing eviction, highlighting the experiences of landlords and repeat evictees. Desmond that eviction has come to affect poor black women to much the same degree that incarceration has affected poor black men.

In a recent with the non-profit How Housing Matters project, he said sociologists need much more data on evictions, but the he led between 2009 and 2011 found that 13 percent of low-income private renters were 'involuntarily displaced from housing in the two years prior to being surveyed.' These evictions disproportionately struck black and Hispanic Milwaukeeans. In his 2015 report, Desmond noted that two-thirds of families living below the poverty line in the United States receive no public assistance, and he showed housing assistance for the poor has not kept up with rising housing costs. Rent is also taking up a bigger cut of poor Americans' income. In 2013, 1 in 4 American renter households spent 50 percent or more of their income on rent, a by Enterprise Community Partners and the Harvard Joint Center for Housing Studies found, and the problem will only get worse.

Census Bureau data don't quite capture this proportion of household budgets dedicated to housing. The census measures 'gross rent as a percentage of household income,' but the highest category it uses is '35 percent or more.' Therefore, available census data do not aid investigations of where the toughest of the tough rent squeezes are in Milwaukee. But the '35 percent or more' measurement, assessed by zip code, reinforces the pattern of disparities in Milwaukee. Desmond considers eviction to be not just a cause of poverty, but also a challenge that keeps the poor from escaping their circumstances.

As he explained in a on Wisconsin Public Television's 'Here And Now,' an eviction can have insidious cascading effects, ranging from unemployment to interrupting children's education. Desmond's work provides additional evidence that these are not simply factors that coexist geographically, but rather a complex of related issues that can feed into one another, contributing to gun violence that claims innocent victims. 'It's no longer to the point where if you don't hang out at the wrong places or with the wrong people, then you have nothing to worry about,' Milwaukee's said in WPT's documentary, 'Too Many Candles.' Indeed, simply living in the midst of segregation and economic neglect is itself a risk factor.