GREATER TWIN CITIES UNITED WAY F A C E S O F P O V E R T Y
2 0 1 2
Acknowledgements: Greater Twin Cities United Way would like to thank our external reviewers who reviewed an earlier version of this report: Timothy M. Smeeding, director of the Institute for Research on Poverty in Madison, Wisconsin, and Pamela J. Loprest, director of the Center on Income and Benefits Policy at the Urban Institute in Washington, D.C. Both provided detailed feedback about the report. We appreciate the time and effort that they put into the review process, which resulted in a much-improved document.
Authors: Devon Meade Devon Meade is a senior research analyst at Greater Twin Cities United Way. He holds an MSc from the London School of Economics and Political Science in international housing and social change. Devon’s focus areas include poverty, economics, demography, return-on-investment analysis, and geographic information systems. He has been with United Way for 11 years and authored several reports on basic needs, immigration, health and education. Contact Devon at (612) 340-7420 or meaded@unitedawaytwincities.org.
Elizabeth Peterson Elizabeth Peterson is the director of research and planning at Greater Twin Cities United Way. She has a Ph.D. in educational psychology with special emphases in statistics and research methods. She has been with United Way for 20 years, and is in charge of tracking community trends and issues, identifying community needs, measurement, researching best practices, and working with impact area directors on strategic planning. She also authors the United Way Blog at www.unitedwaytwincities.org/blog. Contact Liz at (612) 340-7429 or petersonl@unitedwaytwincities.org.
Copyright Š2012 by Greater Twin Cities United Way.
Greater Twin Cities United Way 404 South Eighth Street Minneapolis, MN 55404 www.unitedwaytwincities.org
1|Page
Table of Contents Table of Contents .......................................................................................................................................... 2 Acknowledgements....................................................................................................................................... 1 Introduction .................................................................................................................................................. 3 Defining Poverty............................................................................................................................................ 8 Poverty Dynamics ....................................................................................................................................... 11 Macroeconomic Impacts on Poverty ...................................................................................................... 11 Income Distribution ................................................................................................................................ 13 Poverty Transitions and Intragenerational Income Mobility .................................................................. 14 Situational Poverty ...................................................................................................................................... 19 Employment Stability .............................................................................................................................. 21 Family Stability and Change .................................................................................................................... 25 Immigrants .............................................................................................................................................. 28 Poverty and Health Care Costs ............................................................................................................... 30 Chronic and Intergenerational Poverty ...................................................................................................... 34 Intergenerational Income Mobility ......................................................................................................... 35 Asset Poverty and Wealth Mobility ........................................................................................................ 36 Children in Poverty.................................................................................................................................. 39 Hard-To-Serve Singles ............................................................................................................................. 42 Veterans .............................................................................................................................................. 42 Ex-Inmates........................................................................................................................................... 43 Victims of Domestic Violence ............................................................................................................. 44 Seniors..................................................................................................................................................... 45 People with Disabilities ........................................................................................................................... 48 Conclusion ................................................................................................................................................... 50 Appendix ..................................................................................................................................................... 50 Data Tables.................................................................................................................................................. 54 References .................................................................................................................................................. 59
2|Page
Introduction The negative consequences of poverty are undeniable–both on individual and community levels. Growing up in poverty can have tremendous long-term consequences on a person’s well-being. Among these are developmental declines, more difficulty in school, and worse health outcomes. At a societal level, having a significant and increasing population in poverty strains our resources as we provide basic needs for those without and provide health care through largely inadequate mechanisms. Poverty also threatens the long-term competitiveness of our region because a well-trained and educated, technologically sophisticated workforce is key to economic success in the 21st century. Finally, poverty also has a nonfinancial cost: Having many of our neighbors living on the edge rends the very fabric of our community. For these reasons and others, Greater Twin Cities United Way leads efforts to create pathways out of poverty. However, dealing with this complex problem requires an understanding of poverty from a variety of angles. That is the purpose of this report. Current Economic Context Three tumultuous macroeconomic trends over the last decade created the context for an increase in poverty and inequality: the bursting of the housing price bubble and home mortgage foreclosure crisis, sharp declines in stock market prices, and record-breaking unemployment levels. After a period of hyperinflation, home prices began to fall, and between 2005 and 2009, Minnesota home prices decreased 19 percent (Taylor, Kochhar, Fry, Velasco, & Motel, 2011). Along with these steep declines was a dramatic increase in foreclosures, with more than 100,000 home mortgages ending in foreclosure since 2007 in Minnesota (HousingLink, 2011). The Great Recession was historic in many ways. The most visible labor market effect was the loss nationally of 7.5 million jobs during its official duration, causing Minnesota’s unemployment rate to rise from 4.7 percent in December 2007 to 8.5 percent in May 2009. One of the most troubling impacts has been an increase in the number of long-term unemployed. Minnesota had 17,400 residents that had been unemployed for more than six months in 2007. Four years later, in June 2011, this had increased to 75,800, including 47,700 that had been unemployed for more than a year (Rohrer, 2011). Not surprisingly, employment and income have significant impacts on poverty rates. In Minnesota, an increase in the unemployment rate of 1 percent increases the poverty rate by 0.2 percentage points, and a 1 percent increase in median earnings reduces poverty by 0.2 percentage points (Grunewald, 2006). These trends, along with others, are all part of a larger trend of increasing income inequality. Recently released research from the Congressional Budget Office shows that between 1979 and 2007 the average real after-tax household income for the lowest 20 percent of earners had grown just 18 percent. This is compared to 65 percent growth among the 20 percent with the highest incomes and a startling 275 percent growth for the top 1 percent of earners (2011).
3|Page
Poverty in a Static View There are two ways we seek to understand this issue. The first is to perform a static census–that is, to take a snapshot of those in poverty at a given point in time. This allows us to consider the demographics of those in poverty and to compare them to those of our community at large. From this we see that nationally and locally, more people are living in poverty today than at any time in the measure’s 50-year recorded history. In the United States, 1 in 7 people lives at or below the federal poverty level. In Minnesota, the figure is 1 in 10.1
Married couple, kids < 18 4.3%
34.0% Female-headed, kids < 18
All families
BA degree or higher 3.0%
Education
7.0%
Some college 7.2%
10.1% High school grad. (or equiv.)
65+ 8.6%
Age
24.8% Less than high school
18 to 64
0 to 5 16.1%
10.3%
Hispanic/Latino 24.4%
6 to 17
Asian/PI 16.9%
Race
12.9%
American Indian
African American 34.8%
37.9%
White 8.6%
Minnesota Poverty Rates, 2010
Household Composition
Source: 2010 American Community Survey, 3-year estimates
But who are the people struggling to make ends meet? Generally speaking poverty is highest, and increasing fastest, for children, populations of color (particularly, African Americans, American Indians, and Hispanics/Latinos), those with low-education levels, and single mothers. There are also varying degrees of poverty. United Way typically uses 200 percent of poverty to define those most in need. If you consider the number of people living at or below 200 percent of poverty ($44,700 for a family of four—an income that still doesn’t stretch to meet a household’s basic needs), the numbers jump to 1 in 3 nationally and 1 in 4 in Minnesota. Even at twice the official poverty level families are not earning enough to meet their basic needs. According to the Jobs Now Coalition, basic necessities (food, housing, clothing, health care, child care) for a Minnesota family of four require an annual income of $56,400. A family of four living at 200 percent of poverty ($44,700) has a gap of $11,700 in meeting their basic needs by this measure.
1
For purposes of this report, we will primarily use the official poverty line as defined by the federal government, despite its shortcomings (see “Defining Poverty” on the following pages), because that is the focus of the vast majority of the poverty research.
4|Page
At the other end of the poverty spectrum is extreme (or deep) povertyâ&#x20AC;&#x201D;people with family incomes less than half the official poverty level, or $11,175 for a family of four. The percentage of people experiencing extreme poverty has increased over the last few years and nationally, as of 2010 (the most recent year for which data are available), 43.6 percent of the poverty population (i.e., people living at or below 100% of the official federal poverty line) was living in extreme poverty, up from 43.3 percent in 2009 and 42.9 percent in 2008. In Minnesota, the percentage of the poverty population living in extreme poverty increased from 42.3 percent in 2008, to 43.7 percent in 2010 (U.S. Census Bureau, 2010). Because families living in extreme poverty have such limited resources (even compared to people at 100% of poverty), it is much more challenging for them to climb out of poverty and they are almost certainly overrepresented in the long-term or chronic poverty population. Rates of extreme poverty are higher among children and African Americans and lower among whites, Asians, and elders (Iceland, 2006).
Percent of Poverty Population in Extreme Poverty, U.S. (below 50% Federal Poverty Guidelines) 45 40 35
Percent
30 25 20 15 10 5 0 1975
1980
Source: U.S. Census Bureau
1985
1990
1995
Recession
2000
2005
2010
Extreme Poverty
Poverty in a Dynamic View But what happens when you look a little deeper? A second way to understand the population of those in poverty is from a dynamic perspective. To do this, we consider not only who is in poverty at a given point in time, but also who is moving into or out of poverty. This approach has three advantages. First, it gives us a more complete view of the number of people living on the edge. While the fact that 11 percent of Minnesotaâ&#x20AC;&#x2122;s population is living in poverty is startling, this statistic underrepresents the true scale of the problem because of the rate at which people cycle into and out of poverty. The second advantage of this approach is that it can help us understand the life events most often associated with moving in and out of poverty. Finally, it can help us understand for what portions of the population poverty is an intransigent problem. That is, it can uncover which people are most likely to stay in poverty for years and even generations. Recently released longitudinal research shows that approximately three-quarters of those that are poor are experiencing short-term poverty compared to roughly a quarter that are considered chronically poor 5|Page
(Anderson, 2011). Research also reveals that nearly half (50%) of people in poverty exit poverty within one year, and three-quarters (75%) exit within three years (McKernan, Ratcliffe, & Cellini, 2009). It should be noted, however, that despite the shortness of most poverty spells, more than half who exited poverty still had incomes less than 150 percent of poverty, and frequently fall into it again a short time later (Anderson, 2011). In fact, about half of those who climb out of poverty return to it within four years (Iceland, 2006). The prospect of economic mobility both within and across generations is the cornerstone on which the American Dream has been built. The research reviewed for this report shows that for the middle class there is still considerable fluidity; however, particularly for those at the bottom of the income ladder, it is much more difficult to move up. In a time when family income growth has slowed, income inequality and relative mobility are increasingly important factors in the changing fortunes of individual families. As income inequality has grown and as economic growth needed to boost incomes across the spectrum has weakened, the question of how much opportunity each individual has to move up or down the ladder is crucial. Disparities As this report highlights, populations of color have disproportionately been negatively impacted by recent economic trends. The Great Recession had particularly profound impacts on the household net worth of populations of color. The gaps in wealth between populations of color and whites are the largest they have been in the quarter century since the Census began publishing such data (Taylor et al., 2011). People of color are also more likely to live in chronic poverty than are whites. Why? The reasons are manifold. For one thing, high levels of residential segregation contribute to patterns of unequal schooling. Segregation can also perpetuate ethnic stereotypes that give rise to discrimination in employment practices and reproduce segregated job referral networks. Areas segregated by race and class frequently saddle poor people with high rent burdens, lack of access to housing wealth, and housing health risks. All of these factors, as well as historic disenfranchisement, contribute to higher, largely entrenched poverty rates (Iceland, 2006; Wilson, 2009). Impact of Public Programs Social insurance programs (primarily Social Security but also federal pensions and unemployment insurance) have a significant impact on poverty. These programs lifted 31 percent of the poor out of poverty (i.e., without the money provided through these insurance programs, they would be included in the poverty counts). The Earned Income Tax Credit (EITC) helped 8 percent of people out of poverty. The EITC has a particularly large impact on working families and children (Iceland, 2006). The Cost of Poverty In her recent book, Rebecca Blank (2011) suggests four reasons why we should care about inequality. First, increases in inequality are a reflection of a decline in the well-being of those at the lowest rungs of the economic ladder. Second, widening inequality reduces economic mobility and makes economic gains even more difficult for the poor. Over time these intensify both economic and social stratifications, 6|Page
having particularly negative long-term impacts on populations of color and single mothers. Third, inequality may have an impact on aggregate economic growth over time. Finally, increasing inequality may affect civic and social behavior outside of economic markets. The costs of childhood poverty to the United States total about $500 billion per yearâ&#x20AC;&#x201D;the equivalent of nearly 4 percent of the GDP.2 Specifically: Childhood poverty reduces productivity and economic output by about 1.3 percent of GDP annually. Childhood poverty raises the costs of crime by about 1.3 percent of GDP annually. Childhood poverty raises health expenditures and reduces the value of health by 1.2 percent of GDP annually. (Holzer, Schanzenbach, Duncan, & Ludwig, 2007). Thus it is not just a moral case that can be made for ending poverty, but an economic case as well. Poverty carries a cost for all of society, not just those who experience it; it reduces the economic potential of our country as a whole. According to calculations conducted by the Center for American Progress, â&#x20AC;&#x153;we could raise our overall consumption of goods and services and our quality of life by about a half trillion dollars a year if childhood poverty were eliminatedâ&#x20AC;?3 (Holzer, Schanzenbach, Duncan, & Ludwig, 2007).
2
GDP, or Gross Domestic Product, is a measure representing the total value of all goods and services produced by labor and property in the United States. 3 These are conservative estimates. The range of estimates is fairly high, and the authors consistently tended towards the lower ends of the estimates.
7|Page
Defining Poverty Poverty in essence refers to economic or income deprivation. Poverty can be defined by absolute measures or by relative measures. Absolute measures attempt to define a basic (absolute) needs standard and they remain constant over time (adjusted for inflation). The current U.S. poverty measure is an absolute measure. Relative measures (more common in Europe) define poverty as a condition of comparative disadvantage, and they are adjusted as standards of living rise or fall. In the 1990s, the National Academy of Sciences developed a quasi-relative measure which combines elements of absolute and relative measures. There are also subjective measures of poverty, which are based on public opinion of what minimum income is needed to exceed the threshold of â&#x20AC;&#x153;poor.â&#x20AC;? Official Poverty Measure The official U.S. poverty measure was developed in the mid-1960s, when food accounted for one-third of the average household budget. Poverty levels are set by using the U.S. Department of Agricultureâ&#x20AC;&#x2122;s Thrifty Food Plan for different family sizes and multiplying it by three. Spending patterns have changed since the 1960s, however, and food now accounts for only 10-15 percent of the average household budget. If the same logic was used today to calculate poverty levels, using the more conservative estimate of 15 percent of household income for food, the poverty level for an individual would be $24,200 rather than the official $10,890; and the poverty level for a family of four would be $49,667 rather than $22,350. These numbers are comparable to the income guidelines provided by the Jobs Now Coalition for a Minnesota family to meet its basic needs.
2011 HHS Poverty Guidelines 48 Contiguous States and D.C. Persons in Family
100%
200%
1
$10,890
$21,780
2
$14,710
$29,420
3
$18,530
$37,060
4
$22,350
$44,700
5
$26,170
$52,340
6
$29,990
$59,980
7
$33,810
$67,620
8
$37,630
$74,720
For each additional person, add
$3,820
$7,640
Source: Federal Register, Vol. 76, No. 13, January 20, 2011, pp. 3637-3638
8|Page
Supplemental Poverty Measure In 2010 an interagency technical working group which included representatives from the Bureau of Labor Statistics, Census Bureau, Council of Economic Advisors, the U.S. Department of Health and Human Services along with others, issued a series of suggestions to the Census Bureau on how to develop a Supplemental Poverty Measure. The new measure is a more complex statistic which incorporates items such as tax payments and work expenses in its family resource estimates. The thresholds are derived from the Consumer Expenditure survey expenditure data on basic necessities (food, shelter, clothing and utilities) and are adjusted for geographic differences in the cost of housing. The new measure is intended to be an indicator of economic well-being and will provide a better understanding of economic conditions and policy effects.
Poverty Measure Concepts: Official and Supplemental Official Poverty Measure
Supplemental Poverty Measure
Measurement units
Families and unrelated individuals
Poverty threshold
Three times the cost of minimum food diet in 1963
Threshold adjustments
Vary by family size, composition, and age of householder Consumer Price Index: all items Gross before-tax cash income
All related individuals who live at the same address, including any co-resident unrelated children who are cared for by the family (such as foster children) and any cohabitors and their children. rd The 33 percentile of expenditures on food, clothing, shelter, and utilities (FCSU) of consumer units with exactly two children multiplied by 1.2. Geographic adjustments for differences in housing costs and a three parameter equivalence scale for family size and composition. Five year moving average of expenditures on FCSU. Sum of cash income, plus in-kind benefits that families can use to meet their FCSU needs, minus taxes (or plus tax credits), minus work expenses, minus out-of-pocket medical expenses.
Updating thresholds Resource measure Source: Short, 2011
The most significant difference between the old measure and the new comes from a special tabulation of data of those individuals between 100 and 150 percent of poverty defined by the Supplemental Poverty measure. This data shows that 51 million individuals have incomes in this range, which is 76 percent higher than the official account for 2010. This places 100 million people, or roughly 30 percent, of the American population either in poverty or just above.
Comparison of Poverty Measures United States, 2010 Numbers in thousands Official Poverty Measure Less than 100% 46,602 100-150% 29,111 150+% 230,397 Total 306,110 Source: U.S. Census Bureau Special Tabulation
9|Page
Supplemental Poverty Measure 49,094 51,365 205,651 306,110
GREATER TWIN CITIES UNITED WAY F A C E S O F P O V E R T Y P O V E R T Y
2 0 1 2
D Y N A M I C S
Poverty Dynamics Poverty dynamics reflect a complex set of interactions between demographic trends and labor market conditions. Influencing factors include education, family structure, workforce participation, gender, age, country of birth, geography, and socialization. The biggest influencing factor is the economy.
Macroeconomic Impacts on Poverty It comes as no surprise that overall economic performance has a cyclical impact on poverty. A strong economy means higher job creation and lower unemployment. For those living in poverty, labor force expansions are particularly beneficial. A tight labor market also means that previously unemployed and part-time workers have more opportunity for employment. Employers often turn to less traditional sources of labor, providing training to workers who otherwise might not have been considered for more skilled positions under different economic conditions (Blank, 2000). Historically, poverty has increased during recessions and decreased during times of economic growth. Research over the last decade, however, has found that the relationship between changes in the poverty rate and macroeconomic variables is weakening. The mid 1980s economic expansion was the first in recent history not to be linked to a notable decline in poverty. The reason for this is that, unlike previous lengthy expansions, it was not accompanied by wage growth (Blank, 2000; Hoynes, Page, & Stevens, 2005; Wirtz, 2006a).
Mean Household Income Received by Income Quintile: U.S. 1967 to 2010 (in 2010 Dollars)
$180,000 $160,000 $140,000
Richest
$120,000
Fourth
$100,000
Third
$80,000
Second
$60,000
Poorest
$40,000
Recession
$20,000 $0 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 Sources: U.S. Census Bureau & Bureau of Labor Statistics
As can be seen in the figure above, after the 1980s recession, there was significant income growth among the highest earners. However, wages actually declined for the poorest. During the expansion from 1983 to 1990, people in the lowest quartile saw a decrease in unemployment due to an increase in 11 | P a g e
hours worked; however, the decline in wages offset the gains in income that they would have otherwise experienced. The expansion in the 1990s brought some increase in wages, but not enough to make up for the previous two decades of wage decline. This reinforced the premise that sustained economic growth is beneficial to the poor only to the extent to which wage growth occurs (Blank, 2000).
Changes in Goods and Service Producing Sectors, U.S. 1965 to 2010 100% Goods Producing Employment 90%
Service Providing Employment
Percent of Total Employment
80% 70% 60% 50% 40% 30% 20% 10% 0% 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Several structural shifts and influencing factors have contributed to the downward trend in wages of less-educated workers. One of the leading forces shaping this trajectory has been the significant change in the structure of the job market in the United States. This market has become steeply polarized over the past two decades with the strongest growth in high-skill, high-wage occupations and low-skill, low-wage occupations, coupled with contracting opportunities in middle-wage, middle-skill jobs. In the years leading up to the Great Recession employment growth was heavily concentrated among low-wage jobs in the service sector such as fast food, banking, child care, and health care attendants (Autor, 2010; Brady, 2006; Newman, 1995; Rynell, 2008).
Source: Bureau of Labor Statistics
The slowing rate of four-year college degree attainment among young adults is another contributing factor. Since the late 1970s, the rate of college degree attainment has not kept up with rising demand for skilled workers; this trend has been particularly severe for males. The rising wage premium that accompanies educational attainment conveys positive economic news but it also masks a more discouraging truth: The increase in relative earnings of college graduates are not just due to a rise in real earnings but also to the falling real earnings of noncollege graduates (Autor, 2010). A related consequence of these economic trends has been a decline in household wealth and income as well as an increase in income inequality. The inflation-adjusted real median income in the Twin Cities metro area fell more than 11 percent, from $70,399 in 2000 to $62,352 in 2010. Statewide, median income fell almost $6,000 over the last decade, to levels not seen since 1989 (U.S. Census Bureau, 2000, 2010). This â&#x20AC;&#x153;hollowing outâ&#x20AC;? of the middle of the job market has had different consequences for men and women. Females generally moved upward in the occupational distribution as they departed the center while males moved in roughly equal measures towards the top and the bottom. The Great Recession generally reinforced this trend rather than moderating it. In particular, jobs and earnings losses were far 12 | P a g e
greater for men with low educational attainment than for women with lower levels of education. As these males have moved out of the middle-skill blue-collar jobs, they have generally moved down in occupational skill and earnings (Autor, 2010). William Julius Wilsonâ&#x20AC;&#x2122;s work highlights the role that manufacturing jobs played for African-American men and how the disappearance of these jobs in the U.S. increased poverty rates for less-skilled workers and familiesâ&#x20AC;&#x201D;particularly in urban areas. Not only do African Americans more often reside in communities that have higher jobless rates and lower unemployment growth, but as over two-thirds of employment growth in metropolitan areas has occurred in the suburbs, many have become physically isolated from places of employment and socially isolated from the informal job networks that are often crucial for job placement (Wilson, 1996, 2009). Other factors include competition for jobs on a global scale, immigration, the decline in the real value of the minimum wage, the increase in use of temporary workers, and the decline of unions (Jones & Weinberg, 2000).
Income Distribution The Great Recession had significant impacts on household wealth. After adjusting for inflation, the median net wealth, or net worth, of U.S. households fell from $96,894 in 2005 to $70,000 in 2009, a drop of 28 percent for the general population. While all racial and ethnic groups experienced drops in wealth there were sharp differences among them. From 2005 to 2009, the inflation-adjusted median wealth of Hispanic Percent Change in Median households fell 66 percent; it declined 53 percent among Net Worth of Households, African Americans and 16 percent among whites. As a result of U.S., 2005 to 2009 these declines, in 2009, the typical African American household African Americans -53% had just $5,677 in wealth (assets minus debts), and the typical Hispanic household had $6,325. This is compared to a typical Hispanics white household which had on average $113,149 in wealth. In -66% other words, the median wealth of white households is 20 times that of African American households and 18 times that of Whites -16% Hispanic households. These ratios are the largest since the Source: Taylor et. al., 2011, Pew Research government began publishing this data 25 years ago, and Center roughly twice the size of what they had been for the two decades prior to the Great Recession (Taylor et al., 2011). The use of quintiles, for comparing aggregate shares of household income received by each fifth of the distribution, is a common method of examining income inequality. The table on the next page shows the income quintile cutoffs for Minnesota in 2010. The aggregate share of income held by households in the poorest quintile is 3.8 percent compared to 47.9 percent in the highest.
13 | P a g e
Income Distribution Quintiles, Minnesota 2010 Quintile Poorest 2nd 3rd 4th Richest
Quintile Upper Limits $24,549 $45,171 $69,305 $103,492 $181,155
Mean Income of Quintile $13,727 $34,694 $56,689 $84,827 $174,314
Percent Shares of Aggregate Income 3.8% 9.5% 15.6% 23.3% 47.9%
(lower limit) Source: 2010 American Community Survey, 3 year estimates
The median wage for all job vacancies in Minnesota (2th Qtr. 2011) falls within the lowest quintile ($20,800 if the job is full-time). Jobs within this quintile are often part-time (38%) and require no education beyond high school (58%). Of the top 10 highest demand jobs in Minnesota, seven have no educational requirements and provide on-the-job training. Two require vocational training, and one requires an associate degree. The average median wage for these 10 occupations if the work is full-time, is $30,731 ($14.77/hour). If registered nurses are taken out of the equation (median annual wage of $73,384) the median wage for the remaining nine drops to $25,991 ($12.50/hour) (Minnesota Department of Employment and Economic Development, 2011).
Poverty Transitions and Intragenerational Income Mobility Understanding how, why, and when an individual or family moves into or out of poverty reveals a much more complete picture of the nationâ&#x20AC;&#x2122;s poor. Itâ&#x20AC;&#x2122;s important to know what events lead people into poverty and what helps them leave poverty. Findings from research show that, contrary to common belief, large portions of the U.S. population will experience poverty at some point, and that poverty spells most often are between one and four years long. Half (50%) of those who become poor in a given year exit poverty a year later, and three-quarters (75%) of poverty spells last less than four years (Cellini, McKernan, & Ratliffe, 2008). Research has shown that slightly more than half (51%) of the U.S. population experiences poverty at some point before the age of 65, and that increases to 59 percent by age 75. Oneâ&#x20AC;&#x2122;s chances of becoming poor are higher for younger people, African Americans, Hispanics/Latinos, those in households headed by women, and those with lower levels of education (McKernan, Ratcliffe, & Cellini, 2009; Rank, 2007). The likelihood of exiting poverty in any given year is about 1 in 3. For African Americans, households headed by single women, and households with more children, the chances are lower. Among those who exit poverty, roughly half will become poor again within five years. Further, for those who were in a poverty spell for at least five years and then escaped, the chances of their returning to poverty are twothirds. The longer the poverty spell, the less likely one is to escape and the more likely to return to poverty after exiting (Cellini, McKernan, & Ratliffe, 2008). While the notion that poverty is transient is important, it is different from the notion of income mobility. Mobility is a more accurate measure of moving individuals from the bottom 20 percent of earners to a higher bracket over time, rather than just over the poverty line. It answers the question of whether it is 14 | P a g e
harder or easier for one to get ahead and stay ahead. Changes in economic mobility are crucial during times of growing economic inequality as it impacts the degree to which families and individuals can move up and down the economic ladder (Bradbury & Katz, 2009; Wirtz, 2006a). The Survey of Income and Program Participation (SIPP) is the U.S. Census Bureauâ&#x20AC;&#x2122;s longitudinal study that provides a dynamic view of poverty. The SIPP interviews a representative sample of U.S. households every four months. The most recently released analysis, March 2011, focuses on data collected in the first 36 months of the 2004 panel. Results show that overall, 55.4 percent of households remained in the same income quintile in 2007 as they had three years earlier, with the remaining 44.6 percent experiencing either upward or downward mobility across the income distribution (Hisnanick & Giefer, 2011). The most interquartile movement occurred in the middle three quartiles, and the least mobility occurred among those in the top and bottom groups.
Percent Distribution of Households by Income Quintile: 2004 and 2007 Top quintile in 2007 (>$92,899) Middle quintile in 2007 ($39,247-$60,576) Bottom quintile in 2007 (<$21,648) 1.6 3.7 6.3 19.3
3.3 8.1 19.2
Fourth quintile in 2007 ($60,577-$92,899) Second quintile in 2007 ($21,648-$39,246)
7.0 20.3 20.3 67.8 46.5 44.4
49.2 69.1 22.0 20.2 6.3 Bottom quintile in 2004 (<$22,367)
Second quintile in 2004 (<$22,367$40,015)
Middle quintile in 2004 (<$40,016$60,895)
22.6
21.5
7.5 3.2
7.4
2.0 1.3
Fourth quintile in Top quintile in 2004 2004 (<$60,896(>$92,886) $92,886)
Source: U.S. Census Bureau, Survey of Income and Program Participation, 2004 Panel; Hisnanick & Giefer, 2011
Among U.S. households, 69.1 percent in the bottom quintile and 67.8 percent in the top were in the same quintile in 2004 and 2007. In comparison, 49.2 percent of those that began in the second, 44.4 percent of those that began in the middle, and 46.5 percent of those that began in the fourth remained in the same quintile (Hisnanick & Giefer, 2011). 15 | P a g e
The amount of income fluctuation varies; among those that stayed in the same quintile, a majority experienced a change in real income of at least 10 percent. Among all households, approximately half experienced either an increase or decrease of less than 25 percent in their income, and another quarter experienced a change of 25 percent or more between 2004 and 2007 (Hisnanick & Giefer, 2011). Between 2004 and 2007, total household income increased $69.9 billion, while the proportion of income in each of the quintiles remained (statistically) unchanged. The increase in household income is explained by the increases experienced by households in the top two quintiles, which offset the declines experienced by households in the other three quintiles (Hisnanick & Giefer, 2011). Approximately one-third of Americans raised in the middle class fall out of the middle class as adults. Research conducted by Pew Charitable Trusts has shown that marital status, education, and race have a strong influence on whether a child that is born into the middle class loses this economic standing as an adult (Acs, 2011). While both men and women who are divorced, widowed, or separated are more likely to slip out of the middle class than are never-married men and women, the impact is particularly strong for women. Married women who experience a change in marital status (divorce, separation, or death) are approximately twice as likely to fall down the economic ladder as never-married women (31-36% compared to 16-19%) (Acs, 2011). Race is also a factor in who falls out of the middle class, but only among men. White, African American, and Hispanic women are equally likely to experience downward mobility out of the middle class. In contrast, nearly 40 percent of African American men fall out, double the percentage of white men who do so. Hispanic men also appear more likely than white men to fall out of the middle class, but the difference is not statistically significant (Acs, 2011). Levels of educational attainment have a strong impact on whether householders are likely to move up or down an income quintile. The strongest patterns of interquartile movement between 2004 and 2007 were for householders with less than a high school education and householders with a bachelor’s degree or higher. Householders with less than a high school education (42.3%) were more than five times as likely as those with a bachelor’s degree or higher (7.6%) to experience a change in income that resulted moving down two or more quintiles in 2007. On the other end of the income distribution, householders with a bachelor’s degree or higher were more likely to experience an increase in income. For example, those with a bachelor’s degree or higher in the bottom quintile (25.1%) in 2004 were more than three times as likely to experience an increase in income that resulted in moving up two or more quintiles in 2007 compared with those with less than a high school education (5.3%) (Hisnanick & Giefer, 2011).
16 | P a g e
Income quintile in 2004
Percent of Households That Moved Across Income Quintiles Between 2004 and 2007 by Educational Attainment of the Householder Bachelor's degree or higher Less than High School
Top
42.3
7.6
Households that moved down two or more income quintiles in 2007
Fourth Middle
25.1
8.5 14.0
3.8
Middle
5.3
Second
5.1
Bottom
5.3 80.0
60.0
40.0
20.0
Households that moved up two or more income quintiles in 2007
11.9 16.9 25.1 0.0
20.0
40.0
60.0
80.0
Source: U.S. Census Bureau, Survey of Income and Program Participation, 2004 Panel; Hisnanick & Giefer, 2011
Changes in educational attainment also affect household income. During this study period, 12.7 percent of householders experienced a change in their level of educational attainment, which could result in an increase in their household income. Individuals with higher levels of educational attainment are, on average, paid higher salaries and wages. Nearly 15 percent of householders who moved up at least two quintiles from the bottom, the second, or the middle quintile experienced a change in educational attainment between 2004 and 2007 (Hisnanick & Giefer, 2011). Poverty Triggers The most common event triggering a poverty episode is a job loss or pay cut. Between 40 and 50 percent of those who become poor live in a household where the head, spouse, or other family member lost his or her job. Other events triggering poverty entry are the addition of a child under age 6 into the household, the shift from a two-parent household to a single female-headed one, or a change in the disability status of a household head (Bane & Ellwood, 1986; McKernan & Ratcliffe, 2005). Employment gains and pay increases are the most common events that lift a household out of poverty. Generally, between 50 and 70 percent of those leaving poverty do so because they, or a family member, obtained employment or had increased earnings. Educational gains, such as receiving a post-secondary degree or certificate, are the second most common. Other events such as a shift in household structure from single female-headed to dual earner or a change in disability status of the head of household also have strong impacts on oneâ&#x20AC;&#x2122;s chances of exiting poverty (Bane & Ellwood, 1986; McKernan & Ratcliffe, 2005).
17 | P a g e
Income Mobility Triggers While employment may be the most common event to pull one above the poverty line, education, particularly schooling beyond high school, is the primary and most consistent driver of sustained upward income mobility. The probability that an individual is able to leave the bottom quintile is more than 30 percentage points higher for those with a high school diploma or more. The second most important characteristic is race, although it should be noted that this has greatly diminished over time. Between 1984 and 1994, the probability that a white person would leave the bottom quintile was 21 percentage points higher than someone of a different race. The strength of this relationship had dramatically decreased during the 1994-2004 period to 8 percentage points. The third characteristic is an increase in the number of hours worked. An extra 1,000 hours of work per year (about 20 hours/week) increased the probability of leaving the bottom quintile by 12 percentage points. This relationship has grown over time and may be a result of the high unemployment rate and the lack of real wage growth for lesseducated workers. Research has found few factors that consistently predict downward income mobility (Acs & Zimmerman, 2008).
18 | P a g e
GREATER TWIN CITIES UNITED WAY F A C E S O F P O V E R T Y S I T U A T I O N A L
2 0 1 2
P O V E R T Y
Situational Poverty Situational or episodic poverty refers to people who are in poverty for a relatively short period of time (i.e., at least two months but less than two years). Situational poverty is in contrast to chronic or longterm poverty. According to the most recently released Survey of Income and Program Participation data (March 2011), approximately three-quarters of those that were poor had been in poverty for at least two or more consecutive months, but not for the entire study period. While local data is not available in this area, if national trends apply, this would roughly equate to an estimated 400,000 people in situational poverty in Minnesota. The survey also found that more than half of those that were in poverty at the beginning of the study and exited by the end continued to have incomes less than 150 percent of poverty (Anderson, 2011).
Episodic Poverty Rates
Distribution of People
Unrelated individuals
39.4%
Male-householder fam.
65 and over 18 to 64 Under 18
51.8%
White alone
Population Episodically Poor
20.9%
15.6
14.4 4.1
21.2
Population
27.7% 36.4%
45.5% 22.6% 26.0%
Episodically Poor White alone Population Episodically Poor
65.9
25.8
Under 18 years
18.1%
African American alone White alone, non-Hispanic
Female-householder families Married-couple families
37.3%
Female-householder fam. Married-couple families
Unrelated individuals Male-householder families
5.3
47.7
18 to 64 years
26.1
65 years and over
11.0
63.0
32.8
6.9
60.4
African American alone
Other race groups
80.7 72.5
12.5 6.8 19.6
Sources: U.S. Census Bureau, Survey of Income and Program Participation, 2004-2006 Panel; Anderson, 2011
The figures above show episodic poverty rates based on selected characteristics (left) and the distribution of people across those groups (right). For example, over the course of three years, 39.4 percent of unrelated individuals experienced episodic poverty (i.e., at least two months in poverty); unrelated individuals account for 15.6 percent of the total U.S. population, but 21.2 percent of those experiencing episodic poverty.
20 | P a g e
7.8
Key findings: Non-Hispanic whites had a lower episodic poverty rate (22.6%) than African Americans (45.5%) and Hispanics (45.8%). The episodic poverty rate for children under 18 (36.4%) was higher than the episodic poverty rates for adults. Adults 65 years and over had a lower episodic poverty rate (18.1%) than adults aged 18 to 64 (27.7%). The episodic poverty rate for people in female-householder families (51.8%) exceeded the episodic poverty rates for people in other types of families. People in married-couple families had the lowest episodic poverty rate (20.9%). Female-householder families make up twice the proportion of the episodically poor compared to their proportion of the overall population. And while they only make up approximately a quarter of the episodically poor they are significantly more likely to be in poverty than other types of families (Anderson, 2011).
Employment Stability As one might logically assume, poverty is highly correlated with employment. Poverty is linked to income, and work is the largest overall contributor to income, especially at low-income levels. The importance of employment can be seen in the significant impact that acquiring or losing a job, or an increase or decrease in wages, has on poverty. As mentioned previously, individuals in households that have experienced the loss of a job are the most likely to enter poverty. It is estimated that 40 percent of people who enter poverty live in a household where they or another member experienced a job loss. The proportion is even higher (49.3%) for households that have experienced a decline in earnings (Bane & Ellwood, 1986; Rynell, 2008). Three labor market problems most often hinder a workerâ&#x20AC;&#x2122;s ability to stay above the poverty line: low earnings, periods of unemployment, and involuntary part-time employment. Unemployment rates in Minnesota are typically lower than those of the nation. The annual average (not seasonally adjusted) rate for Minnesota in 2010 was 7.3 percent which is a 0.8 percentage point decrease since 2009. Minnesotaâ&#x20AC;&#x2122;s unemployment spike during the 2007-2009 recession was notably higher than the previous two recessions. Finding a job was considered moderately more difficult during the last two recessions than in normal times, but it was still much easier than it is in todayâ&#x20AC;&#x2122;s job market (Senf, 2010).
21 | P a g e
Annual Average Unemployment Rates, Not Seasonally Adjusted: 1980-2010 Percent Unemployed
Recession
MN
U.S.
12 10 8 6 4 2 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Bureau of Labor Statistics
Unemployment rates vary considerably by race, and the Great Recession was particularly difficult for African American and Hispanic/Latino population groups (Asian and American Indian data not available). The Minnesota unemployment rate for African Americans in 2009 (22.5%) was three times higher than that of whites (7.1%) and the Hispanic/Latino rate (15.5%) was two times higher.
Unemployment by Race, Minnesota 25%
White
African American
Hispanic/Latino
22.0%
20%
15%
12.3% 10%
6.4%
5%
0% 2002
2003
2004
2005
2006
2007
2008
2009
2010
Source: Bureau of Labor Statistics
Economic restructuring has led to an increasing number of permanent job separations. Nationally, in 2010, 43 percent of the total unemployed had been so for more than six months. This is the highest proportion since 1946 (Bureau of Labor Statistics, 2011). The exhaustion rate for unemployment benefits has climbed steadily over the last two decades and on average was 55.3 percent in Minnesota in 2010. This was higher than the national rate of 53.4 percent. The average number of weeks of unemployment insurance benefits followed a similar increasing pattern which averaged 20.2 weeks 22 | P a g e
during 2010 (slightly higher than the U.S., which was 18.9) (American Institute for Full Employment, 2011).
Percent Unemployed for 6 Months or Longer, U.S.: 1966-2010 Recession
50
Percent unemployed 27 weeks or longer
45 40
Percent
35 30 25 20 15 10 5 0 1966
1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
2010
Source: Bureau of Labor Statistics
An analysis of longitudinal data from the 2004 panel of the Survey of Income and Program Participation (SIPP) offers a deeper look into the unemployment patterns of various demographic groups. Over the four-year period of 2004-2007, 43 million people were impacted by unemployment nationally with an average of 1.5 spells per unemployed worker during the time period. Spell length is influenced by a number of things, such as types of jobs typically sought by members of the group, the extent and intensity of job search efforts, and the propensity to accept job offers (Palumbo, 2010). Among racial and ethnic groups, non-Hispanic whites had the shortest spells of unemployment, while spells for African Americans, Asians, and Hispanic/Latino workers were about a third longer. An analysis by age groups shows that people under age 25 tended to have shorter spells than those that were older. The shorter durations may be a result of younger workers having more current or flexible job skills as well as fewer constraints by family or financial responsibilities on job and residential mobility. Among people 21 and over the median length of unemployment spell for those with at least some college education was considerably lower than for those with less education. Those with at least some college had a median spell length which is about 35 percent shorter than someone with less than a high school diploma (Palumbo, 2010). Increasing numbers of unemployment spells, coupled with much longer spell duration, has led to increasing income volatility in households. Volatility of earnings per hour has risen more sharply than volatility in the number of hours, which indicates that the changes are increasingly more involuntary. The current economic outlook suggests that income volatility will be unusually elevated for several years (Dynan, 2010).
23 | P a g e
Having a job doesnâ&#x20AC;&#x2122;t mean you are free of poverty. More than half (51.9%) of Minnesotaâ&#x20AC;&#x2122;s poverty population in 2010 worked during the prior 12 months, and 8.3 percent worked full-time year-round. Structural economic changes have contributed to a rise in low-wage employment. As mentioned earlier in this report, workers at the lower end of the wage distribution have not fared well in recent decades (with the exception of small improvements in the latter half of the 1990s).
Work Experience* of Population in Poverty, 2010 Population in poverty (ages 16+) Worked full-time year-round Worked part-time or part-year Did not work Total
9-County Metro4 208,682 7.2% 42.1% 50.7% 100%
MN
U.S.
408,209 8.3% 43.5% 48.2% 100%
29,768,568 9.1% 34.2% 56.7% 100%
*Work experience over a 12-month period. Source: 2010 American Community Survey, three-year estimates
When the economy is in a recovery period, growth is sluggish, and employers are unsure if there will be a sustained pattern of expansion. As a result they are reluctant to add permanent positions and instead hire temporary workers. Minnesotaâ&#x20AC;&#x2122;s temporary employment services sector began cutting jobs in December of 2006, a year prior to the beginning of the recession. Overall, the temp workforce was reduced from a seasonally adjusted peak of 58,900 in late 2006 to 39,200 in September 2009 (a 33.4% decline). Since then the industry has begun to rebound, adding 6,200 jobs through July of 2010. That is about 14 percent of the 44,000 jobs that the state added through July 2010, after accounting for roughly 12 percent of the jobs lost during the recession. The temporary employment services industry accounts for a lower share of wage and salary employment in Minnesota than the nation and Minnesota ranks roughly in the middle of all states (Senf, 2010).
4
This area consists of the following counties: Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, Scott, and Washington.
24 | P a g e
Family Stability and Change Collapses in the housing and stock markets, along with a tightening of consumer credit, have eroded familiesâ&#x20AC;&#x2122; savings and assets and diminished their capacity to weather economic downturns. Regardless of how families were doing through 2007, the recession has set them back by about a decade (Acs & Nichols, 2010). For lower-income families, a distinguishing attribute of their economic success lies in their ability to work full-time year-round. With monthly unemployment rates that have at times exceeded 10 percent and the ranks of the long-term unemployed at all-time highs, families are being cut off from their surest path to economic security. In 2010, 2.8 percent (or 38,247) of Minnesotaâ&#x20AC;&#x2122;s families were in extreme poverty, 7 percent (or 94,947) were at or below poverty, and 19.5 percent (or 264,138) had incomes below 200 percent of the poverty guidelines. While there has been some slow and steady real economic growth, family incomes have become increasingly volatile. More than 13 percent of families with children experience a drop in income of at least 50 percent over the course of a year. For some this is a short-term loss, but for 3 out of 5 of these families their income fails to recover to its prior level within a year. The poorest and the richest families are more likely to experience losses than middle-income families (Acs, Loprest, & Nichols, 2009).
Probability of Experiencing a Substantial Income Drop by Income Quintile
9.9%
% All families in poverty
% Married-couple families in poverty
57.6% 30.2% 36.9% 42.7%
43.8% 21.5%
16.4% 12.6% 9.0% 8.2% 14.2%
2.5%
12.5% 18.3% 23.4%
30.5% 35.6% 5.2%
African American Asian Hispanic/Latino
% Female-headed families in poverty
Source: 2010 American Community Survey, three-year estimates
25 | P a g e
10.2%
12.0%
Poorest Quintile Quintile Quintile Richest 4 3 2
Families in Poverty, MN 2010 White American Indian Other/2+
20.2%
16.4%
13.6%
All
Source: Acs, Loprest, & Nichols, 2009
Household composition factors such as having children, teen parenthood, marital status, and female-headed households are highly correlated with income and poverty. Generally speaking, both nationally and locally households headed by women are far more likely to be poor than other types of households. Poverty rates in femaleheaded households are typically 3-4 times higher than for the overall population; this is most commonly attributed to lower wages paid to women, fewer hours worked in households with one earner, and fewer hours available to work due to parenting responsibilities (Rynell, 2008).
Families in Poverty, 2010 9-County Metro # % All Families All families in poverty Married couple families in poverty Female-headed households in poverty Families with children under 18 All families in poverty Married couple families in poverty Female-headed households in poverty
MN #
U.S. %
#
%
47,887 15,730 26,569
6.6% 2.8% 22.9%
94,947 33,341 50,897
7.0% 8,000,664 3.1% 2,897,764 26.3% 4,285,222
10.5% 5.1% 29.2%
39,767 10,879 24,328
10.7% 4.1% 30.5%
75,790 20,474 46,668
11.5% 6,247,791 4.3% 1,870,330 34.0% 3,755,711
16.5% 7.5% 38.1%
Source: 2010 American Community Survey, 3-year estimates
A longitudinal analysis of poor households reveals that poverty rates for all families as well as those headed by females have generally declined. However, the proportion of poor families headed by females has increased dramatically. Currently, more than 50 percent of all poor families nationally are headed by females, compared to 23 percent in 1959. In 2010, this rate was 53.6 percent in Minnesota and 55.4 percent in the nine-county metro area. Female-headed families are also more sensitive to business cycles than other poor families. Declines in poverty rates are steeper for female-headed households during times of economic prosperity and poverty rates increase faster for these households during recessions (U.S. Census Bureau). 60
Poverty Rates, U.S. 1959 to 2010 50 Percent of poor families headed by a single female
Percent
40
30
Poverty rate for families with female householder
20
10 Poverty rate for families
0
1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 Source: U.S. Census
26 | P a g e
As mentioned earlier in this report, the transition from a two-parent family to a single-parent family is a leading trigger of poverty spell entry among households. This transition accounts for 59 percent of the poverty beginnings for female heads of household with children. Research has found considerable evidence that after a divorce, women and children experience a substantial financial decline while divorced menâ&#x20AC;&#x2122;s income remains stable or increases (Rynell, 2008). At the end of the last century, among female-headed households, average annual poverty rates fell while earnings rose. Concurrently, however, the incidence of poverty spells actually increased, especially among single heads of households. In other words, poverty spells were more frequent but less persistent. Generally, women entered poverty from higher positions on the income ladder. Over the last 20 years, increasingly womenâ&#x20AC;&#x2122;s economic fortunes have become more dependent upon the labor market, exposing them to greater risks of short-term income fluctuations and spells of poverty (Card & Blank, 2008). Changes in policy and larger labor market trends have led to a growing number of female-headed households that have become disconnected from the labor market. They have lost access to public assistance but are still unable to find stable employment. The main wage earners in these households are likely to face multiple barriers such as caring for someone with poor health or a disability, having a history of being a victim of domestic violence, or past or present problems with substance abuse (Blank & Kovak, 2008). The impact of a change in household composition from two parents to single parent is particularly pronounced in the economic mobility of lower-income children in these households. Among children who start in the bottom third of the income distribution, only 26 percent with divorced parents move up to the middle or top third as adults, compared with 42 percent of children born to unmarried mothers and 50 percent of children born to continuously married parents. Longitudinal research has been done as single mothers move off welfare, to better understand the characteristics that impact their prospects for long-term self-sufficiency. Findings show that 46 percent of single mothers were never in poverty, 30 percent were poor but eventually left poverty, and 24 percent were poor and stayed poor (Moore, Rangarajan, & Schochet, 2007).
27 | P a g e
Immigrants According to the 2009 American Community Survey (three-year estimates), Minnesota is home to approximately 376,000 people born outside the United States (7.1% of the total population). Twentyone percent of this population lives below the poverty level (approximately 78,000 persons). While the poverty rates of immigrants from many global regions have declined, the distribution in countries of origin of U.S. immigrants has shifted strongly towards source countries from which immigrants are typically more likely to be poor in the U.S. Research examining the relationship between immigration to the U.S. between 1970 and 2008 and the nationâ&#x20AC;&#x2122;s poverty rate finds that this is the only substantive contribution to the overall poverty rate. Recent immigrants from Latin America and Asia tend to experience higher initial poverty rates. While this may increase the overall poverty rate relative to what it would otherwise be, the effect is small and through wage growth and selective out-migration, immigrant poverty declines quickly with time in the U.S. (Raphael & Smolensky, 2009). Citizenship status also has an important correlation to poverty rates. In Minnesota, 14.3 percent of the naturalized foreign-born population is in poverty, compared to 26.2 percent of the foreign born that are not U.S. citizens (2010 American Community Survey, three-year estimates).
Percent of Foreign Born Population by Region of Birth, Minnesota 100% 90% 80% 70%
Oceania
60%
North America
50%
Africa
40%
Latin America
30%
Asia
20%
Europe
10% 0% 1970
1980
1990
2000
2008-2010
Source: U.S. Census Bureau, American Community Survey and U.S. Census Bureau, Decennial Census
Research on employment trends shows that Minnesota immigrants are slightly more likely to be employed than U.S.-born residents. Among the population ages 16 and older in Minnesota, 66.2 percent of foreign-born persons were working compared to 65.9 percent of native-born Minnesotans in the 2006 to 2008 period. The employment gap between native born and foreign born has been closing over the last decade: In 1990, 64 percent of foreign-born Minnesotans were working compared to 78 percent for native born. Length of time in the U.S. has a significant impact on employment rates: Those with five or fewer years had an employment rate of 61 percent, compared to 73 percent for those here six to 10 years, and 79 percent for those here 11 to 15 years (Minnesota Compass, 2011). National research has 28 | P a g e
found that after controlling for education, English language, and other risk factors, most immigrant groups had substantially higher chances of being employed than U.S.-born individuals. That said, those with limited English language skills experienced much higher odds of poverty and hardship. They also had lower rates of employer-provided health insurance, savings accounts, and home ownership than those that were more English proficient. This underscores again that education is the most important determinant of economic advancement regardless of race or ethnicity (Rawlings, Capps, Gentsch, & Fortuny, 2007). Research shows that overall, immigration has very little effect on the poverty rates of U.S.-born residents through labor market competition. Both national and local studies have shown that the net economic effects of immigration are positive. It should be noted, however, that not all groups benefit at the same level. Workers with higher education levels see more job opportunities associated with general population and economic growth. Workers with a high school diploma or less may suffer from a decline in wages due to higher concentrations of immigrant workers in those areas. The degree to which this has a negative impact is heavily debated. While research from more conservative sources shows a sizeable earnings disadvantage, other sources conclude that the overall impacts are negligible. This is primarily attributed to the fact that most U.S.-born resident poor households have at least one working adult with at least a high school education (Borjas, 2006; Owen, 2010; Raphael & Smolensky, 2009). Economic Mobility among Immigrants Economic mobility among immigrants is highly dependent upon legal status and length of time in the U.S. Many key characteristics contributing to poverty entry and exit shift dramatically between first- and second-generation immigrants. All of these cross-generational integration patterns are important to consider together. For example, changes in family composition such as the doubling of the divorce rate between the first and second generations typically increase the probability of entering poverty. However, other trends such as increasing educational attainment, higher English language proficiency, and continued high rates of labor force participation all have positive impacts (Fix, Zimmerman, & Passel, 2001). Among first-generation immigrants, legal status is a critical component to short- and long-term financial success. Adult unauthorized immigrants are disproportionately likely to have lower educational attainment and have jobs at the lower end of the earnings ladder. In contrast to other immigrants, if legal status does not change, undocumented immigrants do not experience the same rates of income growth the longer they live in the U.S. (Passel & Cohn, 2009). Generally speaking, across generations and regardless of legal status, it is thought that about half the economic status of one generation persists into the next. This level has remained stable over the past several decades (Borjas, 2006; Fix, Zimmerman, & Passel, 2001).
29 | P a g e
Poverty and Health Care Costs The increasing cost of health care through the growth of health insurance premiums and out-of-pocket costs, as well as increased difficulty in obtaining health insurance coverage in the United States, has led to more and more households struggling to meet their needs because of medical debt. More than 6 percent of people (291,000 individuals) in Minnesota spent more than 25 percent of their pretax income on health care costs during 2009, nearly double the percentage in 2000 (3.2%) (Families USA, 2009). An estimated 64.4 million people under age 65 (nearly 1 in 4 nonelderly Americans) are in families that spend more than 10 percent of their pretax income on health care. Even more startling, nearly 4 out of 5 of these families have health insurance. More than 18.7 million nonelderly people are in families that spend more than 25 percent of their income on health care, and more than three-quarters of this group has health insurance (Families USA, 2009). As health care costs and medical debt continue to consume a growing share of budgets, many families have no choice except bankruptcy. Between 2005 and 2007 alone, 5 million families in the U.S. filed for bankruptcy following a serious medical problem. Economists estimate that 16 times as many families are on the brink of filing for bankruptcy due to medical expenses (Families USA, 2009). Medical debt is now the leading cause of personal bankruptcy and is a significant trigger into poverty. In 2007, 62 percent of bankruptcies were medical, a 50 percent increase from 2001. Medical debt is not limited to low-income or uninsured households, either: Most individuals declaring medical bankruptcy had health insurance (75%), attended college (62%), and are homeowners (52%) (Himmelstein, Thorne, Warren, & Woolhandler, 2009). Among those with medical debt, significant trade-offs are often made which impact basic needs. Nearly 1 in 3 had been unable to pay for their basic necessities like food, heat, or rent; 39 percent had used their savings to pay bills; and 30 percent took on credit card debt (Collins, Kriss, Doty, & Rustgi, 2008). Results from surveys of people filing tax returns at VITA sites show that more than one-quarter of respondents with medical debt reported housing problems. These problems included an inability to qualify for a mortgage, make rent or mortgage payments, and being turned down from renting a home (Seifert, 2005).
Medical Bill Problems and Accrued Medical Debt, U.S. Percent of Adults Ages 19-64 In the past 12 months: Had problems paying or unable to pay medical bills Contacted by collection agency for unpaid medical bills Had to change way of life to pay bills Any of the above problems Medical bills being paid off over time Any bill problems or medical debt Source: Collins, Kriss, Doty, & Rustgi, 2008
30 | P a g e
2005
2007
23% (39 mil) 13% (22 mil) 14% (24 mil) 28% (48 mil) 21% (37 mil) 34% (58 mil)
27% (48 mil) 16% (28mil) 18% (32 mil) 33% (59 mil) 28% (49 mil) 41% (72 mil)
The increase in health insurance premiums has forced employers to make tough decisions about the coverage they offer. Some have chosen to drop coverage completely (especially small businesses); others increase the share of premium that employees must pay; and many are offering insurance that covers less and/or requires higher out-of-pocket costs. The culmination of these trends means that families are shouldering ever-increasing amounts of health care costs themselves. Health insurance premiums for both single and family coverage more than doubled between 1996 and 2009. For family coverage, the average premium in Minnesota was $5,067 in 1996-1997 and $13,424 in 2008-2009, an increase of 165 percent (MDH Health Economics Program). Lack of Insurance The unstable job market and current high rates of unemployment mean frequent lapses in health coverage for millions of Americans. With more than 50 percent of people in Minnesota receiving health care coverage through their jobs (or a family memberâ&#x20AC;&#x2122;s job), changes in employment rates have strong correlations with lack of coverage as well as with medical debt. For every percentage point increase in the seasonally adjusted unemployment rate, the percentage of uninsured working-age adults is estimated to grow by 0.59 percentage points (Families USA, 2009). Approximately 7 percent of Minnesotaâ&#x20AC;&#x2122;s population was uninsured in 2009. Among those that are uninsured and had annual incomes below 200 percent of the federal poverty guidelines, just under onethird had been offered insurance by their employer (32%). With the increasing premiums among the many financial stresses a family in poverty faces, the take-up rate for employer-offered insurance has been steadily declining. The table below shows the potential sources of health insurance coverage for the low-income uninsured (MDH Health Economics Program).
Potential Sources of Coverage for Low-income Uninsured, Minnesota Employer offer* Employer eligible** Potentially public eligible Not eligible for employer or public
2001 35.7% 23.6% 82.5% 4.8%
Source: MDH Health Economics Program *Connection to employer that offers coverage **Among people with a connection to an employer offering coverage.
31 | P a g e
2004 34.8% 17.2% 88.3% 3.2%
2007 40.2% 23.4% 79.6% 6.2%
2009 31.9% 15.5% 89.2% 2.2%
Sources of health insurance vary significantly by income. While only 26 percent of low-income (at or below 200% poverty) Minnesotans had group coverage, the rate was much higher for higher income (above 200% poverty) Minnesotans, at 69 percent (MDH Health Economics Program).
100%
Sources of Health Insurance Coverage, MN 2009 17.0%
90% 80%
6.0%
19.1% 5.6%
70% 60%
53.4%
50% 40% 30%
3.6%
Uninsured Public
69.2%
Individual Group
20%
26.0% Among those with insurance, those 10% with medical debt are similar in 0% At or below 200% poverty Above 200% poverty many ways to the uninsured. Compared to those with coverage, Source: MDH Health Economics Program those who have medical debt were more than three times as likely to have skipped a recommended test or treatment due to its cost. They were more than twice as likely to have neglected to fill a drug prescription due to cost, and were four times as likely to postpone care due to cost (Hoffman, Rowland, & Hamel, 2005).
Health Care Choices Among Insured and Uninsured, U.S. 30% 29% 25%
24% 25%
28%
27%
30% 29%
Insured: no medical debt Insured: with medical debt 8%
9% 6%
Uninsured part-year Uninsured full-year
Percent skipping test/treatment due to cost
Percent not filling a Percent postponing care prescription due to cost due to cost
Source: Hoffman, Rowland, & Hamel, 2005
32 | P a g e
GREATER TWIN CITIES UNITED WAY F A C E S O F P O V E R T Y
2 0 1 2
C H R O N I C / I N T E R G E N E R A T I O N A L
P O V E R T Y
Chronic and Intergenerational Poverty Chronic or long-term poverty refers to people who are enmeshed in poverty and unable to rise above the poverty line. According to the most recently released Survey of Income and Program Participation data (March 2011), nearly one-quarter (23.1%) of the population in poverty is chronically poor (Anderson, 2011). This means they were in poverty for the entire monitored period (2004-2006). While local data is not available, if national trends apply, this would roughly equate to an estimated 120,000 people in chronic poverty in Minnesota. The figures below show chronic poverty rates based on selected characteristics (left) and the distribution of people across those groups (right). For example, over the course of three years, 9.7 percent of female-householder families experienced chronic poverty (i.e., in poverty for the entire three-year period). Female-householder families represent 14.4 percent of U.S. households, but 49.9 percent of households experiencing chronic poverty.
Chronic Poverty Rates Unrelated individuals Male-householder fam.
65 and over 18 to 64
9.7%
Population
1.9%
Chronically Poor
4.8%
8.4%
1.9%
15.6
14.4 4.1
White alone
49.9 18 to 64 years
26.1
3.8 17.0 65 years and over
63.0 44.9
11.0 43.3
African American alone
Population Chronically Poor
65.9
29.2
Under 18 years
3.0%
1.4%
Population Chronically Poor
0.7%
African American alone
White alone
Female-householder families Married-couple families
2.6%
Under 18
White alone, non-Hispanic
Unrelated individuals Male-householder families
5.2%
Female-householder fam. Married-couple families
Distribution of People
Other race groups
80.7 54.5
11.8
12.5 6.8 37.6
Source: U.S. Census Bureau, Survey of Income and Program Participation, 2004-2006 Panel; Anderson, 2011
Key findings: As was the case with episodic poverty rates, children, African Americans, and female-householder families have the highest chronic poverty rates. Unlike the patterns found in episodic poverty, the chronic poverty rate for adults 18 to 64 is lower than the rate for adults 65 years and older. While children make up 26 percent of the total population, they represent around 45 percent of those who are chronically poor. Similarly, while African Americans make up only 12.5 percent of the population, their proportion of the chronically poor is three times higher (37.6%).
34 | P a g e
7.9
Among those in poverty at the beginning of the study, 38 percent of seniors (65+), and 30 percent of female-householder families were chronically poor (Anderson, 2011).
Intergenerational Income Mobility “For more than two centuries, economic opportunity and the prospect of upward mobility have formed the bedrock upon which the American story has been anchored“ (Sawhill, 2008, p.1). Broadly speaking, intergenerational mobility is the term used to describe the ability of people to move up or down the economic ladder between one generation and the next. Important components of this include income mobility, social mobility, and wealth mobility. Economic mobility across generations is a result of both absolute mobility, economic growth boosting everyone’s incomes, and relative mobility, taking into account income inequality. Economic growth is an important source of mobility and a growing economy ensures that each generation has a greater chance of being better off than the previous one. Between 1947 and 1973, the growth rate of a typical family’s income was unusually robust, roughly doubling in a generation’s time. However, over the last four decades, the level of connection between people’s current income and occupation, and those of their parents, has been much smaller, at roughly 20 percent. The tide lifting all boats has weakened and in turn the improvements for the current generation were not able to keep pace with those that their parents and grandparents experienced. This has been particularly felt among men in their 30s today (Beller & Hout, 2006; Isaacs, 2008a; Sawhill, 2008b). Despite this, family incomes have continued to rise, although slowly. The main reason attributed to this has been the increase in women’s education and workforce participation levels; it is generally no longer the case that a typical family can depend on a single earner to move them up the economic ladder. Research has now shown that the growth of the two-earner family has been the primary factor that has saved the typical family from downward mobility (Blank, 2011; Isaacs, 2008a; Sawhill, 2008a). So how much opportunity exists for economic mobility? What are the chances of today’s rich becoming tomorrow’s poor and vice versa? These questions about relative mobility are particularly critical during periods such as this when inequalities in income and wealth are on the rise. As inequality has grown, the ability of economic growth to make each generation better off than the next has weakened (Sawhill, 2008a). “All Americans do not have an equal shot at getting ahead, and one’s chances are largely dependent on one’s parents’ economic position” (Isaacs, 2008a, p.19). The chart on page 36 shows the probability of transitioning from one income group to another over a generation. Generally speaking, there is what is referred to as a “stickiness” in the mobility distribution: The tendency of children’s incomes to look like that of their parents’ is strongest at the top and bottom of the income distribution. In other words, children born into poverty have a much higher chance of living in chronic poverty than children born into higher-income households. Forty-two percent of children born to families with incomes in the bottom fifth remain in the bottom fifth, and another 23 percent end up in the second quintile which is still low-income. Only 17 percent of those born to parents in the bottom quintile climb to one of the top two income groups (Beller & Hout, 2006; Isaacs, 2008a).
35 | P a g e
Children's Chances of Getting Ahead or Falling Behind, by Parents' Family Income Percent of Adult Children in Each Family Income Group
100% 90% 80% 70%
6% 11% 19%
60% 50%
23%
40%
19%
18% 24% 23% 24%
42% 25%
10%
17%
0%
Bottom Quintile
26%
Percent adult children with income in top quintile
39%
17%
23%
30% 20%
10%
Second Quintile
Middle Quintile
32%
Percent adult children with income in fourth quintile
23%
Percent adult children with income in middle quintile
19%
14%
Percent adult children with income in second quintile
15%
15%
8%
9%
Fourth Quintile
Top Quintile
Percent adult children with income in bottom quintile
Parents' Family Income Group Source: Brookings tabulations of PSID data on family income over several years and reported in 2006 dollars. Isaacs, 2008a.
It is important to also think of mobility in terms of gaining higher incomes (absolute mobility) as well as rising in society (relative mobility). Research done at Brookings combining both absolute and relative mobility shows that about a third of Americans are â&#x20AC;&#x153;riding the tide,â&#x20AC;? meaning they experience an increase in real income over their parents without actually moving up in relative standing. In addition, another third are actually downwardly mobile in both family income and relative rank (Isaacs, 2008a). While research is limited, studies comparing differences in economic mobility between white and African American families show important differences. The data reveals significant disparities in the extent to which parents are able to pass their economic advantages on to their children. For every parental income grouping, white children are more likely than African American children to move ahead of their parentsâ&#x20AC;&#x2122; income ranking, while African American children are more likely to fall behind. An estimated 45 percent of African American children, whose parents were solidly middle-income, end up falling to the bottom income quintile compared to only 16 percent of white children. Among those who start at the bottom of the income distribution, 54 percent of African American children remain there, compared to 31 percent of white children. Economic success in the parental generation, as measured by family income, does not appear to protect African American children from future economic hardship in the same way that it protects white children (Isaacs, 2008b).
Asset Poverty and Wealth Mobility While income represents the flow of resources earned in a particular time period, wealth and assets are a pool of money generally used for improving life, increasing opportunities, and passing along to the next generation. In other words, wealth is special in that it is utilized to launch social mobility. Two families with similar incomes but different wealth most likely do not share similar life trajectories (Shapiro, 2006). 36 | P a g e
The persistence of wealth across generations is even stronger than the persistence of income. Wealth mobility is particularly important because its distribution is more unequal than income. It also has greater effects on other aspects of well-being such as investment in childrenâ&#x20AC;&#x2122;s education and homeownership. Family inheritances, especially financial resources, are the primary way that class and race advantages and disadvantages are passed from one generation to the next. The wealth gap will continue to grow; not only does it persist between generations, it mushrooms. The probability of wealth mobility is similar to that of income mobility, with ties being strongest at the highest and lowest ends.
Intergenerational Wealth Mobility, 1979-2000 Origin quintile Poorest Second Third Fourth Richest
Poorest 45 24 11 7 5
Second 27 35 20 11 6
Destination quintile Third Fourth 11 9 20 14 35 21 23 33 9 25
Richest 9 7 13 25 55
Total 100 100 100 100 100
Source: Beller & Hout, 2006
Research conducted on the differences between Probability of Child Having the Same 55% white and African American wealth mobility show Wealth Quintile as Parents, that whites are about one and a half times more 45% 1979-2000 likely to come from families with assets and are 35% 35% 33% three and a half times more likely to receive an inheritance than African Americans. Even this does not represent the full measure of inequality, because for white families the average inheritance amounted to $52,430, while for African Americans Poorest Second Third Fourth Richest it was $21,796. Median amounts were $10,000 for white families and $798 for black families. In other Source: Beller & Hout, 2006 words, among those who receive a bequest, African Americans receive 8 cents of inheritance for every dollar inherited by whites (Shapiro, 2004). The three main channels through which intergenerational wealth impacts mobility are increased educational investments, neighborhood choice and homeownership, and expanded occupational choice. An often overlooked characteristic of inherited wealth is that it doesnâ&#x20AC;&#x2122;t just occur when parents die. Wealth transfers occur throughout the life span and this is particularly apparent when looking at spending on education. Families with greater wealth are better able to finance education investments in their children (Shapiro, 2004). Education, in some respects, has been found to level the playing field: Research has found that college graduates have mobility that no longer depends on family background. While this may be the case, it should also be noted that in many cases obtaining a college degree is very dependent on class or family income (Barnett & Belfield, 2006).
37 | P a g e
Differences in wealth also affect neighborhood choices and first-time homeownership. Home equity is estimated to account for 60 percent of the total wealth of Americaâ&#x20AC;&#x2122;s middle class. For many, this story seems as though it results primarily from individual hard work, discipline, and savings. However, for most, it is in large part also assisted by a series of federal policies that helped create a mortgage market with long-term, low-interest loans, with relatively small down payments (mostly administered through the Federal Housing Administration, Veterans Administration, and the GI Bill). While these policies succeeded in many ways at anchoring the white middle class in homeownership, the same policies also contributed heavily to residential segregation and blocked the path to homeownership, the establishment of a positive credit history, and wealth accumulation for African Americans through redlining and discriminatory lending practices. More recently, the subprime lending market emerged to target prospective buyers with blemished credit or high levels of debt. In return for the riskier investments, financial institutions charge higher interest rates, prepayment penalties, and often include balloon payments, adjustable interest rates, and higher processing and closing fees (Shapiro, 2006). The impact that neighborhoods have on current and future economic prosperity is both highly correlated and well-researched. For instance, for children whose family income is in the top three quintiles, spending childhood in a high-poverty neighborhood (vs. a low-poverty neighborhood) raises the chances of downward mobility by 52 percent. Neighborhood poverty also explains one-quarter to one-third of the African American-white gap in downward mobility. A change in neighborhood also has an impact on economic success. African American children who lived in neighborhoods that saw a decline in poverty of 10 percentage points in the 1980s had annual adult incomes almost $7,000 greater than those who grew up in neighborhoods where the poverty rate was stable (Sharkey, 2009). Finally, wealth may expand occupational choice. This particularly impacts level of self-employment. People with inheritances are twice as likely to become self-employed, most likely because of access to significant start-up capital. Self-employed individuals are much more likely to experience upward mobility within the wealth distribution. Research also shows that this may further extend into the third generation, as children of entrepreneurs are twice as likely as children in general to be self-employed (Grawe, 2008).
38 | P a g e
Children in Poverty Children are the most likely age group to live Poverty Rates by Age, MN 2010 in poverty, and growing up in poverty can Below 100% Poverty Below 200% Poverty have devastating long-term consequences. 36.0% More than one-third of Minnesotaâ&#x20AC;&#x2122;s children 30.7% 30.0% under age 5 live in households with incomes 23.2% below 200 percent of poverty (46.0% nationally, and 33.5% in the nine-county 16.1% metro area), and 16.1 percent are under 100 12.9% 10.3% 8.6% percent of poverty (23.1% nationally, and 15.1% in the nine-county metro area). The Great Recession has had a negative impact on children in poverty, increasing both the 0 to 5 6 to 17 18 to 64 65+ Source: 2010 American Community Survey, three-year estimates number of children in poverty and the child poverty rate. Of even greater concern is the increase in the number and percentage of children living in extreme poverty (households with incomes below 50% of the federal poverty level). In 2010, 6.1 percent (or 76,664) of Minnesotaâ&#x20AC;&#x2122;s children under 18 were living in extreme poverty, a rate that is still well below the nation (8.8 percent, or 6,437,076), but significantly higher than in 2000, when 3.9 percent of Minnesota children under 18 were in extreme poverty (7.4% for the U.S.).
Children in Poverty, 2010 U.S. Age 0 to 5 Below 100% 5,503,690 23.1% Below 200% 10,973,814 46.0% Age 6 to 11 Below 100% 4,766,164 19.9% Below 200% 10,123,929 42.2% Age 12 to 17 Below 100% 4,372,186 17.4% Below 200% 9,631,887 38.3%
MN
9 County Metro
67,286 150,751
16.1% 36.0%
35,756 79,333
15.1% 33.5%
55,716 133,900
13.4% 32.3%
32,323 71,354
13.7% 30.3%
53,188 125,838
12.4% 29.3%
31,136 66,363
12.8% 27.3%
Source: 2010 American Community Survey, three-year estimates
The negative effects of poverty are pervasive, cumulative, and increase with age (Shore, 1997). Children who are raised in poverty show a negative impact even when they are born healthy and free of medical problems. They tend to show gradual declines in mental, motor, and socio-emotional development; they have poorer quality relationships with their caregivers; and they are more likely to exhibit anxious attachment. In preschool, they are more likely to have problems getting along with other children and functioning on their own. By the time they start school they are more likely to need special education services, and as they progress through school they are more likely to be held back. 39 | P a g e
This cumulative impact of poverty and disadvantage on education outcomes is known as the achievement gap. As can be seen in the accompanying graph, higher income students (defined as above 185% of poverty) are much more likely to be proficient in 3rd-grade reading and 11th-grade math and are also more likely to graduate on time than are lower income students (at or below 185% of poverty).
2010 Percent Meeting Standards & Graduating on Time (MN) Higher Income
Lower Income
86%
84% 61% 54%
52%
22%
This achievement gap is highly correlated to parental education and 3rd-Grade Reading 11th-Grade Math On-Time family income. When these two Graduation Source: Minnesota Compass demographic variables are examined, much (though not all) of the variation between racial/ethnic groups is explained. What many people do not realize, however, is that the gap is already in place before children enter kindergarten (Fryer & Levitt, 2004, 2006). Numerous studies have found that most of the inequality in cognitive skills and differences in behavior come from family and neighborhood sources rather than from schools (Berliner, 2009). The graph below shows children of color have nearly twice the rates of poverty of whites and the difference is much larger (more than four times as high) for African American and American Indian children.
Children in Poverty by Race and Age, MN 2010 White
African American
American Indian
Asian/Pacific Islander
57.1%
Other/2+ Races
Hispanic/Latino
51.8% 44.7%
43.9%
41.3% 38.2%
38.9% 33.9% 25.5% 17.7% 11.3%
8.3%
0 to 5
25.4% 20.5%
8.3%
6 to 11
Source: 2010 American Community Survey, three-year estimates
40 | P a g e
25.5%
20.1% 20.9%
12 to 17
Evans and Schamberg (2009) provide evidence that living in poverty results in chronically elevated physiological stress, which in turn affects working memory. Working memory is essential to language comprehension, reading, and problem solving; it is a critical prerequisite for long-term storage of information. The longer the period of childhood poverty, the higher the stress load is during childhood, and the greater the long-term effect on working memory. Children growing up in poverty are, as a rule, exposed to more risk factors (e.g., substandard housing, highly segregated neighborhoods) than children growing up in middle-income households. Evans and English (2002) report that exposure to one risk factor generally has a negligible impact on children, while exposure to two or more risks has a cumulative, adverse psychological impact. Not surprisingly, the environment of poverty is characterized by exposure to cumulative, adverse, physical and social stressors. The housing is noisier, more crowded, and of lower quality. People living in poverty experience elevated levels of family turmoil, greater child-family separation, and higher levels of violence. According to Dearing (2008), the economic costs of childhood poverty in the United States could be as high as $500 billion a yearâ&#x20AC;&#x201D;about 4 percent of the U.S. GDP. The impact of poverty manifests in many ways, including the avenues of prenatal care, health care, food insecurity, environmental concerns (e.g., lead paint), family relations and family stress, and neighborhood characteristics. See Berliner (2009) for a detailed discussion of the impact of poverty on children in each of these areas. Persistent Childhood Poverty A strong predictor of future economic success is poverty status at birth. Children who are born into poverty and spend multiple years living in poor families have worse adult outcomes than their counterparts in higher-income families. Understanding the dynamics of persistent childhood poverty is particularly important, as the cumulative effect of being poor may lead to numerous negative outcomes and limited opportunities that can have ripple effects for generations. To put into context the importance of understanding the dynamics of childhood poverty, compare the fact that in 2008, 34.7 percent of African American children lived below the poverty threshold. Yet more than twice as many (77%) are poor at some point during their childhoods, and 37 percent are persistently poor (Ratcliffe & McKernan, 2010). Research shows that approximately 13 percent of children in the United States are poor at birth. This rate varies greatly by race: 8 percent of white children compared to 40 percent of African American children. Further, very few children who are poor for multiple years have a single uninterrupted poverty spell. They tend to cycle into and out of poverty over time. Among children who are poor nine or more years, only 17 percent have a single uninterrupted spell, while 58 percent experience three or more poverty spells. Put another way, African American children are roughly two and a half times more likely than white children to ever be poor and seven times more likely to be persistently poor (Ratcliffe & McKernan, 2010). Much of the importance of understanding persistent childhood poverty lies in the fact that its four most highly correlated outcomes are also those that are the strongest predictors of adult poverty. First, being 41 | P a g e
born into poverty is a significant predictor of adult poverty. While 4 percent of individuals in economically stable families at birth go on to spend at least half of their early adult years living in poverty, the rate for individuals born into poverty is 21 percent. Second, the likelihood of not completing high school is three times greater for individuals who are poor versus not poor at birth. While 7 percent of individuals born in economically stable households lack high school diplomas, rates are much higher (22%) for those born in into poor households. Third, the likelihood of having a teen nonmarital birth is three times as likely for women who are poor versus not poor at birth (31% versus 10%). Fourth, among certain demographic groups, poverty at birth is a predictor of being consistently employed as a young adult. For men in general, there is no statistically significant difference; however, upon closer look at the data by race, we see that African American males born into poverty are 33 percentage points less likely to be consistently employed than those not poor at birth. There is a similar pattern for African American females, although the differences are not as large (Ratcliffe & McKernan, 2010).
Hard-To-Serve Singles Hard-to-serve singles represent a small percentage of people in poverty but they tend to account for a significant portion of the expenses associated with poverty. Hard-to-serve singles generally experience issues that either exacerbate or are exacerbated by poverty, such as mental illness (including Posttraumatic Stress Disorder or PTSD), substance abuse and addiction, prison records, and domestic violence. Many hard-to-serve singles are dealing with several of these challenges simultaneously, and difficulties are often compounded by chronic homelessness, the most extreme form of poverty. The 2009 Wilder Homeless Study (2010b) found high co-occurring levels of serious mental illness, chronic health conditions, and substance abuse disorders among homeless adults. Only 1 in 4 homeless individuals (26%) reported none of the three disabilities. 11 percent experienced serious mental illness, chronic health conditions, and substance abuse disorders. 19 percent experienced chronic health conditions and mental illness. 8 percent experienced substance abuse disorders and serious mental illness. 2 percent experienced substance abuse disorders and chronic health conditions. Veterans More than 400,000 Minnesotans have served in the military, approximately 10 percent of the adult population. Veterans overall tend to have higher incomes than nonveterans (31% higher in 2007). Veterans also have lower poverty rates than the nonveteran population: 4.1 percent of veterans live at or below the poverty level, compared to 9.2 percent of nonveterans (Vilsack, 2009). Veterans are much more likely than nonveterans to have a disability. For veterans at or below the poverty level, 44 percent have a disability compared to 30 percent of the nonveteran poverty population. Of the nonpoverty population, 21 percent of veterans have a disability compared to 13 percent of nonveterans (Vilsack, 2009). While veterans are less likely to live in poverty overall, they represent a distinct subset of the chronic homeless population. Chronically homeless individuals, in addition to living below the poverty level, 42 | P a g e
often suffer from additional problems such as mental illness (including PTSD) and substance abuse disorders. In Minnesota, 11 percent of homeless adults counted in the 2009 homeless survey were veterans (Wilder Research, 2010a), mirroring their presence in the statewide population. While the large majority of homeless veterans are male, the homeless female veteran population increased significantly between 2006 and 2009, from 29 to 64, an increase of 120 percent. Nearly half of homeless vets are people of color (46%) and the overrepresentation is particularly strong among African Americans and American Indians. Nearly two-thirds of Minnesota’s homeless vets (63%) are long-term homeless (homeless a year or longer or homeless four or more times in the last three years). Two-thirds (67%) had experienced at least one institutional or treatment program such as a drug or alcohol treatment facility, a halfway house, a mental health treatment facility, a group home, or a foster home. More than half (59%) had been in a correctional facility (Wilder, 2010a). Many homeless veterans face multiple challenges: 44 percent report a service-related health problem. 43 percent have at least one chronic medical condition. 57 percent have serious mental health problems (e.g., schizophrenia, bipolar disorder, major depression, PTSD). 45 percent are alcoholic or chemically dependent. 23 percent have a dual diagnosis of mental illness and chemical dependency. 54 percent have a physical, mental, or other health condition that limits the amount or type of work they can do. 84 percent have at least one serious or chronic disability. Ex-Inmates About 2.3 million Americans are behind bars—more than 1 in 100 adults. This is an increase of over 300 percent from 1980, giving the United States the highest rate of incarceration in the world. Prior to incarceration, more than two-thirds of male inmates were employed, and more than half were the primary source of financial support for their children (Pew Charitable Trusts, 2010). In 2008, about 1 in 33 working-age adults was an ex-prisoner and about 1 in 15 was an ex-felon. Looking solely at men, about 1 in 17 was an ex-prisoner and about 1 in 8 was an ex-felon (Pew Charitable Trusts, 2010; Schmitt & Warner, 2010).
People with Mental Disorders Approximately 6 percent of the U.S. population suffers from a serious mental disorder (National Institute of Mental Health, 2010). There is a strong, consistent, negative relationship between mental illness and socioeconomic status: The lower the socioeconomic status of an individual, the higher the risk of mental illness (Hudson, 2005). The relationship between mental illness and poverty is complex and multidirectional. In some circumstances, the stress associated with adverse circumstances, such as poverty, influences the development of mental illness (Perese, 2007). Conversely, developing a mental illness can impact ability to maintain employment, which can trigger descent into poverty. But while the relationship can and does go in both directions, research has found that socioeconomic status does impact the development of mental illness directly as well as indirectly; thus if poverty is reduced, mental illness by necessity will also be reduced. Serious mental illness is a significant cause of homelessness. It disrupts one’s ability to carry out essential aspects of daily life, such as working, self-care, and household management. The 2009 Wilder Homeless Study found that more than half (59%) of adults who are homeless for at least a year have a serious mental illness. Among youth, 46 percent report a serious mental illness. Between one-third and one-half of people with serious mental illness are at or near the poverty level (Cook, 2006).
Currently 1 in every 28 children in the United States—nearly 4 percent—has a parent in jail or prison. Twenty-five years ago, 1 in 125 had a parent in jail or prison. The numbers are even starker for African Americans: More than 10 percent of African American children have an incarcerated father and 1 percent have an incarcerated mother (Pew Charitable Trusts, 2010). Incarceration has a significant impact on earning power: Former inmates saw subsequent wages reduced by 11 percent, annual employment reduced by nine weeks, and annual earnings reduced by 40 percent (from $39,100 to $23,500; Pew Charitable Trusts, 2010). This affects not only ex-felons, but their families as well. 43 | P a g e
Bruce Western (2002) characterizes incarceration as a key life event that triggers a cumulative spiral of disadvantage: It reduces not just the level of wages but also the rate of wage growth over the life course. Incarceration reduces access to the steady job market: Employers are less likely to hire exoffenders than comparable job applicants without criminal records. Incarceration also erodes job skills and may exacerbate pre-existing mental or physical illness which also impact the likelihood of finding gainful employment. As a result, ex-inmates tend to follow the low-wage trajectories common among day laborers and other contingent workers. In general, career jobs are inaccessible to ex-offenders (Western, 2002). To further exacerbate their situation, many felons emerge from prison not only with a criminal record but also substantial debt (Harris, Evans, & Beckett, 2010). Monetary sanctions are imposed on a substantial majority of people convicted of crimes. More than three-quarters (80%) of individuals on probation have fines, fees, and/or restitution orders imposed on them, and two-thirds (66%) of prison inmates were assessed monetary sanctions by the courts in 2004 (up from 25% in 1991). This debt further reduces income and adds an additional challenge to obtaining a living-wage job. Minnesota numbers are higher than the national average: 78 percent of prison inmates experienced court-imposed monetary sanctions (Harris, Evans, & Beckett, 2010). These monetary sanctions have a significant and long-term impact: It will take more than a decade for an ex-inmate paying $100/month to pay off his debt. Those who make payments of $50/month will remain in arrears 30 years later. While poverty estimates are not available for ex-prisoners, this is an important population to keep in mind when addressing poverty, as the proportion of ex-offenders in the working-age population is expected to increase substantially in coming decades. According to Wilder Research (2010b), the proportion of people experiencing homelessness who are ex-offenders has been on the increase for a decade. In 2009, 63 percent of homeless adult men and 28 percent of homeless adult women had been incarcerated at least once. To get an idea of the scope of future incarceration, the Bureau of Justice Statistics has estimated that 11.3 percent of males born in 2001 will be imprisoned at some point during their lifetime compared to just 3.6 percent of those born in 1974. These higher imprisonment rates will result in large increases in the ex-offender population over time (Schmitt & Warner, 2010). Victims of Domestic Violence Domestic violence is a significant contributor to poverty and homelessness, most particularly for women. Women seeking to leave abusive partners often report economic concerns as a major barrier (Postmus, 2010). Numerous studies have found that between 25 and 50 percent of homeless women are homeless because of domestic violence. In the 2009 Wilder Homeless study, 29 percent of homeless women indicated domestic violence was a primary reason for their homelessness (Wilder, 2010b). Poverty limits womenâ&#x20AC;&#x2122;s choices and makes it harder for them to escape violent relationships. While women of all income levels experience domestic violence, low-income women experience domestic violence at higher rates than middle- and upper-income women (Buskovick & Peterson, 2009).
44 | P a g e
Seniors Nearly 1 in 10 adults age 65+ lives in poverty and more than 1 in 3 (36%) live below 200 percent of the federal poverty level (Oâ&#x20AC;&#x2122;Brien, Wu, & Baer, 2010). It should be noted that poverty thresholds5 for seniors are higher than for the rest of the population, based on an assumption that elders need less cash income (and less food) to meet basic needs than do younger adults. For example, in 2010, the poverty threshold for a single person under 65 years of age was $11,344 while for a single person age 65 or older it was 8 percent lower, or $10,458 (Gerontology Institute, 2009; U.S. Census Bureau, 2011). The method used to measure poverty makes a large difference when calculating elder poverty rates. Alternative poverty measures that account for out-of-pocket health care costs (a major expense for elders that increases with age) indicate poverty rates 17 to 89 percent higher than the official rate, depending on subgroup (Butrica, Murphy, & Zedlewski, 2008). Significant progress has been made in addressing elder poverty in the last 50 years. In the 1960s, poverty rates for elders stood at 25 percent. Poverty declined throughout the 1970s as Social Security benefits expanded. The elder poverty rate stabilized at about 10 percent in the late 1990s, and has remained at or near that level since.
Percent of Population 65+ in Poverty, U.S. 25
Percent
20 15 10 5 0 1970
1974
Source: U.S. Census Bureau
5
1978
1982
1986
1990
1994
Recession
1998
2002
2006
2010
People 65+ Poverty
Federal poverty thresholds are different from federal poverty guidelines. The poverty thresholds are updated each year by the U.S. Census Bureau and vary by family size and age of members. The poverty guidelines are based on the poverty thresholds and are updated each year by the U.S. Department of Health and Human Services. The guidelines are a simplification of the thresholds and are used primarily for administrative purposes, such as determining eligibility for certain federal programs.
45 | P a g e
Elder Poverty Rates with and without Social Security (U.S., 2008)
All 65+
45.2%
49.7%
Women 65+
9.7%
11.9%
With Social Security
Without Social Security
Source: Van de Water & Sherman, 2010
Social Security continues to play a significant role in alleviating poverty among elders: More than 30 percent of our elder population is lifted out of poverty via Social Security (Johnson & Mermin, 2009; Van de Water & Sherman, 2010). Poverty is not evenly distributed among elders, and some subgroups are significantly more likely to struggle to meet their basic needs than others: Elders of color, particularly blacks (20%) and Hispanics/Latinos (19%) Elder women (12%) Widows (14%) Elder women of color, particularly blacks (24%) and Hispanics/Latinos (22%) Elders with less than a high school diploma (19%) Elders who never married (18%) or are divorced or separated (17%) Elders living alone (17%) Those age 85+ (13%) Noncitizens (21%)
According to the Elder Economic Security Standard Index (Elder Index) for Minnesota: For single elders in good health, the statewide Minnesota Elder Index (the amount of money required to meet basic needs in Minnesota) is $16,767 for homeowners without a mortgage or $19,090 for renters and homeowners with a mortgage. The average Social Security benefit for Minnesota elders is $13,059 per year for an individual. For elder couples in good health, the statewide Minnesota Elder Index is $26,486 for homeowners without a mortgage or $28,809 for renters and homeowners with a mortgage. The federal poverty guideline is $14,000 per year for elder couples. This is only 53 percent of the Elder Index for homeowners without a mortgage or 49 percent for renters and homeowners with a mortgage. The average Social Security benefit for Minnesota couples is estimated to be $21,143 per year. This represents 80 percent of the statewide Elder Index for homeowners without a mortgage or 74 percent for renters and homeowners with a mortgage. Source: The Elder Economic Security Standard Index for Minnesota, Gerontology Institute, University of Massachusetts Boston, 2009.
Older women are more likely to be poor than older men because of fewer economic resources due to lower wages, lower lifetime earnings, and less time in the workforce. Women also have longer life expectancies accompanied by chronic illness, and are more likely than men to experience loss of income when widowed (Gerontology Institute, 2009). Elder women in Minnesota are nearly twice as likely to live in poverty as men: 10.5 percent of Minnesota women age 65+ are at or below poverty compared to 6.2 percent of 65+ men (2010 American Community Survey, three-year estimates). Widowhood is a significant risk factor or trigger into poverty. Approximately half of women over age 65 are widows. Nearly 1 in 3 will experience poverty in any given year, and their risk of being poor at some point over a 10-year span is over 50 percent. Two leading causes of these high poverty rates are 46 | P a g e
insufficient wealth accumulation prior to widowhood and significant health care expenses immediately prior to the death of a spouse. Out-of-pocket spending averages approximately $6,000 in the last year of life, a 50 percent increase over previous years. One study found that mean family income for women before and after the death of a spouse dropped from $23,284 to $11,121 and the poverty rate jumped from 14 percent to 26 percent (Lee & Lee, 2006; McGarry & Schoeni, 2005). Seniors of color are also at greater risk: A recent study found that significant majorities of African American senior households (76%) and Hispanic/Latino senior households (85%) are at risk of having insufficient financial resources to meet median projected expenses for their projected life expectancy, based on financial net worth and projected Social Security and pension income. This is due in large part to health care costs. Health care premiums are rising disproportionately to income for seniors on fixed incomes, posing an even larger burden for economic security in the future (Meschede, Shapiro, Sullivan, & Wheary, 2010). The figure below shows that in 2010, African American and American Indian seniors had the highest poverty rates of all racial and ethnic groups, and in Minnesota and the Minneapolis/St. Paul Metropolitan Statistical Area (MSA)6 rates for these two population groups were three or more times higher than for whites and significantly higher than the national average.
Population 65+ in Poverty by Race, 2010
U.S.
20.0%
16.2%
21.1%
21.9%
30.2%
30.9%
American Indian Hispanic/Latino
6.3%
7.9%
15.3%
21.7%
19.0%
18.0%
12.8%
19.3%
19.4% 8.0%
26.1%
African American Other/2+ Races 31.9%
White Asian/Pacific Islander
MN
Minneapolis/St. Paul MSA
Source: 2010 American Community Survey, 3-year estimates
Nearly 40 percent of elders age 65+ report having a disability of some kind, while more than half (53%) of elders at or below poverty report a disability of some kind. Not surprisingly, low-income elders are much more likely to experience financial hardship due to medical expenses. While the majority of elders spend less than one-eighth of their income on health care (primarily on premiums), over one-quarter (28%) spend more than 20 percent of their income on health care, and nearly half of low-income elders
6
This area consists of the following counties: Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, Scott, Sherburne, Washington, and Wright.
47 | P a g e
(< 200% poverty) spend more than 20 percent of their income on health care. Twenty percent is a common indicator for financially burdensome health care costs (Johnson & Mommaerts, 2009). It should be noted that elders with very low incomes may qualify for Medicaid, which covers virtually all heath care costs and is much more comprehensive than Medicare. About a quarter of older adults in poverty are currently enrolled in Medicaid—only half of those eligible (Butrica, Murphy, & Zedlewski, 2008; Johnson & Mommaerts, 2009; O’Brien, Wu, & Baer, 2010). While elder households are generally less likely to be food insecure than households with children, those that are food insecure are less likely to be enrolled in the food stamp program: only 30 to 40 percent of eligible elders receive SNAP (federal food stamp program) benefits (O’Brien, Wu, & Baer, 2010). While the situation of elders currently in poverty is of concern, it should be noted that today’s elders are better prepared for retirement than upcoming generations will be. This is because subsequent generations are experiencing declining employer-based retirement savings and rising debt (Meschede, Shapiro, Sullivan, & Wheary, 2010). In general, older people are more likely to fall into poverty for a long period of time compared to younger people, and they are also less likely to escape poverty after they fall into it. In a five-year period, 24 percent of elders will experience at least one year in poverty compared to 20 percent for the younger population. Once they have fallen into poverty, the likelihood that elders will exit poverty is 35 percent compared to 40 percent for the younger population. And after three consecutive years in poverty, the likelihood of escaping poverty is substantially lower for elders, especially elder women (7.3% vs. 21.3% for younger people). Among the 65+ population who fall into poverty, 31 percent remain poor for 10 or more years compared to 11 percent for the younger population (Public Policy Institute, 2003).
Poverty Rates Before and After Disability (Ages 51-64) Before Disability
30.5%
After Disability 15.5%
17.0%
7.4%
All Adults
Single Adults
Source: Johnson, Favreault, & Mommaerts, 2010
48 | P a g e
People with Disabilities Disability has a strong intersect with aging, as disability rates increase steadily when people approach retirement: Disability rates approximately double from age 55 to age 64. Disability is particularly common among low-income individuals with little education: High school dropouts are nearly three times as likely to be disabled as college graduates. These higher disability rates are likely due to a combination of limited access to health care, higher stress levels, and, potentially, less healthy behaviors. In addition, disability rates are higher for women than for men and
higher for African Americans and Hispanics/Latinos than for non-Hispanic whites (Johnson, Favreault, & Mommaerts, 2010). One reason that the links between poverty and disability have received so little attention is that there is no official or consistent definition of disability across surveys. However, studies on the dynamics of poverty have found that changes in disability are second only to changes in employment status as a predictor of poverty entry and exit, followed by shifts to and from a female-headed household (She & Livermore, 2009). For adults who become disabled between the ages of 51 and 64, the percentage in poverty increases from 7.4 percent (prior to disability) to 15.5 percent (after disability). Poverty rates are much higher for single adults, both before disability (17%) as well as after disability onset (30.5%) (Johnson, Favreault, & Mommaerts, 2010). Poverty rates are high because many people with disabilities do not receive benefits. For those that do receive benefits, they are often not generous enough to lift them out of poverty. The connection between poverty and disability is complex and multidirectional. For working adults who become disabled, disability can be a trigger into poverty. Even if the individual is able to continue to work, people with disabilities are often excluded from the labor market because of fears of increased costs and the potential need for accommodations. Poverty can also contribute to the likelihood of disability, as people in poverty often have less access to health care in general and preventive care in particular. People living in poverty are also exposed to risk factors that increase the likelihood of impairment and disability, including insufficient nutrition and substandard and crowded housing (Kessler Foundation, 2010). Disability rates in Minnesota are below the national average: Nationally, 10 percent of the working age population (ages 21-64) has some type of disability compared to 8 percent in Minnesota. Likelihood of disability increases with age. Children under age 5 are the least likely to experience disability (< 1%). For children ages 5 to 15 the prevalence increases to 5 percent. Moving up the age spectrum, 27 percent of those ages 65 to 74 have some type of disability, as do 52 percent of those age 75+ (Erickson & von Schrader, 2010). The poverty rate for working-age people with disabilities is significantly higher than that of the nondisabled population: 25 percent compared to about 10 percent nationally. Poverty rates vary by type of disability.7 Among the working-age population, poverty levels are highest for individuals with cognitive disabilities (32% in poverty), independent living disabilities (31%), and self-care disabilities (31%). However, high poverty rates are also seen for people with visual and ambulatory disabilities (28% each) and hearing disabilities (18%) (Erickson & von Schrader, 2010). Long-term poverty rates among people with disabilities are much higher than short-term poverty rates. People with disabilities represented 47 percent of those in poverty according to a short-term measure but 65 percent of those in poverty according to a long-term measure (She & Livermore, 2009).
7
The Census Bureau defines six types of disability: visual, hearing, ambulatory, cognitive, self-care, and independent living (Erickson & von Schrader, 2010).
49 | P a g e
GREATER TWIN CITIES UNITED WAY F A C E S O F P O V E R T Y C O N C L U S I O N
2 0 1 2
Conclusion Poverty is a growing part of the American experience. Structural changes in the economy make it increasingly likely that the average American will spend a year or more in poverty at some point in his or her life. Economic factors that contribute to the increase in situational poverty include job loss, flat and declining wages, more low-paying service jobs and fewer high-paying manufacturing jobs, a decline in unions, and the increasingly common experience of significant medical debt. Between the ages of 20 and 75, more than half the population (59%) will have experienced at least one year in poverty and twothirds (65%) will at some point reside in a household that receives a means-tested welfare program such as food stamps, SSI, Medicaid, Temporary Assistance for Needy Families (TANF), or some other cash assistance (Rank, 2007). Most spells of poverty are relatively short. Typically, households are in poverty for a year or two and then come out the other side. However, an increasing segment of the poverty populationâ&#x20AC;&#x201D;44 percent in Minnesotaâ&#x20AC;&#x201D;are in extreme poverty (half of the federal poverty guidelines, or $11,175 for a family of four). These households struggle more to emerge from poverty and are more likely to stay in poverty for extended periods of time. One reason for this is that they do not have the wealth, assets, and social networks available to more resourced households. This is also why populations of color are more likely to live in poverty: Because of historical disenfranchisement, they are less likely to have the accumulation of wealth, assets, and social connections to weather a poverty spell and are more likely to live in longterm, intergenerational poverty. Poverty is not one size fits all. A college student living below the poverty line and eating ramen noodles does not have the same experience as a homeless veteran with serious and persistent mental illness. A person unable to work because of a severe disability does not have the same poverty experience as a newly arrived immigrant. For children growing up in poverty, however, the risks cross all boundaries: The negative effects of poverty are pervasive, cumulative, and increase with age. Children who are raised in poverty show a negative impact on their lives, even when they are born healthy. For example, they tend to show gradual declines in mental, motor, and socio-emotional development. They have poorer quality relationships with their caregivers. In preschool, they are more likely to have problems getting along with other children. By the time they start school they are more likely to need special services and are more likely to be held back a grade (or more) as they age. They are more likely to drop out of high school and are more likely to live in poverty as adults. While there are no panaceas, education in general is one of the best antipoverty strategies known. People with higher levels of academic achievement and more years of school earn more than those with less education. Early intervention in the form of high-quality childcare and preschool can help to level the playing field. Continuing support throughout the school years and helping to connect these youth to postsecondary education will have a positive, long-term effect on lifetime earnings.
51 | P a g e
For adults, workforce development programs can be an effective pathway out of poverty. Programs that give workers a postsecondary credential, have direct ties to employers and industries with well-paying jobs, and supports and services during training and initial placement have been found to have the strongest results.
52 | P a g e
Appendix
53 | P a g e
Data Tables Poverty by Age and Race Minneapolis/ St. Paul MSA Total # poverty % poverty 0 to 5 # poverty % poverty 6 to 17 # poverty % poverty 18 to 64 # poverty % poverty 65+ # poverty % poverty Minnesota Total # poverty % poverty 0 to 5 # poverty % poverty 6 to 17 # poverty % poverty 18 to 64 # poverty % poverty 65+ # poverty % poverty
USA
Total 3,204,209 321,033 10.0% 268,000 38,591 14.4% 541,265 68,697 12.7% 2,067,544 189,206 9.2% 327,400 24,539 7.5% Total 5,155,446 565,433 11.0% 417,946 67,215 16.1% 844,527 108,883 12.9% 3,251,358 334,340 10.3% 641,615 54,995 8.6% Total
African American 229,652 77,033 33.5% 30,163 12,964 43.0% 50,838 21,309 41.9% 139,540 39,941 28.6% 9,111 2,819 30.9% African American 254,507 88,562 34.8% 33,783 14,826 43.9% 56,404 24,234 43.0% 154,610 46,409 30.0% 9,710 3,093 31.9% African American
White 2,643,103 182,771 6.9% 189,314 15,626 8.3% 408,793 29,120 7.1% 1,737,684 118,641 6.8% 307,312 19,384 6.3% White 4,464,311 385,276 8.6% 320,033 36,301 11.3% 680,598 58,450 8.6% 2,847,762 241,972 8.5% 615,918 48,553 7.9% White
Total 298,931,525 36,797,544 222,663,718 # poverty 42,931,760 9,475,042 25,988,866 % poverty 14.4% 25.7% 11.7% 0 to 5 23,860,449 3,353,036 15,996,196 # poverty 5,503,690 1,399,283 2,937,137 % poverty 23.1% 41.7% 18.4% 6 to 17 49,138,544 7,232,194 33,861,930 # poverty 9,138,350 2,428,260 4,862,968 % poverty 18.6% 33.6% 14.4% 18 to 64 187,652,919 22,995,979 140,181,847 # poverty 24,673,397 5,022,626 15,567,531 % poverty 13.1% 21.8% 11.1% 65+ 38,279,613 3,216,335 32,623,745 # poverty 3,616,323 624,873 2,621,230 % poverty 9.4% 19.4% 8.0% Source: 2010 American Community Survey (3-year estimates)
54 | P a g e
American Indian 18,924 5,753 30.4% 1,547 472 30.5% 3,894 1,600 41.1% 12,827 3,483 27.2% 656 198 30.2% American Indian 52,938 20,049 37.9% 5,810 3,008 51.8% 11,391 5,403 47.4% 33,013 10,928 33.1% 2,724 710 26.1% American Indian 2,421,062 644,423 26.6% 230,221 88,358 38.4% 485,849 151,822 31.2% 1,531,223 370,749 24.2% 173,769 33,494 19.3%
Asian/ Pac. Isl. 181,849 31,476 17.3% 21,214 4,041 19.0% 38,716 9,308 24.0% 113,711 16,331 14.4% 8,208 1,796 21.9% Asian/ Pac. Isl. 205,594 34,759 16.9% 23,416 4,144 17.7% 42,453 9,678 22.8% 130,159 18,862 14.5% 9,566 2,075 21.7% Asian/ Pac. Isl. 14,759,532 1,745,726 11.8% 1,114,994 126,453 11.3% 2,204,526 290,176 13.2% 10,076,099 1,155,080 11.5% 1,363,913 174,017 12.8%
Other/ 2+ 130,681 24,000 18.4% 25,762 5,488 21.3% 39,024 7,360 18.9% 63,782 10,810 16.9% 2,113 342 16.2% Other/ 2+ 178,096 36,787 20.7% 34,904 8,936 25.6% 53,681 11,118 20.7% 85,814 16,169 18.8% 3,697 564 15.3%
Hispanic/ Latino 169,102 37,021 21.9% 26,581 7,931 29.8% 40,267 10,182 25.3% 98,566 18,170 18.4% 3,688 738 20.0% Hispanic/ Latino 238,076 58,151 24.4% 37,833 12,823 33.9% 58,688 15,378 26.2% 136,079 28,792 21.2% 5,476 1,158 21.1%
Other/ 2+
Hispanic/ Latino
22,289,669 5,077,703 22.8% 3,166,002 952,459 30.1% 5,354,045 1,405,124 26.2% 12,867,771 2,557,411 19.9% 901,851 162,709 18.0%
48,267,138 11,259,201 23.3% 5,925,798 1,975,526 33.3% 10,595,767 3,057,160 28.9% 29,130,873 5,730,086 19.7% 2,614,700 496,429 19.0%
Children in Poverty by Age and Race Minneapolis/ St. Paul MSA 0 to 17 # poverty % poverty 0 to 5 # poverty % poverty 6 to 11 # poverty % poverty 12 to 17 # poverty % poverty
Minnesota 0 to 17 # poverty % poverty 0 to 5 # poverty % poverty 6 to 11 # poverty % poverty 12 to 17 # poverty % poverty
USA
Total 809,265 107,288 13.3% 268,000 38,591 14.4% 268,265 35,001 13.0% 273,000 33,696 12.3% Total 1,262,473 176,098 13.9% 417,946 67,215 16.1% 414,472 55,716 13.4% 430,055 53,167 12.4% Total
African American 81,001 34,273 42.3% 30,163 12,964 43.0% 24,988 10,874 43.5% 25,850 10,435 40.4% African American 90,187 39,060 43.3% 33,783 14,826 43.9% 27,896 12,472 44.7% 28,508 11,762 41.3% African American
White 598,107 44,746 7.5% 189,314 15,626 8.3% 199,112 14,644 7.4% 209,681 14,476 6.9% White 1,000,631 94,751 9.5% 320,033 36,301 11.3% 329,253 29,418 8.9% 351,345 29,032 8.3% White
0 to 17 72,998,993 10,585,230 49,858,126 # poverty 14,642,040 3,827,543 7,800,105 % poverty 20.1% 36.2% 15.6% 0 to 5 529,516,204 69,268,565 388,834,431 # poverty 341,863,285 46,272,586 248,652,584 % poverty 64.6% 66.8% 63.9% 6 to 11 24,009,371 3,439,383 16,460,877 # poverty 4,766,164 1,243,916 2,550,179 % poverty 19.9% 36.2% 15.5% 12 to 17 25,129,173 3,792,811 17,401,053 # poverty 4,372,186 1,184,344 2,312,789 % poverty 17.4% 31.2% 13.3% Source: 2010 American Community Survey (3-year estimates)
55 | P a g e
American Indian 5,441 2,072 38.1% 1,547 472 30.5% 1,977 1,124 56.9% 1,917 476 24.8% American Indian 17,201 8,411 48.9% 5,810 3,008 51.8% 5,561 3,176 57.1% 5,830 2,227 38.2% American Indian 716,070 240,180 33.5% 4,596,708 3,065,485 66.7% 233,772 78,155 33.4% 252,077 73,667 29.2%
Asian/ Pac. Isl. 59,930 13,349 22.3% 21,214 4,041 19.0% 19,801 4,232 21.4% 18,915 5,076 26.8% Asian/ Pac. Isl. 65,869 13,822 21.0% 23,416 4,144 17.7% 21,733 4,385 20.2% 20,720 5,293 25.5% Asian/ Pac. Isl. 3,319,520 416,629 12.6% 1,114,994 126,453 11.3% 1,111,055 133,030 12.0% 1,093,471 157,146 14.4%
Other/ 2+ 64,786 12,848 19.8% 25,762 5,488 21.3% 22,387 4,127 18.4% 16,637 3,233 19.4% Other/ 2+ 88,585 20,054 22.6% 34,904 8,936 25.6% 30,029 6,265 20.9% 23,652 4,853 20.5% Other/ 2+ 8,520,047 2,357,583 27.7% 3166002 952459 30.1% 2,764,284 760,884 27.5% 2,589,761 644,240 24.9%
Hispanic/ Latino 66,848 18,113 27.1% 26,581 7,931 29.8% 23,336 6,031 25.8% 16,931 4,151 24.5% Hispanic/ Latino 96,521 28,201 29.2% 37,833 12,823 33.9% 32,991 8,859 26.9% 25,697 6,519 25.4% Hispanic/ Latino 16,521,565 5,032,686 30.5% 88,657,212 59,526,339 67.1% 5,402,312 1,652,635 30.6% 5,193,455 1,404,525 27.0%
Seniors in Poverty by Age and Race Minneapolis/ St. Paul MSA 55 to 64 # poverty % poverty 65 to 74 # poverty % poverty 75+ # poverty % poverty
Minnesota 55 to 64 # poverty % poverty 65 to 74 # poverty % poverty 75+ # poverty % poverty
USA
Total 350,866 22,384 6.4% 178,964 11,874 6.6% 148,483 12,665 8.5% Total 599,269 39,869 6.7% 341,719 22,731 6.7% 299,969 32,285 10.8% Total
African American 13,283 4,315 32.5% 5,813 1,921 33.0% 3,298 898 27.2% African American 14,626 4,721 32.3% 6,231 2,177 34.9% 3,479 916 26.3% African American
White 322,057 15,500 4.8% 165,819 8,256 5.0% 141,493 11,128 7.9% White 562,109 31,264 5.6% 324,630 18,110 5.6% 291,288 30,443 10.5% White
55 to 64 34,468,714 3,504,847 28,062,519 # poverty 3,233,413 661,649 2,193,228 % poverty 9.4% 18.9% 7.8% 65 to 74 20,968,815 1,925,314 17,535,995 # poverty 1,751,043 339,984 1,201,471 % poverty 8.4% 17.7% 6.9% 75+ 17,310,798 1,291,021 15,087,750 # poverty 1,865,280 284,889 1,419,759 % poverty 10.8% 22.1% 9.4% Source: 2010 American Community Survey (3-year estimates)
56 | P a g e
American Indian 1,706 271 15.9% 493 143 29.0% 163 55 33.7% American Indian 4,598 1,070 23.3% 1,990 530 26.6% 734 180 24.5% American Indian 221,460 46,785 21.1% 112,670 20,383 18.1% 61,099 13,111 21.5%
Asian/ Pac. Isl. 9,982 1,614 16.2% 5,440 1,314 24.2% 2,768 482 17.4% Asian/ Pac. Isl. 11,893 1,753 14.7% 6,293 1,508 24.0% 3,273 567 17.3% Asian/ Pac. Isl. 1,494,969 132,963 8.9% 825,376 91,647 11.1% 538,537 82,370 15.3%
Other/ 2+ 3,779 684 18.1% 1,386 240 17.3% 727 102 14.0% Other/ 2+ 5,938 1,061 17.9% 2,536 385 15.2% 1,161 179 15.4% Other/ 2+ 1,184,919 198,788 16.8% 569,460 97,558 17.1% 332,391 65,151 19.6%
Hispanic/ Latino 5,803 938 16.2% 2,599 468 18.0% 1,089 270 24.8% Hispanic/ Latino 8,446 1,509 17.9% 3,469 620 17.9% 2,007 538 26.8% Hispanic/ Latino 2,932,869 468,222 16.0% 1,566,311 278,842 17.8% 1,048,389 217,587 20.8%
Ratio of Income to Poverty by Age 9 County Metro # % Total Population Below 50% Below 100% Below 200% 0 to 5 Below 50% Below 100% Below 200% 0 to 17 Below 50% Below 100% Below 200% 18 to 64 Below 50% Below 100% Below 200% 65+ Below 50% Below 100% Below 200%
2,876,625 135,931 296,696 679,993 237,002 16,529 35,756 79,333 715,426 45,375 99,215 217,050 1,862,565 84,295 175,014 388,499 298,634 6,261 22,467 74,444
100.0% 4.7% 10.3% 23.6% 100.0% 7.0% 15.1% 33.5% 100.0% 6.3% 13.9% 30.3% 100.0% 4.5% 9.4% 20.9% 100.0% 2.1% 7.5% 24.9%
MN # 5,157,470 247,275 565,594 1,356,333 418,202 29,771 67,286 150,751 1,263,008 76,664 176,190 410,489 3,252,774 156,708 334,388 753,110 641,688 13,903 55,016 192,734
U.S. % 100.0% 4.8% 11.0% 26.3% 100.0% 7.1% 16.1% 36.0% 100.0% 6.1% 14.0% 32.5% 100.0% 4.8% 10.3% 23.2% 100.0% 2.2% 8.6% 30.0%
# 298,931,525 18,723,394 42,931,760 98,122,034 23,860,449 2,536,400 5,503,690 10,973,814 72,998,993 6,437,076 14,642,040 30,729,630 187,652,919 11,329,918 24,673,397 55,239,888 38,279,613 956,400 3,616,323 12,152,516
% 100.0% 6.3% 14.4% 32.8% 100.0% 10.6% 23.1% 46.0% 100.0% 8.8% 20.1% 42.1% 100.0% 6.0% 13.1% 29.4% 100.0% 2.5% 9.4% 31.7%
Source: 2010 American Community Survey (3-year estimates)
Work Experience of Those in Poverty 9 County Metro # % 208,682 100.0 Worked full time, year-round 15,000 7.2 Worked part-time or part-year 87,821 42.1 Did not work 105,861 50.7 Source: 2010 American Community Survey (3-year estimates) *Population 16 years and over In the past 12 months income below poverty level*:
MN # 408,209 33,723 177,786 196,700
% 100.0 8.3 43.6 48.2
U.S. # 29,768,568 2,697,795 10,178,501 16,892,272
% 100.0 9.1 34.2 56.7
U.S. # 29,750,677 13,355,675 9,375,495 3,980,180 16,395,002
% 100.0 44.9 70.2 29.8 55.1
Employment Status of Those in Poverty 9 County Metro # % Among those in poverty*: 208,610 100.0 In the labor force: 104,313 50.0 Employed 75,521 72.4 Unemployed 28,792 27.6 Not in the labor force 104,297 50.0 Source: 2010 American Community Survey (3-year estimates) *Population 16 years and over
57 | P a g e
MN # 408,107 205,947 154,432 51,515 202,160
% 100.0 50.5 75.0 25.0 49.5
Highest Level of Educational Attainment 9 County Metro Among total population ages 25+: Total Population Less than High School Diploma High School Dip. or Equivalent Some College Bachelorâ&#x20AC;&#x2122;s Degree or Higher
# 1,908,486 140,006 436,746 584,119 747,615
% 100.0% 7.3% 22.9% 30.6% 39.2%
MN # 3,441,768 280,865 944,198 1,118,848 1,097,857
U.S. % 100.0% 8.2% 27.4% 32.5% 31.9%
# 198,503,896 28,445,571 56,123,802 57,616,224 56,318,299
% 100.0% 14.3% 28.3% 29.0% 28.4%
Source: 2010 American Community Survey (3-year estimates)
9 County Metro Among those in poverty ages 25+: Total in Poverty Less than High School Diploma High School Dip. or Equivalent Some College Bachelorâ&#x20AC;&#x2122;s Degree or Higher
# 144,559 38,647 45,077 38,966 21,869
% 100.0% 26.7% 31.2% 27.0% 15.1%
Source: 2010 American Community Survey (3-year estimates)
58 | P a g e
MN # 278,211 69,659 95,798 80,366 32,388
U.S. % 100.0% 25.0% 34.4% 28.9% 11.6%
# 21,678,727 7,293,434 7,015,324 5,150,707 2,219,262
% 100.0% 33.6% 32.4% 23.8% 10.2%
Poverty Status of Families with Children under 18 Minneapolis/ St. Paul MSA All families # in Poverty % in Poverty Married couple # in Poverty % in Poverty Male householder, no wife present # in Poverty % in Poverty Female householder, no husband present # in Poverty % in Poverty Minnesota All families # in Poverty % in Poverty Married couple # in Poverty % in Poverty
816,833 52,216 6.4% 641,180 17,410 2.7%
Asian/ Pacific Islander 4,637 1,275 27.5% 2,061 98 4.8%
49,376 14,564 29.5% 19,746 2,960 15.0%
4,637 1,275 27.5% 2,061 98 4.8%
705,242 28,234 4.0% 579,111 11,000 1.9%
19,500 3,294 16.9% 10,496 604 5.8%
32,019 6,485 20.3% 18,939 2,061 10.9%
50,146 5,986 11.9%
685 190 27.7%
5,730 1,317 23.0%
685 190 27.7%
38,036 3,389 8.9%
2,875 637 22.2%
4,921 1,282 26.1%
125,507 28,820 23.0%
1,891 987 52.2% Asian/ Pacific Islander
23,900 10,287 43.0%
1,891 987 52.2%
88,095 13,845 15.7%
6,129 2,053 33.5%
8,159 3,142 38.5%
1,356,375 94,947 7.0% 1,080,197 33,341 3.1%
12,578 4,482 35.6% 5,105 641 12.6%
53,481 16,319 30.5% 21,723 3,561 16.4%
12,578 4,482 35.6% 5,105 641 12.6%
1,219,708 63,801 5.2% 1,004,187 24,862 2.5%
27,333 5,009 18.3% 15,562 1,270 8.2%
45,106 10,552 23.4% 26,720 3,790 14.2%
82,633 10,709 13.0%
1,969 672 34.1%
6,624 1,753 26.5%
1,969 672 34.1%
66,760 6,991 10.5%
3,695 760 20.6%
6,586 1,719 26.1%
193,545 50,897 26.3%
5,504 3,169 57.6%
25,134 11,005 43.8%
5,504 3,169 57.6%
148,761 31,948 21.5%
8,076 2,979 36.9%
11,800 5,043 42.7%
All
All
African American
African American
American Indian
American Indian
White
White
Other/ 2+
Other/ 2+
Hispanic/ Latino
Hispanic/ Latino
Male householder, no wife present # in Poverty % in Poverty
Female householder, no husband present # in Poverty % in Poverty USA
All
Asian/ Pacific Islander 657,863 136,136 20.7% 392,093 44,433 11.3%
African American
All families 76,262,975 8,737,081 # in Poverty 8,000,664 1,919,346 % in Poverty 10.5% 22.0% Married couple 56,319,371 3,872,473 # in Poverty 2,897,764 288,826 % in Poverty 5.1% 7.5% Male householder, no wife present 5,286,720 74,428 822,240 # in Poverty 817,678 19,218 186,296 % in Poverty 15.5% 25.8% 22.7% Female householder, no husband present 14,656,884 191,342 4,042,368 # in Poverty 4,285,222 72,485 1,444,224 % in Poverty 29.2% 37.9% 35.7% Source: 2010 American Community Survey (3-year estimates)
59 | P a g e
American Indian
White
Other/ 2+
Hispanic/ Latino
553,564 121,845 22.0% 321,039 38,078 11.9%
59,153,642 4,745,633 8.0% 46,683,264 2,039,930 4.4%
4,322,415 906,523 21.0% 2,618,237 340,939 13.0%
10,189,850 2,127,777 20.9% 6,424,536 896,510 14.0%
63,657 17,238 27.1%
3,648,549 483,610 13.3%
526,369 102,796 19.5%
1,167,617 224,147 19.2%
168,868 66,529 39.4%
8,821,829 2,222,093 25.2%
1,177,809 462,788 39.3%
2,597,697 1,007,120 38.8%
References
60 | P a g e
References Acs, G. (2011). Downward Mobility from the Middle Classâ&#x20AC;Ż: Waking Up from the American Dream. Washington, DC: Pew Charitable Trusts. Acs, G., Loprest, P., & Nichols, A. (2009). Understanding the Changing Risks to Family Incomes. LowIncome Working Families No. 14. Washington, DC: The Urban Institute. Acs, G., & Nichols, A. (2010). Changes in the Economic Security of American Families. Low Income Working Families No. 16. Washington, DC: The Urban Institute. Acs, G., & Zimmerman, S. (2008). U.S. Intragenerational Economic Mobility from 1984 to 2004: Trends and Implications. Washington, DC: The Economic Mobility Project and The Pew Charitable Trusts. American Institute for Full Employment (2011, June). Minnesota Unemployment Insurance: Opportunity Report. American Institute for Full Employment: Klamath Falls, OR. Anderson, R.J. (2011). Dynamics of Economic Well-Being: Poverty, 2004-2006. Current Population Reports, Series P70-123, U.S. Census Bureau: Washington, DC. Autor, D. (2010). The Polarization of Job Opportunities in the U.S. Labor Market. Washington, DC.: Center for American Progress and The Hamilton Project. Bane, M. J., & Ellwod, D. T. (1986). Slipping into and out of Poverty: The Dynamics of Spells. The Journal of Human Resources, 21(1), pp. 1-23. Barnett, W. S., & Belfield, C. R. (2006). Early Childhood Development and Social Mobility. The Future of Children, 16(2), pp. 73-98. Beller, E., & Hout, M. (2006). Intergenerational Mobility for Women and Minorities in the United States. The Future of Children, 16(2), pp. 19-36. Berliner, D. (2009). Poverty and Potential: Out-of-School Factors and School Success. East Lansing, MI: The Great Lakes Center for Education Research & Practice. Blank, R. M. (2000). Fighting Poverty: Lessons from Recent U.S. History. Journal of Economic Perspectives, 14(2), pp. 3-19. Blank, R. M. (2011). Changing Inequality. Berkeley, CA: University of California Press. Blank, R. M., & Kovak, B. (2008). The Growing Problem of Disconnected Single Mothers. National Poverty Center Working Paper Series, #07-28. Washington, DC: National Poverty Center.
61 | P a g e
Borjas, G. J. (2006). Making It in America: Social Mobility in the Immigrant Population. Future of Children 16(2), pp. 55-71. Bradbury, K., & Katz, J. (2009). Trends in U.S. Family Income Mobility, 1967-2004. Working Paper No. 097. Federal Reserve Bank of Boston. Brady, D. (2006). Structural theory and Relative Poverty in Rich Western Democracies, 1869-2000. Research in Social Stratification and Mobility. 24, pp. 153-175. Bureau of Labor Statistics (2011, December). Unemployed Persons by Duration of Unemployment. Retrieved from http://www.bls.gov/webapps/legacy/cpsatab12.htm. Buskovick, D., & Peterson, E. A. (2009, July). Domestic Violence: Results from the 2008 Minnesota Crime Victim Survey. St. Paul, MN: Department of Public Safety & Greater Twin Cities United Way. Butrica, B. A., Murphy, D., & Zedlewski, S. R. (2008, January). How Many Struggle to Get By in Retirement? Washington, DC: Urban Institute. Card, D., & Blank, R. M. (2008). The Changing Incidence and Severity of Poverty Spells Among FemaleHeaded Families. American Economic Review, 98(2), pp. 387-391. Cellini, S. R., McKernan, S., & Ratcliffe, C. (2008). The Dynamics of Poverty in the United States: A Review of Data, Methods, and Findings. Journal of Policy Analysis and Management 27(3), pp. 577-605. Collard, C. S. (2007). Linking Housing, Social Services, and Family Well-Being: A Model for Reducing Welfare-Dependency Among the Hard-to-Employ. Journal of Human Behavior in the Social Environment, 15(2/3), 415-433. Collins, S. R., Kriss, J. L., Doty, M. M., & Rustgi, S. D. (2008). Losing Ground: How the Loss of Adequate Health Insurance Is Burdening Working Families. Washington DC: The Commonwealth Fund. Congressional Budget Office (2011). Trends in the Distribution of Household Income Between 1979 and 2007. Washington, DC: The Congress of the United States. Cook, J. A. (2006). Employment Barriers for Persons with Psychiatric Disabilities: Update of a Report for the Presidentâ&#x20AC;&#x2122;s Commission. Psychiatric Services, 57(10), 1391-1405. Retrieved from http://psychservices.psychiatryonline.org/cgi/reprint/57/10/1391. Cunha, F., & Heckman, J. J. (2006, November). Investing in Our Young People. Bethesda, MD: National Institutes of Health. Dearing, E. (2008). Psychological Costs of Growing Up Poor. Annals of the New York Academy of Sciences, 324-332. Dickens, W. T., Sawhill, I., & Tebbs, J. (2006). The Effects of Investing in Early Education on Economic Growth. Washington, DC: The Brookings Institution. 62 | P a g e
Dynan, K. (2010). The Income Rollercoaster: Rising Volatility and its Implications. Pathways. Washington DC: Brookings Institute. Erickson, W. L., & von Schrader, S. (2010). 2008 Disability Status Report: The United States. Ithaca, NY: Cornell University Rehabilitation Research and Training Center on Disability Demographics and Statistics. Evans, G. W., & English, K. (2002). The Environment of Poverty: Multiple Stressor Exposure, Psychophysiological Stress, and Socioemotional Adjustment. Child Development, 73(4), 12381248. Evans, G. W., & Schamberg, M. A. (2009). Childhood Poverty, Chronic Stress, and Adult Working Memory. Proceedings of the National Academy of Sciences, PNAS Early Edition, Retrieved from www.pnas.org/cgi/doi/10.1073/pnas0811910106. Families USA. (2009). Too Great a Burden: Americans Face Rising Health Care Costs. Washington DC: Families USA. Fix, M., Zimmermann, W., & Passel, J. S. (2001). The Integration of Immigrant Families in the United States. Washington DC: The Urban Institute. Fryer, R. G., Jr., & Levitt, S. D. (2004). Understanding the Black-White Test Score Gap in the First Two Years of School. The Review of Economics and Statistics, 86(2), 447-464. Fryer, R. G., Jr., & Levitt, S. D. (2006). The Black-White Test Score Gap Through Third Grade. American Law and Economic Review, 8, pp. 249-281. Grawe, N. (2008). Wealth and Economic Mobility. Washington, DC: Urban Institute. Gerontology Institute, John W. McCormack Graduate School of Policy Studies, University of Massachusetts Boston, & Wider Opportunities for Women. (2009). Elder Economic Security Initiative Program: The Elder Economic Security Index for Minnesota. Grunewald, R. (2006, November). The Connection Between Poverty and the Economy. Fedgazette. Minneapolis, MN: The Federal Reserve Bank of Minneapolis. Retrieved from http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=1296. Harris, A., Evans, H., & Beckett, K. (2010). Drawing Blood from Stones: Legal Debt and Social Inequality in the Contemporary United States. American Journal of Sociology, 115(6), 1753-1799. Heckman, J. J. (2000). Policies to Foster Human Capital. Research in Economics, 54, 3-56. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 1900-1902. Heckman, J. J. (2008, May). Schools, Skills, and Synapses. IZA Discussion Paper No. 3515. Bonn, Germany: IZA. 63 | P a g e
Heckman, J. J., & Masterov, D. V. (2006, August). The Productivity Argument for Investing in Young Children. (Early Childhood Research Collaborative Discussion Paper, ECRC Paper Series). Minneapolis: University of Minnesota and The Federal Reserve Bank of Minneapolis. Himmelstein, D. U., Thorne, D., Warren, E., & Woolhandler, S. (2009). Medical Bankruptcy in the United States, 2007: Results of a National Study. The American Journal of Medicine, 122(8), 741-746. Hisnanick, J.J. & Giefer, G.K. (2011). Dynamics of Economic Well-Being: Fluctuations in the U.S. Income Distribution, 2004-2007. Current Population Reports, P70-124. Washington DC: U.S. Census Bureau. Hoffman, C., Rowland, D. & Hamel, E. C. (2005). Medical Debt and Access to Health Care. Washington DC: Kaiser Family Foundation. Holzer, H. J. (2009). Workforce Development as an Antipoverty Strategy: What Do We Know? What Should We Do? Focus 26(2), 62-68. Holzer, H. J., Schanzenbach, D. W., Duncan, G. J., & Ludwig, J. (2007). The Economic Costs of Poverty in the United States: Subsequent Effect of Children Growing up Poor. Washington, DC: Center for American Progress. HousingLink (2011). 2010 Foreclosures in Minnesota: A Report Based on County Sheriffâ&#x20AC;&#x2122;s Sale Data. Minneapolis, MN: HousingLink. Hoynes, H., Page, M., & Stevens, A. (2005). Poverty in America: Trends and Explanations. Working Paper 11681. Cambridge, MA: National Bureau of Economic Research. Hudson, C. G. (2005). Socioeconomic Status and Mental Illness: Tests of the Social Causation and Selection Hypotheses. American Journal of Orthopsychiatry, 75(1), 3-18. Retrieved from http://www.salemstate.edu/~chudson/papers/final_copy_sesmi.pdf. Iceland, J. (2006). Poverty in America: A Handbook. Berkeley: University of California Press. Isaacs, J. (2008a). Economic Mobility of Families Across Generations. In J. B. Isaacs, I. V. Sawhill, & R. Haskins (Eds.), Getting Ahead or Losing Ground: Economic Mobility in America (pp. 15-26). Washington, DC: The Brookings Institution. Isaacs, J. (2008b). Economic Mobility of Black and White Families. In J. B. Isaacs, I. V. Sawhill, & R. Haskins (Eds.), Getting Ahead or Losing Ground: Economic Mobility in America (pp. 71-80). Washington, DC: The Brookings Institution. Jacob, B. A., & Ludwig, J. (2010). Improving Educational Outcomes for Poor Children. In M. Cancian & S. Danziger (Eds.), Changing Poverty, Changing Policies (pp. 266-300). New York, NY: Russell Sage Foundation.
64 | P a g e
Jobs Now Coalition. (2010). Family Wage and Budget Calculator. Retrieved from http://www.jobsnowcoalition.org/calculator/calculator.html. Johnson, R. W., Favreault, M. M., & Mommaerts, C. (2010, January). Disability Just Before Retirement Often Leads to Poverty. Washington, DC: Urban Institute. Johnson, R. W., & Mermin, G. B. T. (2009, March). Financial Hardship before and after Social Securityâ&#x20AC;&#x2122;s Eligibility Age. Washington, DC: Urban Institute. Johnson, R. W., & Mommaerts, C. (2009, July). Are Health Care Costs a Burden for Older Americans? Washington, DC: Urban Institute. Jones, A. F., Jr., & Weinberg, D. H. (2000). The Changing Shape of the Nationâ&#x20AC;&#x2122;s Income Distribution: 19471998. Washington, DC: U.S. Census Bureau. Kaiser Family Foundation. (2011, March). Kaiser Health Tracking Poll. Washington DC: Kaiser Family Foundation. Kearney, M. S. (2006). Intergenerational Mobility for Women and Minorities in the United States. The Future of Children, 16(2), 37-54. Kemple, J. J. (2008, June). Career Academies: Long-Term Impacts on Labor Market Outcomes, Educational Attainment, and Transitions to Adulthood. New York, NY: MRDC. Kessler Foundation & National Organization on Disability (2010, July). The ADA, 20 Years Later. New York: Harris Interactive. Lee, Y., & Lee, J. (2006). The Poverty of Widows: How Do They Become the Poor? Paper presented at the Population Association of American 2006 Annual Meeting. Retrieved from http://paa2006.princeton.edu/download.aspx?submissionId=61592. Levine, P. B., & Zimmerman, D. J. (Eds.). (2010). Targeting Investments in Children: Fighting Poverty When Resources Are Limited. Chicago: University of Chicago Press. McGarry, K., & Schoeni, R. F. (2005). Medicare Gaps and Widow Poverty. Social Security Bulletin, 66(1). McKernan, S., & Ratcliffe, C. (2005). Events that Trigger Poverty Entries and Exits. Social Science Quarterly, 86, pp. 1146-1169. McKernan, S., Ratcliffe, C., & Cellini, S. R. (2009). Transitioning In and Out of Poverty. Understanding Poverty. Fact Sheet No. 1. Washington DC: The Urban Institute. Meschede, T., Shapiro, T. M., Sullivan, L., & Wheary, J. (2010). Severe Financial Insecurity among AfricanAmerican and Latino Seniors. Waltham, MA: Institute on Assets and Social Policy.
65 | P a g e
Minnesota Compass (2011, December). Proportion of Adults working. Retrieved from http://www.mncompass.org/immigration/keymeasures.php?km=Proportionofadultsworking#7-3025-g. Minnesota Department of Employment and Economic Development. (2011). Job Vacancy Survey 2nd Quarter 2011. Retrieved from http://www.positivelyminnesota.com/Data_Publications/Data/All_Data_Tools/Job_Vacancy_Su rvey_2.aspx. Minnesota Department of Health (2011). Health Economics Program. [Data Query Program]. Retrieved from http://www.health.state.mn.us/healtheconomics. Moore, Q., Rangarajan, A., & Schochet, P. (2007). Economic Patterns of Single Mothers Following Their Poverty Exits. Princeton, NJ: Mathematica Policy Research, Inc. National Institutes of Mental Health. (2010). The Numbers Count: Mental Disorders in America. Retrieved from http://www.nimh.nih.gov/health/publications/the-numbers-count-mentaldisorders-in-america/index.shtml#Intro. Neuman, S. B. (2009). Changing the Odds for Children at Risk: Seven Essential Principles of Educational Programs that Break the Cycle of Poverty. Westport, CT: Praeger. Newman, K. S. (1995). Deindustrialization, Poverty, and Downward Mobility: Toward an Anthropology of Economic Disaster. In S. Forman, (Ed.), Diagnosing America: Anthropology and Public Engagement (pp. 121-161). Ann Arbor, MI: The University of Michigan Press. Oâ&#x20AC;&#x2122;Brien, E., Wu, K. B., & Baer, D. (2010, April). Older Americans in Poverty: A Snapshot. Washington, DC: AARP. Owen, G. (2010). A New Age of Immigrants: Making Immigration Work for Minnesota. Minneapolis, MN: Minneapolis Foundation. Palumbo, T. J. (2010). Dynamics of Economic Well-Being: Spells of Unemployment, 2004-2007. Current Population Reports P70-120. Washington DC: U.S. Census Bureau. Passel, J. S., & Cohn, D. (2009). A Portrait of Unauthorized Immigrants in the United States. Washington DC: Pew Hispanic Research Center. Perese, E. F. (2007). Stigma, Poverty, and Victimization: Roadblocks to Recovery for Individuals with Severe Mental Illness. Journal of the American Psychiatric Nurses Association. 13(5), 285-295. Pew Charitable Trusts. (2010). Collateral Costs: Incarcerationâ&#x20AC;&#x2122;s Effect on Economic Mobility: Washington, DC: The Pew Charitable Trusts.
66 | P a g e
Postmus, J. L. (2010, October). Economic Empowerment of Domestic Violence Survivors. Applied Research Forum: National Online Resource Center on Violence Against Women. Retrieved from http://new.vawnet.org/Assoc_Files_VAWnet/AR_EcoEmpowerment.pdf. Prince, D., & Jenkins, D. (2005, April). Building Pathways to Success for Low-Skill Adult Students. Retrieved from http://ccrc.tc.columbia.edu/Publication.asp?UID=204. Public Policy Institute. (2003). In Brief: Poverty Experience of Older Persons: A Poverty Study from a LongTerm Perspective. Washington, DC: AARP. Purcell, P. J. (2009, October). Income and Poverty Among Older Americans in 2008. Washington, DC: Congressional Research Service. Rank, M. R. (2007). Rethinking the Scope and Impact of Poverty in the United States, Connecticut Public Interest Law Journal, 6(2), 165-181. Raphael, S., & Smolensky E. (2009). Immigration and Poverty in the United States. Focus 26(2). Ratcliffe, C., & McKernan, S. (2010). Childhood Poverty Persistence. Washington DC: the Urban Institute. Rawlings, L. A., Capps, R., Gentsch, K., & Fortuny, K. (2007). Immigrant Integration in Low-Income Urban Neighborhoods: Improving Economic Prospects and Strengthening Connections for Vulnerable Families. Washington, DC: The Urban Institute. Rohrer, A. (2011). Left Behind by the Job Market. Minnesota Economic Trends. September 2011, pp. 2-8. Rynell, A. (2008). Causes of Poverty: Findings from Recent Research. Chicago, IL: Heartland Alliance. Sawhill, I. V. (2008a). Overview. In J.B. Isaacs, I.V. Sawhill, & R. Haskins (Eds.), Getting Ahead or Losing Ground: Economic Mobility in America (pp. 1-14). Washington, DC: The Brookings Institution. Sawhill, I. V. (2008b). Trends in Intergenerational Mobility. In J.B. Isaacs, I.V. Sawhill, & R. Haskins (Eds.), Getting Ahead or Losing Ground: Economic Mobility in America (pp. 27-36). Washington, DC: The Brookings Institution. Sawhill, I. V., & McLanahan, S. (2006). Introducing the Issue. The Future of Children. 16(2) pp. 3-18. Schmitt, J., & Warner, K. (2010, November). Ex-offenders and the Labor Market. Washington, DC: Center for Economic and Policy Research. Seifert, R. W. (2005). Home Sick: How Medical Debt Undermines Housing Security. Boston, MA: The Access Project. Senf, D. (2010). The Great Recession Lives Up To Its Name. Economic Trends Magazine, September, 2010.
67 | P a g e
Shapiro, T. M. (2004). Hidden Cost of Being African American: How Wealth Perpetuates Inequality. New York: Oxford Press. Shapiro, T. M. (2006). Race, Homeownership, and Wealth. Journal of Law and Policy, 20(53), 53-74. Sharkey, P. (2009). Neighborhoods and the Black-White Mobility Gap. Washington DC: The Pew Charitable Trusts. She, P., & Livermore, G. A. (2009). Long-Term Poverty and Disability Among Working-Age Adults. Journal of Disability Policy Studies, 19(4), 244-256. Shore, R. (1997). Rethinking the Brain: New Insights into Early Development. New York: Families and Work Institute. Short, K. (2011). The Research SUPPLEMENTAL POVERTY MEASURE: 2010. Current Population Reports P60-241. Washington, DC: U.S. Census Bureau. Soares, L. (2009, June). Working Learners: Educating Our Entire Workforce for Success in the 21st Century. Washington, DC: Center for American Progress. Taylor, P., Kochhar, R., Fry, R., Velasco, G., & Motel, S. (2011). Wealth Gaps Rise to Record Highs Between Whites, Blacks and Hispanics. Washington, DC: Pew Research Center. U.S. Census Bureau. (2008, August). Income, Poverty, and Health Insurance Coverage in the United States: 2007. Washington, DC: U.S. Department of Commerce. U.S. Census Bureau. (2008, December). Americans with Disabilities: 2005. Washington, DC: U.S. Department of Commerce. U.S. Census Bureau. (2009, September). Income, Poverty, and Health Insurance Coverage in the United States: 2008. Washington, DC: U.S. Department of Commerce. U.S. Census Bureau. (2010, September). Income, Poverty, and Health Insurance Coverage in the United States: 2009. Washington, DC: U.S. Department of Commerce. U.S. Census Bureau. (2011, January). Preliminary Estimate of Weighted Average Poverty Thresholds for 2010. Retrieved from http://www.census.gov/hhes/www/poverty/data/threshld/index.html. Uphoff, K. (2010, September). More Eggs In Fewer Baskets. Minnesota Economic Trends. St. Paul, MN: Minnesota Department of Employment and Economic Development. Van de Water, P. N., & Sherman, A. (2010, August). Social Security Keeps 20 Million Americans Out of Poverty: A State-By-State Analysis. Washington, DC: Center of Budget and Policy Priorities. Vericker, T., Fortuny, K., Finegold, K., & Ozdemir, S. B. (2010) Effects of Immigration on WIC and NSLP Caseloads. Washington DC: The Urban Institute.
68 | P a g e
Vilsack, R. (2009, June). By the Numbers: A Snapshot of Minnesota Veterans. Minnesota Economic Trends. St. Paul, MN: Minnesota Department of Employment and Economic Development. Western, B. (2002). The Impact of Incarceration on Wage Mobility and Inequality. American Sociological Review 67(4), 526-546. Wilder Research. (2010a, December). Homeless Veterans in Minnesota 2009. St. Paul, MN: Wilder Research. Wilder Research. (2010b, October). Homelessness in Minnesota 2009. St. Paul, MN: Wilder Research. Wilson, W. J. (1996). When Work Disappears: The World of the New Urban Poor. New York: Vintage. Wilson, W. J. (2009). More Than Just Race: Being Black and Poor in the Inner City. New York: W. W. Norton & Co. Wirtz, R.A. (2006a, November). Excuse me, Which Way Is Up? Fedgazette. Minnepolis, MN: The Federal Reserve Bank of Minneapolis. Wirtz, R.A. (2006b, November). The Myriad, Changing Faces of Poverty. Fedgazette. Minneapolis, MN: The Federal Reserve Bank of Minneapolis.
69 | P a g e