Poverty in the United States: 2013

In 2013, 45.3 million people were counted as poor in the United States under the official poverty measure—a number statistically unchanged from the 46.5 million people estimated as poor in 2012. The poverty rate, or percent of the population considered poor under the official definition, was reported at 14.5% in 2013, a statistically significant drop from the estimated 15.0% in 2012. Poverty in the United States increased markedly over the 2007-2010 period, in tandem with the economic recession (officially marked as running from December 2007 to June 2009), and remained unchanged at a post-recession high for three years (15.1% in 2010, and 15.0% in both 2011 and 2012). The 2013 poverty rate of 14.5% remains above a 2006 pre-recession low of 12.3%, and well above an historic low rate of 11.3% attained in 2000 (a rate statistically tied with a previous low of 11.1% in 1973).

The incidence of poverty varies widely across the population according to age, education, labor force attachment, family living arrangements, and area of residence, among other factors. Under the official poverty definition, an average family of four was considered poor in 2013 if its pre-tax cash income for the year was below $23,834.

The measure of poverty currently in use was developed some 50 years ago, and was adopted as the “official” U.S. statistical measure of poverty in 1969. Except for minor technical changes, and adjustments for price changes in the economy, the “poverty line” (i.e., the income thresholds by which families or individuals with incomes that fall below are deemed to be poor) is the same as that developed nearly a half century ago, reflecting a notion of economic need based on living standards that prevailed in the mid-1950s.

Moreover, poverty as it is currently measured only counts families’ and individuals’ pre-tax money income against the poverty line in determining whether or not they are poor. In-kind benefits, such as benefits under the Supplemental Nutrition Assistance Program (SNAP, formerly named the Food Stamp program) and housing assistance, are not accounted for under the “official” poverty definition, nor are the effects of taxes or tax credits, such as the Earned Income Tax Credit (EITC) or Child Tax Credit (CTC). In this sense, the “official” measure fails to capture the effects of a variety of programs and policies specifically designed to address income poverty.

A congressionally commissioned study conducted by a National Academy of Sciences (NAS) panel of experts recommended, some 20 years ago, that a new U.S. poverty measure be developed, offering a number of specific recommendations. The Census Bureau, in partnership with the Bureau of Labor Statistics (BLS), has developed a Supplemental Poverty Measure (SPM) designed to implement many of the NAS panel recommendations. The SPM is to be considered a “research” measure, to supplement the “official” poverty measure. Guided by new research, the Census Bureau and BLS intend to improve the SPM over time. The “official” statistical poverty measure will continue to be used by programs that use it as the basis for allocating funds under formula and matching grant programs. The Department of Health and Human Services (HHS) will continue to issue poverty income guidelines derived from “official” Census Bureau poverty thresholds. HHS poverty guidelines are used in determining individual and family income eligibility under a number of federal and state programs. Estimates from the SPM differ from the “official” poverty measure and are presented in a final section of this report.

Poverty in the United States: 2013

January 29, 2015 (RL33069)

Contents

Figures

Summary

In 2013, 45.3 million people were counted as poor in the United States under the official poverty measure—a number statistically unchanged from the 46.5 million people estimated as poor in 2012. The poverty rate, or percent of the population considered poor under the official definition, was reported at 14.5% in 2013, a statistically significant drop from the estimated 15.0% in 2012. Poverty in the United States increased markedly over the 2007-2010 period, in tandem with the economic recession (officially marked as running from December 2007 to June 2009), and remained unchanged at a post-recession high for three years (15.1% in 2010, and 15.0% in both 2011 and 2012). The 2013 poverty rate of 14.5% remains above a 2006 pre-recession low of 12.3%, and well above an historic low rate of 11.3% attained in 2000 (a rate statistically tied with a previous low of 11.1% in 1973).

The incidence of poverty varies widely across the population according to age, education, labor force attachment, family living arrangements, and area of residence, among other factors. Under the official poverty definition, an average family of four was considered poor in 2013 if its pre-tax cash income for the year was below $23,834.

The measure of poverty currently in use was developed some 50 years ago, and was adopted as the "official" U.S. statistical measure of poverty in 1969. Except for minor technical changes, and adjustments for price changes in the economy, the "poverty line" (i.e., the income thresholds by which families or individuals with incomes that fall below are deemed to be poor) is the same as that developed nearly a half century ago, reflecting a notion of economic need based on living standards that prevailed in the mid-1950s.

Moreover, poverty as it is currently measured only counts families' and individuals' pre-tax money income against the poverty line in determining whether or not they are poor. In-kind benefits, such as benefits under the Supplemental Nutrition Assistance Program (SNAP, formerly named the Food Stamp program) and housing assistance, are not accounted for under the "official" poverty definition, nor are the effects of taxes or tax credits, such as the Earned Income Tax Credit (EITC) or Child Tax Credit (CTC). In this sense, the "official" measure fails to capture the effects of a variety of programs and policies specifically designed to address income poverty.

A congressionally commissioned study conducted by a National Academy of Sciences (NAS) panel of experts recommended, some 20 years ago, that a new U.S. poverty measure be developed, offering a number of specific recommendations. The Census Bureau, in partnership with the Bureau of Labor Statistics (BLS), has developed a Supplemental Poverty Measure (SPM) designed to implement many of the NAS panel recommendations. The SPM is to be considered a "research" measure, to supplement the "official" poverty measure. Guided by new research, the Census Bureau and BLS intend to improve the SPM over time. The "official" statistical poverty measure will continue to be used by programs that use it as the basis for allocating funds under formula and matching grant programs. The Department of Health and Human Services (HHS) will continue to issue poverty income guidelines derived from "official" Census Bureau poverty thresholds. HHS poverty guidelines are used in determining individual and family income eligibility under a number of federal and state programs. Estimates from the SPM differ from the "official" poverty measure and are presented in a final section of this report.


Poverty in the United States: 2013

Trends in Poverty1

In 2013, the official U.S. poverty rate was 14.5%, compared to 15.0% in 2012, and marked the first statistically significant drop in the rate since 2006. In 2013, 45.3 million persons were estimated as having income below the official poverty line, a number statistically unchanged from the estimated 46.5 million poor in 2012. (See Figure 1.)

Figure 1 shows a clear relationship between poverty and the economy. The level of poverty tends to follow the economic cycle quite closely, tending to rise when the economy is faltering and fall when the economy is in sustained growth.

The poverty rate increased markedly over the past decade, in part a response to two economic recessions (periods marked in red). A strong economy during most of the 1990s is generally credited with the declines in poverty that occurred over the latter half of that decade, resulting in a record-tying, historic low poverty rate of 11.3% in 2000 (a rate statistically tied with the previous lowest recorded rate of 11.1% in 1973). The poverty rate increased each year from 2001 through 2004, a trend generally attributed to economic recession (March 2001 to November 2001), and failed to recede appreciably before the onset of the December 2007 recession. This most recent recession, which officially ended in June 2009, was the longest recorded (18 months) in the post-World War II period.2 Over the course of the most recent recession, the unemployment rate increased from 4.9% (January 2008) to 7.2% (December 2008), and continued to rise over most of 2009, peaking at 10.0% in October of that year. Even as the economy has been recovering, poverty has remained well above pre-recessionary levels. Although the unemployment rate has generally been falling since late 2009, it has not been until this past year that we have seen a marked (statistically significant) decline in the official poverty rate. That the unemployment rate has continued to fall over 2014 suggests that poverty levels are likely to fall in 2014. Poverty statistics for 2014 poverty will be issued in the late summer of 2015. The recession especially affected non-aged adults (persons age 18 to 64) and children. (See Figure 2.) The poverty rate of non-aged adults reached 13.8% in 2010, the highest it has been since the early 1960s.3 In 2013 the non-aged poverty rate of 13.6% remained statistically unchanged from rates seen in the prior three years. The poverty rate for non-aged adults will need to fall to 10.8% to reach its 2006 pre-recession level.

The 2013 poverty data provide one encouraging sign with respect to children. Both the estimated number of poor children and their poverty rate fell from 2012 to 2013. In 2013, the number of poor children fell by an estimated 1.3 million (15.4 million in 2012 to 14.1 million in 2013), and their poverty rate fell from 21.3% in 2012 to 19.5% in 2013. The 2013 child poverty rate is still well above its pre-recession low of 16.9% (2006). Child poverty appears to be especially sensitive to economic cycles, as it often takes two working parents to support a family, and a loss of work by one may put the family at risk of falling into poverty.4 Moreover, roughly one-third of all children in the country live with only one parent, making them even more prone to falling into poverty when the economy falters.

In 2013, the aged poverty rate (9.5%) was statistically unchanged from 2012, although the number of poor rose by an estimated 305,000 (from 3.9 million in 2012 to 4.2 million in 2013). In spite of the recession, the aged poverty rate remains near an historic low level. The longer-term secular trend in poverty has been affected by changes in household and family composition and by government income security and transfer programs. In 1959, over one-third (35.2%) of persons age 65 and over were poor, a rate well above that of children (26.9%). Social Security, in combination with a maturing pension system, has helped greatly to reduce the incidence of poverty among the aged over the years, and as recent evidence seems to show, it has helped protect them during the economic downturn.

The U.S. "Official" Definition of Poverty5

The Census Bureau's poverty thresholds form the basis for statistical estimates of poverty in the United States.6 The thresholds reflect crude estimates of the amount of money individuals or families, of various size and composition, need per year to purchase a basket of goods and services deemed as "minimally adequate," according to the living standards of the early 1960s. The thresholds are updated each year for changes in consumer prices. In 2013, for example, the average poverty threshold for an individual living alone was $11,888; for a two-person family, $15,142; and for a family of four, $23,834.7

The current official U.S. poverty measure was developed in the early 1960s using data available at the time. It was based on the concept of a minimal standard of food consumption, derived from research that used data from the U.S. Department of Agriculture's (USDA's) 1955 Food Consumption Survey. That research showed that the average U.S. family spent one-third of its pre-tax income on food. A standard of food adequacy was set by pricing out the USDA's Economy Food Plan—a bare-bones plan designed to provide a healthy diet for a temporary period when funds are low. An overall poverty income level was then set by multiplying the food plan by three, to correspond to the findings from the 1955 USDA Survey that an average family spent one-third of its pre-tax income on food and two-thirds on everything else.

The "official" U.S. poverty measure8 has changed little since it was originally adopted in 1969, with the exception of annual adjustments for overall price changes in the economy, as measured by the Consumer Price Index for all Urban Consumers (CPI-U). Thus, the poverty line reflects a measure of economic need based on living standards that prevailed in the mid-1950s. It is often characterized as an "absolute" poverty measure, in that it is not adjusted to reflect changes in needs associated with improved standards of living that have occurred over the decades since the measure was first developed. If the same basic methodology developed in the early 1960s was applied today, the poverty thresholds would be over three times higher than the current thresholds.9

Persons are considered poor, for statistical purposes, if their family's countable money income is below its corresponding poverty threshold. Annual poverty estimates are based on a Census Bureau household survey (Annual Social and Economic Supplement to the Current Population Survey, CPS/ASEC, conducted February through April). The official definition of poverty counts most sources of money income received by families during the prior year (e.g., earnings, social security, pensions, cash public assistance, interest and dividends, alimony, and child support, among others). For purposes of officially counting the poor, noncash benefits (such as the value of Medicare and Medicaid, public housing, or employer provided health care) and "near cash" benefits (e.g., food stamps, renamed Supplemental Assistance Nutrition (SNAP) benefits beginning in FY2009) are not counted as income, nor are tax payments subtracted from income, nor are tax credits added (e.g., Earned Income Tax Credit (EITC)). Many believe that these and other benefits should be included in a poverty measure so as to better reflect the effects of government programs on poverty.

The Census Bureau, in partnership with the Bureau of Labor Statistics (BLS), has recently released a Supplemental Poverty Measure (SPM), designed to address many of the perceived flaws of the "official" measure. The SPM is discussed in a separate section at the end this report (see "The Research Supplemental Poverty Measure").

Figure 1. Trend in Poverty Rate and Number of Poor Persons: 1959-2013,
and Unemployment Rate from January 1959 through August 2014

(recessionary periods marked in red)

Source: Prepared by the Congressional Research Service (CRS) using U.S. Census Bureau, "Income and Poverty United States: 2013,"
Table B-1, Current Population Report P60-249, September 2014, available on the Internet at http://www.census.gov/content/dam/Census/library/publications/2014/demo/p60-249.pdf. Unemployment rates are available on the Internet at http://www.bls.gov/cps/. Recessionary periods defined by National Bureau of Economic Research Business Cycle Dating Committee: http://www.nber.org/cycles/main.html.

Figure 2. U.S. Poverty Rates by Age Group, 1959-2013

Source: Prepared by the Congressional Research Service using U.S. Census Bureau, "Income and Poverty in the United States: 2013," Tables
B-1 and B-2, Current Population Report P60-249, September 2014, available on the Internet at http://www.census.gov/content/dam/Census/library/publications/2014/demo/p60-249.pdf.

Poverty among Selected Groups

Even during periods of general prosperity, poverty is concentrated among certain groups and in certain areas. Minorities; women and children; the very old; the unemployed; and those with low levels of educational attainment, low skills, or disability, among others, are especially prone to poverty.

Racial and Ethnic Minorities10

The incidence of poverty among African Americans and Hispanics exceeds that of whites by several times. In 2013, 27.2% of blacks (11.0 million) and 23.5% of Hispanics (12.7 million) had incomes below poverty, compared to 9.6% of non-Hispanic whites (18.8 million) and 10.5% of Asians (1.8 million). Although blacks represent only 13.0% of the total population, they make up 24.4% of the poor population; Hispanics, who represent 17.3% of the population, account for 28.1% of the poor. Poverty rates for Hispanics fell from 25.6% in 2012 to 23.5% in 2013, as did the number of poor Hispanics, from 13.6 million in 2012, to 12.7 million in 2013. Poverty rates and the numbers estimated as poor were statistically unchanged from 2012 to 2013 for white non-Hispanics, blacks, and Asians.

Nativity and Citizenship Status

In 2013, among the native-born population, 13.9% (37.9 million) were poor—a rate and number statistically unchanged from 2012 (14.3%, 38.8 million). Among the foreign-born population, 18.0% (7.4 million) were poor in 2013—a statistically significant drop in the poverty rate (from 19.7%), but not in the number estimated as poor. The poverty rate among foreign-born naturalized citizens (12.7%, in 2013) was lower than that of the native-born U.S. population (13.9%). In 2013, the poverty rate of non-citizens (22.8%) dropped significantly from 2012 (24.9%), as did the estimated number who were poor (about one-half million, dropping from 5.4 million in 2012, to 4.0 million in 2013).

Children

Poverty among children dropped significantly from 2012 to 2013. Their estimated poverty rate fell from 21.3% in 2012, to 19.5% in 2013. In 2013, an estimated 1.3 million fewer children were poor than in 2012 (14.1 million versus 15.4 million, respectively). However, the 2013 child poverty rate (19.5%) is still well above its pre-recession low of 16.9% (2006). The lowest recorded rate of child poverty was in 1969, when 13.8% of children were counted as poor.

Children living in single female-headed families are especially prone to poverty. In 2013 a child living in a single female-headed family was nearly five times more likely to be poor than a child living in a married-couple family. In 2013, among all children living in single female-headed families, 45.8% were poor. In contrast, among children living in married-couple families, 9.5% were poor. The increased share of children who live in single female-headed families has contributed to the high overall child poverty rate. In 2013, one quarter (25.0%) of children were living in single female-headed families, more than double the share who lived in such families when the overall child poverty rate was at a historical low (1969). Among all poor children, nearly 6 in 10 (58.7%) were living in single female-headed families in 2013.

In 2013, 38.0% of black children were poor (4.2 million), compared to 30.0% of Hispanic children (5.3 million) and 10.1% of non-Hispanic white children (3.8 million). (See Figure 3.) Among children living in single female-headed families, more than half of black children (54.0%) and Hispanic children (52.3%) were poor; in contrast, one-third of non-Hispanic white children (33.6%) were poor. The poverty rate among Hispanic children who live in married-couple families (19.9%) was above that of black children (16.8%), and four times that of non-Hispanic white children (4.9%) who live in such families. Contributing to the high rate of overall black child poverty is the large share of black children who live in single female-headed families (54.0%) compared to Hispanic children (30.1%) or non-Hispanic white children (15.7%). (See Figure 4.)

Figure 3. Child Poverty Rates by Family Living Arrangement, Race and Hispanic Origin, 2013

Source: Figure prepared by the Congressional Research Service (CRS) based on U.S. Census Bureau data from
the 2014 Current Population Survey Annual Social and Economic Supplement, available at http://www.census.gov/hhes/www/cpstables/032014/pov/pov05_000.htm.

Figure 4. Composition of Children, by Family Type, Race and Hispanic Origin, 2013

Source: Figure prepared by the Congressional Research Service (CRS) based on U.S. Census Bureau data from
the 2014 Current Population Survey Annual Social and Economic Supplement, available at http://www.census.gov/hhes/www/cpstables/032014/pov/pov05_000.htm.

Adults with Low Education, Unemployment, or Disability

Adults with low education, those who are unemployed, or those who have a work-related disability are especially prone to poverty. Among 25- to 34-year-olds without a high school diploma, between one-third and two-fifths (36.8%) were poor in 2013. In 2013, 1 in 10 25- to 34-year-olds lacked a high school diploma. Within the same age group whose highest level of educational attainment was a high school diploma, about one in five (20.7%) were poor. In contrast, only about 1 in 16 (6.5%) of 25- to 34-year-olds with at least a bachelor's degree were found to be living below the poverty line.

Among persons between the ages of 16 and 64 who were unemployed in March 2014, nearly 3 out of 10 (29.8%) were poor based on their families' incomes in 2013; among those who were employed, 6.9% were poor.

In 2013, persons who had a work disability11 represented 11.3% of the 16- to 64-year-old population, and about one-quarter (26.0%) of the poor population within this age range. Among those with a severe work disability, 35.6% were poor, compared to 17.0% of those with a less severe disability and 11.4% who reported having no work-related disability.

The Aged

In 2013, the 9.5% poverty rate among persons age 65 and older was statistically unchanged from the 2012 rate (9.1%), but statistically higher than the all-time low-poverty rate among the aged of 8.7% attained in 2011. The number of aged poor grew by 305,000 from 2012 to 2013, from 3.9 million to 4.2 million,. Among persons age 75 and over, 11.2% were poor in 2013, compared to 8.3% of those ages 65 to 74. Measured by a slightly raised poverty standard (125% of the poverty threshold), 15.1% of the aged could be considered poor or "near poor" in 2013; 12.6% who are ages 65 to 74, and 18.4% who are 75 years of age and over, could be considered poor or "near poor."

Receipt of Need-Tested Assistance Among the Poor

In 2013, nearly three of every four poor persons (73.8%) lived in households that received any means-tested assistance during the year.12 Such assistance could include cash aid, such as Temporary Assistance for Needy Families (TANF), Supplemental Security Income (SSI) payments, SNAP benefits (Food Stamps), Medicaid, subsidized housing, free or reduced price school lunches, and other programs. In 2013, somewhat over one in five (17.4%) poor persons lived in households that received cash aid; half (49.5%) received SNAP benefits (formerly named Food Stamps); 6 in 10 (61.3%) lived in households where one or more household members were covered by Medicaid; and about 1 in 7 (14.8%) lived in subsidized housing. Poor single-parent families with children are among those families most likely to receive cash aid. Among poor children who were living in single female-headed families, about one-fifth (21.9%) were in households that received government cash aid in 2013, down from 24.0% in 2012. The share of poor children in single female-headed families receiving cash aid is well below historical levels. In 1993, 70.2% of these children's families received cash aid. In 1995, the year prior to passage of sweeping welfare changes under PRWORA, 65% of such children were in families receiving cash aid.

The Geography of Poverty

Poverty is more highly concentrated in some areas than in others; it is about twice as high in center cities as it is in suburban areas and nearly three times as high in the poorest states as it is in the least poor states. Some neighborhoods may be characterized as having high concentrations of poverty. Among the poor, the likelihood of living in an area of concentrated or extreme poverty varies by race and ethnicity.

Poverty in Metropolitan and Nonmetropolitan Areas, Center Cities, and Suburbs

Within metropolitan areas, the incidence of poverty in central city areas is considerably higher than in suburban areas—19.1% versus 11.1%, respectively, in 2013. Nonmetropolitan areas had a poverty rate of 16.1%. A typical pattern is for poverty rates to be highest in center city areas, with poverty rates dropping off in suburban areas, and then rising with increasing distance from an urban core. In 2013, only nonmetropolitan areas experienced a statistically significant decline in poverty (both rate and numbers poor) from 2012, with the poverty rate decreasing from the 17.7% in 2012 to 16.1% in 2013, and the number of poor declining by an estimated 891,000 persons. Poverty rates and estimated numbers of poor people remained statistically unchanged in metropolitan areas, center cities, and suburbs from 2012 to 2013.

Poverty by Region

In 2013, poverty rates were lowest in the Northeast (12.7%) and Midwest (12.9%), followed by the West (14.7%), with the South (16.1%) having the highest poverty rate. Poverty remained statistically unchanged (measured both in terms of numbers poor and rates) in each of the four regions from 2012 to 2013.

State Poverty Rates

American Community Survey (ACS) State Poverty Estimates—2013

Up to this point, the poverty statistics presented in this report come from the U.S. Census Bureau's Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS). For purposes of producing state and sub-state poverty estimates, the Census Bureau now recommends using the American Community Survey (ACS)—because of its much larger sample size, the ACS produces estimates with a much smaller margin of statistical error than that of the CPS/ASEC. However, it should be noted that the ACS survey design differs from the CPS/ASEC in a variety of ways, and may produce somewhat different estimates than those obtained from the ASEC/CPS. Based on the 2013 ACS, the U.S. poverty rate was estimated to be 15.8%, compared to 14.5% based on the 2014 CPS/ASEC. The CPS/ASEC estimates are based on a survey conducted in February through April 2013, and account for income reported for the previous year. In contrast, the ACS estimates are based on income information collected between January and December 2013, for the prior 12 months. For example, for the sample with data collected in January, the reference period is from January 2012 to December 2013, and for the sample with data collected in December, from December 2012 to November 2013. The ACS data consequently cover a time span of 23 months, with the data centered at mid-December 2012.

Based on 2012 American Community Survey (ACS) data, poverty rates were highest in the South (with the exception of Virginia), extending across to Southwestern states bordering Mexico (Texas, New Mexico, and Arizona). (See Figure 5.) Poverty rates in several states bordering the Ohio River (Ohio, West Virginia, Kentucky) also exceeded the national rate, as did those of Michigan and New York, and the District of Columbia, in the eastern half of the nation, and California, Oregon, and Montana in the western half.

States along the Atlantic Seaboard from Virginia northward tended to have poverty rates well below the national rate, as did three contiguous states in the upper Midwest/plains (Iowa, Minnesota, and North Dakota), as well as Utah, Wyoming, Alaska, and Hawaii.

Figure 5. Percentage of People in Poverty in the Past 12 Months by State and Puerto Rico: 2013

Source: U.S. Census Bureau, 2012 American Community Survey, 2013 Puerto Rico Community Survey. Alemayehu Bishaw, Poverrty: 2012 and 2013, U.S. Census Bureau, American Community Survey Briefs, ACSBR/13-0101, Washington, DC, September 2014, p. 4, http://census.gov/content/dam/Census/library/publications/2014/acs/acsbr13-01.pdf.

Figure 6 shows estimated poverty rates for the United States and for each of the 50 states and the District of Columbia on the basis of the 2013 American Community Survey (ACS), the most recent ACS data currently available. In addition to the point estimates, the figure displays a 90% statistical confidence interval around each state's estimate, indicating the degree to which these estimates might be expected to vary based on sample size. Although the states are sorted from lowest to highest by their respective poverty rate point estimates, the precise ranking of each state is not possible because of the depicted margin of error around each state's estimate. All states with non-overlapping statistical confidence intervals have statistically significant different poverty rates from one another. Some states with overlapping confidence intervals may also have significantly different poverty rates from one another, measured at the 90% confidence interval.13 For example, New Hampshire, shown as having the lowest poverty rate (8.7%) in 2013, is statistically tied with Alaska (9.3%). Mississippi clearly stands out as the state with the highest poverty rate (24.0%) and New Mexico, with a poverty rate of 21.8%, has the second-highest poverty rate. Louisiana, a state ranked as having the third-highest poverty rate (19.7%), is statistically tied with Arkansas (19.7%) and the District of Columbia (18.9%), but not with Georgia (19.0%), even though Louisiana and Georgia's statistical confidence intervals overlap.

Figure 6. Poverty Rates for the 50 States and the District of Columbia:
2013 American Community Survey (ACS) Data

Source: Prepared by the Congressional Research Service on the basis of U.S. Census Bureau 2013 American
Community Survey (ACS) data.


Change in State Poverty Rates: 2002-2013

Table 1 provides estimates of state and national poverty rates from 2002 through 2013 from the ACS. Statistically significant changes from one year to the next are indicated by an upward-pointing arrow (▲) if a state's poverty rate was statistically higher, and by a downward-pointing arrow (▼) if statistically lower, than in the immediately preceding year or for other selected periods (i.e., 2005 vs. 2002, 2013 vs. 2007).14 It should be noted that ACS poverty estimates for 2006 and later are not strictly comparable to those of earlier years, due to a change in ACS methodology that began in 2006 to include some persons living in non-institutionalized group quarters who were not included in earlier years.15

Table 1 shows that three states (New Jersey, New Mexico, and Washington) experienced statistically significant increases in their poverty rates from the 2012 to 2013 ACS. New Jersey's estimated poverty rate increased from 10.8% in 2012 to 11.4% in 2013, New Mexico's rate increased from 20.8% to 21.9%, and Washington's rate increased from 13.5% to 14.1%. Four states (Colorado, New Hampshire, Texas, and Wyoming) experienced statistically significant decreases in their poverty rates from 2012 to 2013.

The table shows that poverty among states generally increased over the 2002 to 2005 period, as measured by the ACS, consequent to the 2001 (March to November) economic recession. From the 2002 to 2003 ACS, five states (including the District of Columbia) experienced statistically significant increases in their poverty rates, whereas none experienced a statistically significant decrease. From 2003 to 2004, eight states saw their poverty rates increase, whereas two saw decreases. From 2004 to 2005, 13 states saw their poverty rates increase, whereas only 1 saw its poverty rate decrease. Comparing poverty rates from the 2005 ACS to those from the 2002 ACS, poverty was statistically higher in 22 states, and lower in only one.

By 2007, poverty rates among states were beginning to improve, with 13 states (including the District of Columbia) experiencing statistically significant declines in their poverty rates from 2006; only Michigan experienced a statistically significant increase in its poverty rate in 2007 compared to a year earlier.

Since 2007, state poverty rates have generally increased consequent to the 18-month recession (December 2007 to June 2009). From 2007 to 2008, the ACS data showed eight states (California, Connecticut, Florida, Hawaii, Indiana, Michigan, Oregon, and Pennsylvania) as experiencing statistically significant increases in their poverty rates, whereas three states (Alabama, Louisiana, and Texas) experienced statistically significant decreases. From 2008 to 2009, 32 states saw their poverty rates increase, and no state experienced a statistically significant decrease, and from 2009 to 2010, 34 states experienced statistically significant increases in poverty, and again, no state experienced a decrease. As noted above, from 2012 to 2013, three states saw their poverty rates rise, and four saw a decline. Comparing 2013 to 2007, poverty rates were statistically higher in 48 states (including the District of Columbia), and no state had a poverty rate statistically below its prerecession rate.

Table 1. Poverty Rates for the 50 States and the District of Columbia, 2002 to 2013
Estimates from the American Community Survey (ACS)

(percent poor)

 

Estimated Poverty Rates and Statistically Significant Differences over Previous Year

Change in Poverty Rates over Selected Periods and Statistically Significant Differencesa

 

2002

2003

2004

2005

2006b

2007b

2008b

2009b

2010b

2011b

2012b

2013b

2005

vs.

2001

2013

vs.

2007

United States

12.4

 

12.7

13.1

13.3

13.3

13.0

13.2

 

14.3

15.3

15.9

15.9

 

15.8

 

0.9

2.9

Alabama

16.6

 

17.1

16.1

17.0

16.6

16.9

15.7

17.5

19.0

19.0

 

19.0

 

18.7

 

0.4

1.9

Alaska

7.7

 

9.7

8.2

11.2

10.9

8.9

8.4

9.0

9.9

10.5

 

10.1

 

9.3

 

3.5

0.4

Arizona

14.2

 

15.4

14.2

14.2

14.2

14.2

14.7

16.5

17.4

19.0

18.7

 

18.6

 

0.0

4.5

Arkansas

15.3

 

16.0

17.9

17.2

17.3

17.9

17.3

18.8

18.8

19.5

 

19.8

 

19.7

 

1.9

1.8

California

13.0

 

13.4

13.3

13.3

13.1

12.4

13.3

14.2

15.8

16.6

17.0

16.8

 

0.3

4.4

Colorado

9.7

 

9.8

11.1

11.1

12.0

12.0

11.4

12.9

13.4

13.5

 

13.7

 

13.0

1.4

1.0

Connecticut

7.5

 

8.1

7.6

8.3

8.3

7.9

9.3

9.4

10.1

10.9

10.7

 

10.7

 

0.9

2.8

Delaware

8.2

 

8.7

9.9

10.4

11.1

10.5

10.0

10.8

11.8

11.9

 

12.0

 

12.4

 

2.2

1.9

Dist. of Col.

17.5

 

19.9

18.9

19.0

19.6

16.4

17.2

18.4

19.2

18.7

 

18.2

 

18.9

 

1.6

2.5

Florida

12.8

 

13.1

12.2

12.8

12.6

12.1

13.2

14.9

16.5

17.0

17.1

 

17.0

 

0.0

4.9

Georgia

12.7

 

13.4

14.8

14.4

14.7

14.3

14.7

16.5

17.9

19.1

19.2

 

19.0

 

1.7

4.7

Hawaii

10.1

 

10.9

10.6

9.8

9.3

8.0

9.1

10.4

10.7

12.0

11.6

 

10.8

 

(0.3)

2.9

Idaho

13.8

 

13.8

14.5

13.9

12.6

12.1

12.6

14.3

15.7

16.5

 

15.9

 

15.6

 

0.0

3.4

Illinois

11.6

 

11.3

11.9

12.0

12.3

11.9

12.2

13.3

13.8

15.0

14.7

 

14.7

 

0.4

2.7

Indiana

10.9

 

10.6

10.8

12.2

12.7

12.3

13.1

14.4

15.3

16.0

15.6

 

15.9

 

1.3

3.6

Iowa

11.2

 

10.1

9.9

10.9

11.0

11.0

11.5

11.8

12.6

12.8

 

12.7

 

12.7

 

(0.3)

1.6

Kansas

12.1

 

10.8

10.5

11.7

12.4

11.2

11.3

13.4

13.6

13.8

 

14.0

 

14.0

 

(0.4)

2.8

Kentucky

15.6

 

17.4

17.4

16.8

17.0

17.3

17.3

18.6

19.0

19.1

 

19.4

 

18.8

 

1.2

1.4

Louisiana

18.8

 

20.3

19.4

19.8

19.0

18.6

17.3

17.3

18.7

20.4

19.9

 

19.8

 

1.0

1.1

Maine

11.1

 

10.5

12.3

12.6

12.9

12.0

12.3

12.3

12.9

14.1

14.7

 

14.0

 

1.5

1.9

Maryland

8.1

 

8.2

8.8

8.2

7.8

8.3

8.1

9.1

9.9

10.1

 

10.3

 

10.1

 

0.2

1.8

Massachusetts

8.9

 

9.4

9.2

10.3

9.9

9.9

10.0

10.3

11.4

11.6

 

11.9

 

11.9

 

1.4

2.0

Michigan

11.0

 

11.4

12.3

13.2

13.5

14.0

14.4

16.2

16.8

17.5

17.4

 

17.0

 

2.2

3.0

Minnesota

8.5

 

7.8

8.3

9.2

9.8

9.5

9.6

11.0

11.6

11.9

 

11.4

11.2

 

0.6

1.7

Mississippi

19.9

 

19.9

21.6

21.3

21.1

20.6

21.2

21.9

22.4

22.6

 

24.2

24.0

 

1.5

3.4

Missouri

11.9

 

11.7

11.8

13.3

13.6

13.0

13.4

14.6

15.3

15.8

 

16.2

 

15.9

 

1.4

2.9

Montana

14.6

 

14.2

14.2

14.4

13.6

14.1

14.8

15.1

14.6

14.8

 

15.5

 

16.5

 

(0.3)

2.4

Nebraska

11.0

 

10.8

11.0

10.9

11.5

11.2

10.8

12.3

12.9

13.1

 

13.0

 

13.2

 

0.0

2.0

Nevada

11.8

 

11.5

12.6

11.1

10.3

10.7

11.3

12.4

14.9

15.9

 

16.4

 

15.8

 

(0.7)

5.1

New Hampshire

6.4

 

7.7

7.6

7.5

8.0

7.1

7.6

8.5

8.3

8.8

 

10.0

8.7

1.1

1.6

New Jersey

7.5

 

8.4

8.5

8.7

8.7

8.6

8.7

9.4

10.3

10.4

 

10.8

 

11.4

1.2

2.9

New Mexico

18.9

 

18.6

19.3

18.5

18.5

18.1

17.1

18.0

20.4

21.5

 

20.8

 

21.9

(0.4)

3.8

New York

13.1

 

13.5

14.2

13.8

14.2

13.7

13.6

14.2

14.9

16.0

15.9

 

16.0

 

0.7

2.3

North Carolina

14.2

 

14.0

15.2

15.1

14.7

14.3

14.6

16.3

17.5

17.9

 

18.0

 

17.9

 

0.8

3.6

North Dakota

12.5

 

11.7

12.1

11.2

11.4

12.1

12.0

11.7

13.0

12.2

 

11.2

 

11.8

 

(1.3)

(0.3)

 

Ohio

11.9

 

12.1

12.5

13.0

13.3

13.1

13.4

15.2

15.8

16.4

16.3

 

16.0

 

1.2

2.8

Oklahoma

15.0

 

16.1

15.3

16.5

17.0

15.9

15.9

16.2

16.9

17.2

 

17.2

 

16.8

 

1.5

0.9

Oregon

13.2

 

13.9

14.1

14.1

13.3

12.9

13.6

14.3

15.8

17.5

17.2

 

16.7

 

0.9

3.7

Pennsylvania

10.5

 

10.9

11.7

11.9

12.1

11.6

12.1

12.5

13.4

13.8

 

13.7

 

13.7

 

1.4

2.1

Rhode Island

10.7

 

11.3

12.8

12.3

11.1

12.0

11.7

11.5

14.0

14.7

 

13.7

 

14.3

 

1.6

2.3

South Carolina

14.2

 

14.1

15.7

15.6

15.7

15.0

15.7

17.1

18.2

18.9

18.3

 

18.6

 

1.3

3.5

South Dakota

11.4

 

11.1

11.0

13.6

13.6

13.1

12.5

14.2

14.4

13.9

 

13.4

 

14.2

 

2.3

1.1

 

Tennessee

14.5

 

13.8

14.5

15.5

16.2

15.9

15.5

17.1

17.7

18.3

 

17.9

 

17.8

 

1.0

1.9

Texas

15.6

 

16.3

16.6

17.6

16.9

16.3

15.8

17.2

17.9

18.5

17.9

17.5

2.0

1.3

Utah

10.5

 

10.6

10.9

10.2

10.6

9.7

9.6

11.5

13.2

13.5

 

12.8

 

12.7

 

(0.3)

3.0

Vermont

8.5

 

9.7

9.0

11.5

10.3

10.1

10.6

11.4

12.7

11.5

11.8

 

12.3

 

2.9

2.2

Virginia

9.9

 

9.0

9.5

10.0

9.6

9.9

10.2

10.5

11.1

11.5

11.7

 

11.7

 

0.0

1.8

Washington

11.4

 

11.0

13.1

11.9

11.8

11.4

11.3

12.3

13.4

13.9

 

13.5

 

14.1

0.5

2.7

West Virginia

17.2

 

18.5

17.9

18.0

17.3

16.9

17.0

17.7

18.1

18.6

 

17.8

 

18.5

 

0.8

1.6

Wisconsin

9.7

 

10.5

10.7

10.2

11.0

10.8

10.4

12.4

13.2

13.1

 

13.2

 

13.5

 

0.5

2.7

Wyoming

11.0

 

9.7

10.3

9.5

9.4

8.7

9.4

9.8

11.2

11.3

 

12.6

 

10.9

(1.5)

2.2

Number of states with statistically significant change in poverty:

 

 

5

 

10

14

7

14

11

32

 

34

 

18

 

5

 

7

 

23

 

48

Increase in poverty

 

 

5

8

13

4

1

8

32

34

17

3

3

22

48

Decrease in poverty

 

 

0

2

1

3

13

3

0

0

1

2

4

1

0

Source: Congressional Research Service (CRS) estimates from U.S. Census Bureau American Community Survey (ACS) data, 2002 to 2013.

Notes: ▲ Statistically significant increase in poverty rate at the 90% statistical confidence level.

▼ Statistically significant decrease in poverty rate at the 90% statistical confidence level.

Numbers in parentheses are negative.

a. Depicted changes in poverty rates over selected periods may differ slightly from differences calculated directly from the table, due to rounding.

b. Comparisons to 2002 through 2005 estimates are not strictly comparable, due to inclusion of persons living in some non-institutional group quarters beginning in 2006 and after.

Poverty Rates by Metropolitan Area

The four tables that follow provide poverty estimates for large metropolitan areas having a population of 500,000 and over, and for smaller metropolitan areas having a population of 50,000 or more but less than 500,000. Among large metropolitan areas, 10 areas with some of the lowest poverty rates are shown in Table 2, and the 10 areas with some of the highest poverty rates are shown in Table 3. Among smaller metropolitan areas, 10 areas with some of the lowest poverty rates are shown in Table 4, and 10 among those with the highest poverty rates in Table 5. It should be noted that metropolitan areas shown in these tables may not be statistically different from one another, or from others not shown in the tables. Poverty estimates for all metropolitan areas in 2013 are shown in Appendix B. Table B-1.

Table 2. Large Metropolitan Areas Among Those with the
Lowest Poverty Rates: 2013

(Metropolitan Areas with Population of 500,000 and Over)

 

 

Number Poor

Poverty Rate
(Percent Poor)

Metropolitan Area

Total Population

Estimate

Margin of Errora

Estimate

Margin of Errora

Washington-Arlington-Alexandria, DC-VA-MD-WV

5,846,655

495,683

+/-19,944

8.5%

+/-0.3%

Urban Honolulu, HI

951,718

89,684

+/-7,816

9.4%

+/-0.8%

Bridgeport-Stamford-Norwalk, CT

921,302

88,808

+/-6,895

9.6%

+/-0.7%

Minneapolis-St. Paul-Bloomington, MN-WI

3,397,278

349,161

+/-13,880

10.3%

+/-0.4%

Boston-Cambridge-Newton, MA-NH

4,525,102

470,178

+/-18,981

10.4%

+/-0.4%

Lancaster, PA

514,196

53,694

+/-5,804

10.4%

+/-1.1%

Ogden-Clearfield, UT

615,823

64,161

+/-7,360

10.4%

+/-1.2%

San Jose-Sunnyvale-Santa Clara, CA

1,891,182

198,842

+/-12,625

10.5%

+/-0.7%

Colorado Springs, CO

660,782

71,297

+/-7,162

10.8%

+/-1.1%

Hartford-West Hartford-East Hartford, CT

1,169,485

125,923

+/-9,009

10.8%

+/-0.8%

Source: Table prepared by the Congressional Research Service (CRS) based on analysis of U.S. Census Bureau 2012 American Community Survey (ACS) data, table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

Notes: Areas are included based on their estimated 2013 poverty rates. Areas shown may not be statistically different from one another, or from others not shown in the table.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical confidence interval bounding the estimate.

Table 3. Large Metropolitan Areas Among Those with the
Highest Poverty Rates: 2013

(Metropolitan Areas with Population of 500,000 and Over)

 

 

Number Poor

Poverty Rate
(Percent Poor)

Metropolitan Area

Total Population

Estimate

Margin of Errora

Estimate

Margin of Errora

McAllen-Edinburg-Mission, TX

803,934

275,681

+/-16,441

34.3%

+/-2.0%

Fresno, CA

937,990

270,072

+/-12,767

28.8%

+/-1.4%

Bakersfield, CA

831,344

189,484

+/-13,393

22.8%

+/-1.6%

El Paso, TX

816,158

184,427

+/-12,589

22.6%

+/-1.5%

Modesto, CA

518,152

114,628

+/-9,386

22.1%

+/-1.8%

Jackson, MS

557,607

122,754

+/-7,806

22.0%

+/-1.4%

Winston-Salem, NC

636,242

127,378

+/-10,165

20.0%

+/-1.6%

Greensboro-High Point, NC

722,405

143,646

+/-9,658

19.9%

+/-1.3%

Stockton-Lodi, CA

690,366

137,663

+/-9,607

19.9%

+/-1.4%

Augusta-Richmond County, GA-SC

565,819

111,863

+/-8,976

19.8%

+/-1.6%

Source: Table prepared by the Congressional Research Service (CRS) based on analysis of U.S. Census Bureau 2012 American Community Survey (ACS) data, table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

Notes: Areas are included based on their estimated 2013 poverty rates. Areas shown may not be statistically different from one another, or from others not shown in the table.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical confidence interval bounding the estimate.

Table 4. Smaller Metropolitan Areas Among Those with the
Lowest Poverty Rates: 2013

(Metropolitan Areas with Populations Between 50,000 and 499,999)

 

 

Number Poor

Poverty Rate
(Percent Poor)

Metropolitan Area

Total Population

Estimate

Margin of Errora

Estimate

Margin of Errora

California-Lexington Park, MD

106,530

6,831

+/-2,204

6.4%

+/-2.1%

Winchester, VA-WV

124,642

8,432

+/-1,934

6.8%

+/-1.5%

Anchorage, AK

386,833

27,596

+/-3,586

7.1%

+/-0.9%

Fairbanks, AK

96,578

7,442

+/-2,543

7.7%

+/-2.6%

Rochester, MN

208,650

16,523

+/-2,572

7.9%

+/-1.2%

Appleton, WI

226,221

18,291

+/-2,940

8.1%

+/-1.3%

Fond du Lac, WI

98,663

8,023

+/-1,707

8.1%

+/-1.7%

Bismarck, ND

121,277

10,119

+/-1,758

8.3%

+/-1.5%

Gettysburg, PA

97,009

8,620

+/-2,132

8.9%

+/-2.2%

Napa, CA

136,394

12,286

+/-2,875

9.0%

+/-2.1%

Source: Table prepared by the Congressional Research Service (CRS) based on analysis of U.S. Census Bureau 2012 American Community Survey (ACS) data, table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

Notes: Areas are included based on their estimated 2013 poverty rates. Areas shown may not be statistically different from one another, or from others not shown in the table.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical confidence interval bounding the estimate.

Table 5. Smaller Metropolitan Areas Among Those with the
Highest Poverty Rates: 2013

(Metropolitan Areas with Population of 500,000 and Over)

 

 

Number Poor

Poverty Rate
(Percent Poor)

Metropolitan Area

Total Population

Estimate

Margin of Errora

Estimate

Margin of Errora

Brownsville-Harlingen, TX

412,432

134,170

+/-8,943

32.5%

+/-2.2%

Laredo, TX

258,684

80,403

+/-7,285

31.1%

+/-2.8%

Visalia-Porterville, CA

448,360

135,066

+/-9,722

30.1%

+/-2.2%

Athens-Clarke County, GA

186,981

53,388

+/-5,015

28.6%

+/-2.6%

College Station-Bryan, TX

224,477

63,800

+/-6,284

28.4%

+/-2.8%

Las Cruces, NM

208,101

57,908

+/-6,390

27.8%

+/-3.1%

Valdosta, GA

139,018

37,443

+/-4,673

26.9%

+/-3.3%

Gainesville, FL

256,894

68,758

+/-5,496

26.8%

+/-2.1%

Greenville, NC

168,611

43,223

+/-5,197

25.6%

+/-3.1%

Monroe, LA

168,802

42,735

+/-5,063

25.3%

+/-3.0%

Source: Table prepared by the Congressional Research Service (CRS) based on analysis of U.S. Census Bureau 2012 American Community Survey (ACS) data, table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

Notes: Areas are included based on their estimated 2013 poverty rates. Areas shown may not be statistically different from one another, or from others not shown in the table.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical confidence interval bounding the estimate.

Congressional District Poverty Estimates

Poverty estimates for congressional districts are shown in Appendix C. Table C-1 includes poverty rate estimates for 2012. Congressional districts in 2012 are not directly comparable to earlier years, due to re-districting.

"Neighborhood" Poverty—Poverty Areas and Areas of Concentrated and Extreme Poverty

The estimates presented here are based on five years of American Community Survey (ACS) data (2009-2013 ACS).

Neighborhoods can be delineated from U.S. Census Bureau census tracts. Census tracts usually have between 2,500 and 8,000 persons and, when first delineated, are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The Census Bureau defines "poverty areas" as census tracts having poverty rates of 20% or more.

Figure 7 groups census tracts according to their level of poverty. The first two groupings are based on poor persons living in census tracts with poverty rates below the national average (15.4% based on the five-year ACS data), and from 15.4% to less than 20.0%. Poor persons living in census tracts with poverty rates of 20% or more meet the Census Bureau definition of living in "poverty areas." Poverty areas are further demarcated in terms of poor persons living in areas of "concentrated" poverty (i.e., census tracts with poverty rates of 30% to 39.9%), and areas of "extreme" poverty (i.e., census tracts with poverty rates of 40% or more). The figure is based on five years of data (2009-2013) from the U.S. Census Bureau's American Community Survey (ACS). Five years of data are required in order to get reasonably reliable statistical data at the census tract level while at the same time preserving the confidentiality of survey respondents.

Figure 7 shows that over the five-year period 2009-2013, over half of all poor persons (55.0%) lived in "poverty areas" (i.e., census tracts with poverty rates of 20% or more). Among the poor, about three out of ten (30.7%) lived in areas with poverty of 30% or more, and about one in seven (14.5%) lived in areas of "extreme" poverty, having poverty rates of 40% or more. Among the poor, African Americans, American Indian and Alaska Natives, and Hispanics are more likely to live in poverty areas than either Asians or white non-Hispanics. Among poor blacks, nearly half (48.0%) live in neighborhoods with poverty rates of 30% or more, and one-quarter (25.2%) live in "extreme" poverty areas, with poverty rates of 40% or more. Among poor Hispanics, about two-fifths (39.6%) live in areas with poverty rates of 30% or more, and about one in six (17.5%) live in areas of "extreme" poverty. Among poor white non-Hispanics, over half (53.2%) live outside poverty areas, while nearly one-quarter (23.2%) live in areas with poverty rates of 30% or more.

Figure 7. Distribution of Poor People by Race and Hispanic Origin,
by Level of Neighborhood (Census Tract) Poverty, 2009-2013

Source: Congressional Research Service (CRS) analysis of U.S. Census Bureau American Community Survey five-year (2009-2013) data.

The Research Supplemental Poverty Measure

On October 16, 2014, the Census Bureau released its fourth annual report using a new Supplemental Poverty Measure (SPM).16 As its name implies, the SPM is intended to "supplement," rather than replace, the "official" poverty measure. The "official" Census Bureau statistical measure of poverty will continue to be used by programs that allocate funds to states or other jurisdictions on the basis of poverty, and the Department of Health and Human Services (HHS) will continue to derive Poverty Income Guidelines from the "official" Census Bureau measure.

Many experts consider the "official" poverty measure to be flawed and outmoded.17 In 1990, Congress commissioned a study on how poverty is measured in the United States, resulting in the National Academy of Sciences (NAS) convening a 12-member expert panel to study the issue. The NAS panel issued a wide range of specific recommendations to develop an improved statistical measure of poverty in its 1995 report Measuring Poverty: A New Approach.18

In late 2009, the Office of Management and Budget (OMB) formed an Interagency Technical Working Group19 (ITWG) to suggest how the Census Bureau, in cooperation with the Bureau of Labor Statistics (BLS), should develop a new Supplemental Poverty Measure, using the NAS expert panel's recommendations as a starting point. Referencing the work of the ITWG,20 the Department of Commerce announced in March 2010 that the Census Bureau was developing a new Supplemental Poverty Measure, as "an alternative lens to understand poverty and measure the effects of anti-poverty policies," with the intention that the new measure "will be dynamic and will benefit from improvements over time based on new data and new methodologies."21

The SPM is intended to address a number of weaknesses of the "official" measure. Criticisms of the "official" poverty measure raised by the NAS expert panel include the following:

  • The "official" poverty measure, by counting only families' total cash, pre-tax income as a resource in determining poverty status, ignores a host of government programs and policies that affect the disposable income families may actually have available. For example, the official measure ignores the effects of payroll taxes paid by families, and tax benefits they may receive such as the EITC and the Child Tax Credit. It ignores a variety of in-kind benefits, such as SNAP benefits and free or reduced-price lunches under the National School Lunch Program, that free up resources to meet other needs. Similarly, it ignores housing subsidies that help make housing more affordable.
  • The "official" poverty income thresholds used in determining families' and individuals' poverty status, devised in the early 1960s, have changed little since. Except for minor technical changes and adjustments for price inflation, poverty income thresholds have essentially been frozen in time, reflecting living standards of a half-century ago.
  • The "official" poverty measure does not take into account necessary work-related expenses, such as child care and transportation costs that are associated with getting to work. Child care expenses are much more common today than when the "official" poverty measure was originally developed, as mothers' labor force participation has since increased.
  • The "official" poverty measure does not take into account medical expenses that individuals and families may incur, affecting their ability to meet other basic needs. These costs, which tend to vary by age, health status, and insurance coverage of individuals, may differentially affect families' abilities to meet other basic needs, especially given rising health care costs.
  • The "official" poverty measure does not take into account changing family situations, such as cohabitation among unmarried couples, or child support payments.
  • The "official" poverty measure does not adjust for differences in prices across geographic areas, which may affect the cost of living from one area to another.

The ITWG, using the NAS-panel recommendations as a starting point, suggested an approach to developing the SPM that addressed how income thresholds should be set and resources counted in measuring poverty. Conceptual differences between the "official" and supplemental poverty measures are summarized in Table 6.

Table 6. Poverty Measure Concepts Under "Official" and Supplemental Measures

 

"Official" Poverty Measure

Supplemental Poverty Measure

Measurement units

Families and unrelated individuals

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

Poverty threshold

Three times the cost of a minimum food diet in 1963

A range around the 33rd percentile (i.e., 30th to 36th percentile) of expenditures on food, shelter, clothing, and utilities (FCSU) for consumer units with exactly two children multiplied by 1.2 to account for other family needs (e.g., household supplies, personal care, non-transportation-related expenses)

Based on data from the U.S. Bureau of Labor Statistics Consumer Expenditure Survey (BLS CE)

Separate thresholds developed for
- homeowners with a mortgage,
- homeowners without a mortgage,
- renters

Threshold adjustments

Vary by family size, composition, and age of householder

A three parameter equivalence scale for number of adults and children in the family

Geographic adjustments for differences in housing costs

Updating thresholds

Consumer Price Index for Urban Consumers (CPI-U) based on all items

Five-year moving average of expenditures on FCSU from the BLS CE

Resource measures

Gross before-tax cash income

Sum of cash income
Plus in-kind benefits that families can use to meet their FCSU needs:

  • Supplemental Nutritional Assistance (SNAP)
  • National School Lunch Program
  • Supplementary Nutrition Program for Women, Infants, and Children (WIC)
  • Housing Subsidies
  • Low-Income Home Energy Assistance (LIHEAP)

Plus refundable tax credits:

  • Earned Income Tax Credit (EITC)
  • Refundable portion of the Child Tax Credit (CTC), known as the Additional Child Tax Credit (ACTC)

Minus nondiscretionary expenses:

  • federal and state income taxes
  • payroll taxes
  • work-related expenses, including work-related child care expenses
  • medical out-of-pocket expenses (MOOP), including insurance premiums paid
  • child support paid

Source: Congressional Research Service (CRS). Adapted from Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

The SPM incorporates a more comprehensive income/resource definition than that used by the "official" poverty measure, including in-kind benefits (e.g., SNAP) and refundable tax credits (e.g., EITC). It also expands upon the traditional family definition based on blood, marriage, and adoption to include cohabiting partners and their family relatives as part of a broader economic unit for assessing poverty status. The SPM subtracts necessary expenses (i.e., taxes, work-related expenses including child-care, child support paid, medical out-of-pocket [MOOP] expenses) from resources to arrive at a measure of an economic unit's disposable income/resources that may be applied to a standard of need based on food, clothing, shelter, and utilities (FCSU), plus "a little bit more" for everything else. The SPM income/resource thresholds are initially set at a range in the distribution (30th to 36th percentile) of what reference families (families with exactly two children) actually spend on FCSU. Separate thresholds are derived for homeowners with a mortgage and those without a mortgage, and for renters. Thresholds are adjusted for price differences in housing costs by geographic area (metropolitan and nonmetropolitan areas in a state). Thresholds for economic units other than initial reference units (i.e., those with exactly two children) are adjusted upwards or downwards for the number of adults and number of children in the unit.

Poverty Thresholds

As described earlier, the "official" U.S. poverty measure measures cash—pre-tax—income against income thresholds that vary by family size and composition. The thresholds were derived from research that showed that the average U.S. family spent one-third of its pre-tax income on food, based on a USDA 1955 Food Consumption Survey. After pricing minimally adequate food plans for families of varying sizes and compositions, poverty thresholds were derived by multiplying the cost of those food plans by a factor of three (i.e., one-third of the thresholds were assumed to address families' food needs, and two-thirds addressed everything else). The thresholds, established in 1963, are adjusted each year for price inflation.

SPM Poverty Thresholds

The SPM poverty thresholds are based on the NAS panel recommendation that thresholds be based on a point in the empirical distribution that "reference" families spend on food, clothing, shelter, and utilities (FCSU). Based on ITWG's suggestions, the Census Bureau derives FCSU thresholds for "reference" units with exactly two children, between the 30th and 36th percentile of what such units spend on FCSU, averaged over five years of survey data from the BLS Consumer Expenditure (CE) Survey.22 Whereas "official" poverty thresholds are based on initial thresholds adjusted for price changes over time, the SPM thresholds are based on changes in reference consumer units' actual spending on FCSU over time.

Following the ITWG's suggestion, three separate sets of thresholds are established: one set for homeowners with a mortgage, another set for homeowners without a mortgage, and a third set for renters. Following NAS panel recommendations, the ITWG suggested that initial poverty thresholds based on FCSU be multiplied by a factor of 1.2, to account for all other needs (e.g., household supplies, personal care, non-work-related transportation).23 Additionally, thresholds are adjusted upward and downward based on SPM reference unit size using a three parameter equivalence scale based on the number of adults and children in the unit.

Lastly, the thresholds are adjusted to account for variation in geographic price differences across metropolitan and nonmetropolitan areas, by state, based on differences in median housing costs across areas relative to the nation. The geographic housing cost adjustment is applied to the shelter portion of the FCSU-based thresholds.

Figure 8 depicts poverty threshold levels under the "official" poverty measure and under the Research SPM for a resource unit consisting of two adults and two children. The figure shows that in 2013, the official poverty threshold for a family with two adults and two children was $23,624. In comparison, for a similar family, the SPM poverty threshold for homeowners with a mortgage was $25,639, $2,015 (8.5%) above the official poverty threshold, and for homeowners without a mortgage, $21,397, or $2,227 (9.4%) below the official threshold. The SPM poverty threshold for renters was $25,144 or $1,520 (6.4%), above the official measure.

Figure 8. Poverty Thresholds Under the "Official" Measure and the
Research Supplemental Poverty Measure for Units with Two Adults and Two Children: 2013

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

Resources and Expenses Included in the SPM

As discussed earlier, the "official" poverty measure is based on counting families' and unrelated individuals' pre-tax cash income against poverty thresholds that vary by family size and composition. The SPM expands upon the pre-tax cash income resource definition used by the "official" measure to develop a more comprehensive measure of "disposable" income that SPM units might use to help meet basic needs (i.e., poverty thresholds based on FCSU, plus "a little more"). The SPM resource measure includes the value of a number of federal in-kind benefits, such as Supplemental Nutrition Assistance Program (SNAP, formerly Food Stamp) benefits; free and reduced-price school lunches; nutrition assistance for women, infants, and children (WIC); federal housing assistance; and energy assistance under the Low Income Home Energy Assistance Program (LIHEAP). It also includes federal tax benefits administered by the Internal Revenue Service, such as the Earned Income Tax Credit (EITC) and the partially refundable portion of the Child Tax Credit (CTC), known as the Additional Child Tax Credit (ACTC).

The SPM subtracts a number of necessary expenses from SPM units' resources to arrive at a measure of "disposable" income that units might have available to meet basic needs. Necessary expenses subtracted from resources on the SPM include child support paid; estimated federal, state, and local income taxes; estimated social security payroll (FICA) taxes; estimated work-related expenses other than child care (e.g., work-related commuting costs, purchase of uniforms or tools required for work); reported work-related child care expenses; and reported medical out of pocket (MOOP) expenses, including the employee share of health insurance premiums plus other medically necessary items such as prescription drugs and doctor copayments.

The effects of counting each of these resources and expenses in the SPM are assessed later in this report (see "Marginal Effects of Counting Specified Resources and Expenses on Poverty under the SPM").

Poverty Estimates Under the Research SPM Compared to the "Official" Measure

In 2013, the overall poverty rate was somewhat higher under the SPM (15.5%) than under an "adjusted official" poverty measure (14.6%)—"adjusted" to include unrelated children typically excluded from the "official" measure.24 In 2013, an estimated 48.671 million people were poor under the SPM, 2.9 million people more than the 45.748 million estimated under the "official" (adjusted) poverty measure. The remainder of this report focuses on differences in poverty rates among and between various groups under the two measures.

Poverty by Age

The SPM yields a very different impression of the incidence of poverty with respect to age than that portrayed by the "official" measure. Figure 9 compares poverty rates by age group under the SPM and the "official" measure in 2013. The poverty rate for adults ages 18 to 64 is somewhat higher under the SPM than under the "official" measure (15.4% compared to 13.6%). The figure shows that the poverty rate for children (under age 18) is lower under the SPM than under the "official" measure (16.4% compared to 20.4%). In contrast, the poverty rate among persons age 65 and over is much higher under the SPM than under the "official" measure (14.6% compared to 9.5%). Although the child poverty rate is lower under the SPM than under the "official" measure, and the aged poverty rate is considerably higher, the incidence of poverty among children still exceeds that of the aged under the SPM, as it did under the "official" measure. The SPM paints a much different picture of poverty among the aged than that conveyed by the "official" measure. As will be shown later, much of the difference between the aged poverty rate measured under the SPM compared to the "official" measure is attributable to the effect of medical expenses on the disposable income among aged units to meet basic needs represented by the SPM resource thresholds.

Figure 9. Poverty Rates Under the "Official"* and Research Supplemental Poverty Measures, by Age: 2013

(Percent poor)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

Note: * Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.

Poverty by Type of Economic Unit

As noted above, the SPM expands the definition of the economic unit considered for poverty measurement purposes over that used under the "official" poverty measure. The "official" poverty measure groups all co-residing household members related by marriage, birth, or adoption as sharing resources for purposes of poverty determination. Unrelated individuals, whether living alone as a single person household or with other unrelated members, are treated as separate economic units under the "official" poverty measure. The "official" measure also excludes unrelated children under age 15 from the universe for poverty determination. As noted earlier, the "adjusted official" poverty measure presented in this section of the report includes unrelated children, resulting in a 14.6% poverty rate as opposed to the published rate of 14.5% in 2013.

The SPM expands the economic unit used for poverty determination beyond that used by the "official" measure.25 The SPM assesses the relationship of unrelated household members to others in the household to determine whether they will be joined with others to construct expanded economic units. For example, the SPM combines unrelated co-residing household members age 14 and older who are not married and who identify each other as boyfriend, girlfriend, or partner as cohabiting partners. Cohabiting partners, as well as any of their co-resident family members, are combined as an economic unit under the SPM. The SPM also combines unmarried co-residing parents of a child living in the household as an economic unit, even if the parents do not identify as a cohabiting couple. Any unrelated children who are under age 15 and are not foster children are assigned to the householder's economic unit, as are foster children under the age of 22. Additionally, the SPM combines children over age 18 living in a household with a parent, and any younger children of the parent, as an economic unit. Under the "official" poverty measure, a child age 18 and over is treated as an unrelated individual, and the child's parent is also treated as an unrelated individual if no other family members are present, or as an unrelated subfamily head if a spouse or other children (under age 18) are also residing in the household.

In 2013, an estimated 27.953 million persons, 8.9% of the 313.395 million persons represented in the CPS/ASEC, were classified as either joining an economic unit or having members added to their economic unit under the SPM measure, compared to how they would have been classified under the "official" measure's economic unit definition. Combining the resources of these additional household members had the effect of reducing poverty under the SPM measure, compared to the "official" measure, in 2013.

Figure 10 shows poverty rates in 2013 by type of economic unit. Persons identified as being in a married-couple unit, or in female- or male-householder units, are persons in those economic units whose members remained unchanged under the SPM compared to the "official" poverty measure. Persons who were added to an economic unit, or were part of an economic unit that had members added to it under the SPM definition, are labeled as being in a "new SPM unit." The figure shows that poverty rates for persons in married-couple units, and in male-householder units, are higher under the SPM than under the "official" poverty measure (9.5% versus 6.7% for persons in married-couple units, and 23.1% versus 18.7% for persons in male-householder units). Poverty rates for persons living in female-householder units did not statistically differ from one another, with about three out of ten persons in such units considered poor under either measure. In contrast, poverty among persons who were members of "new SPM units" fell by about two-fifths, from 31.4% under the "official" measure to 17.9% under the SPM.

Figure 10. Poverty Rates Under the "Official"* and Research Supplemental Poverty Measures, by Type of Economic Unit: 2013

(Percent Poor)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.

Poverty by Region

Figure 11 compares poverty rates in 2013 under the SPM with the "official" measure by Census region. The figure shows that poverty rates in the West are considerably higher (26% higher) under the SPM (18.7%) than under the "official" measure (14.8%). Poverty rates are about 11% higher in the Northeast under the SPM (14.3%) compared to the "official" measure (12.8%). Poverty rates in the Midwest are lower under the SPM than under the "official" measure, and in the South, essentially equal. The differences in poverty rates within and between regions based on the SPM compared to the "official" measure are most directly due to the SPM's geographic price adjustments to poverty thresholds for differences in the cost of housing in metropolitan and nonmetropolitan areas across states. The cost of housing tends to be higher in the West and Northeast, causing their poverty rates to rise under the SPM relative to the "official" measure and relative to the South and Midwest, where housing tends to be less expensive.

Figure 11. Poverty Rates Under the "Official"* and Research Supplemental Poverty Measures, by Region: 2013

(Percent Poor)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.

Poverty by Residence

Figure 12 depicts poverty rates by residence in metropolitan (principal city, and outside principal city [i.e., "suburban"]) and nonmetropolitan areas in 2013.26 The figure shows that under the SPM, the poverty rate for persons living in Metropolitan Statistical Areas (MSAs) (15.9%) is somewhat higher than under the "official" measure (14.3%), whereas for persons living outside MSAs, the poverty rate is lower under the SPM (13.2%) than under the "official" measure (16.2%). Again, this most likely reflects differences in the cost of housing between MSAs and non-MSAs. Within MSAs, poverty rates are higher for persons living within principal cities under both measures than for people living outside them in "suburban" or "ex-urban" areas.

Figure 12. Poverty Rates Under the "Official"* and Research Supplemental Poverty Measures, by Residence: 2013

(Percent Poor)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.

Poverty by State

Figure 13 depicts states according to whether the state's SPM poverty rate statistically differs from its "official" poverty rate.27 Estimates are based on three-year (2011 to 2013) averages of CPS/ASEC data. Three years of data are combined in order to improve the statistical reliability of CPS/ASEC estimates at the state level. The figure shows that 13 states (Alaska, California, Connecticut, Florida, Hawaii, Illinois, Maryland, Massachusetts, Nevada, New Hampshire, New Jersey, New York, and Virginia) and the District of Columbia had higher poverty rates under the SPM than under the "official" measure. Among the 13 states with higher SPM poverty rates than their respective "official" poverty rate, only Illinois and Nevada were inland, and with the exception of Florida and Virginia, none were in the South. The figure shows that the SPM poverty rate was not statistically different than the "official" poverty rate in 11 states (Arizona, Colorado, Delaware, Georgia, Minnesota, Oregon, Pennsylvania, Rhode Island, Utah, Vermont, and Washington). Among the 26 remaining states in which their SPM poverty rates were lower than their respective "official" poverty rates, nearly all (with Maine being the exception) were either in the South, or inland.

Figure 13. Difference in Poverty Rates by State Using the "Official"* Measure and the SPM: Three-Year Average 2011-2013

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

Notes: Within state difference between official and SPM poverty rates determined at a 90% statistical
confidence level.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.

Figure 14 and Figure 15 depict poverty rates by state under the official poverty measure and the SPM based on three years of CPS/ASEC data. Estimates are based on three-year (2011 to 2013) averages to improve the statistical reliability of estimates attainable from CPS/ASEC data at the state level. The two figures differ only in terms of the order in which states are sorted. In Figure 14, states are sorted from lowest to highest based on their respective "official" poverty rate point estimates, whereas in Figure 15 states are sorted from lowest to highest based on their respective SPM poverty rate point estimates. In neither figure are precise rankings of states possible because of the depicted margin of error around each state's estimate. Within a state, a statistically significant difference28 between a state's official poverty rate and its SPM poverty rate is signified by solid-filled markers, indicating the point estimate under each measure, and a line connecting them, indicating the estimated difference (which is also shown in parentheses after each state name). The figures show the magnitude of the difference among the 13 states and the District of Columbia that had statistically significant higher poverty rates under the SPM than under the "official" measure, as well as for the 26 states in which the state's SPM rate was lower than its "official" poverty rate and the 11 states in which the incidence of poverty under the two measures did not differ statistically.

Differences in state poverty rates based on the SPM compared to the "official" measure may be due to a variety of factors. Geographic adjustments to SPM poverty income thresholds to account for differences in housing costs tend to result in higher poverty rates in areas with higher-priced housing than in areas with lower-priced housing. The mix of housing tenure (e.g., owner occupied, with or without a mortgage, renter occupied) may account for some of the difference between "official" and SPM poverty rates, within and between areas. Similarly, taxes may differ among areas. Also, populations may differ across areas in terms of household composition (e.g., share of households with cohabiting partners). The composition of the population based on age, or health insurance status, may also affect the incidence of SPM poverty relative to "official" poverty within and between geographic areas, by affecting medical out of pocket spending (MOOP), which is considered by SPM in estimating poverty.

Among the states with a statistically significant increase in poverty under the SPM, California's poverty rate increased by more than any other state's, increasing from 16.0% under the "official" measure to 23.4% under the SPM, or 7.4 percentage points. Under the "official" measure, California's poverty rate was substantially above the U.S. rate (14.6%), but under the SPM, California's poverty rate is estimated as the highest in the nation.

Other states with comparatively large increases in their poverty rates (in the four to five percentage point range) under the SPM compared to the "official" measure include Florida (a 15.1% to 19.1% increase), Hawaii (an increase from 12.4% to 18.4%), and New Jersey (a 10.7% to 15.9% increase).

Four states had decreases in their SPM poverty rate compared to their "official" rate in the four to five percentage point range. Among the states with the highest "official" poverty rates, New Mexico and Mississippi, (21.5% and 20.7%, respectively) both have estimated SPM poverty rates (16.0% and 15.3%, respectively) statistically tied with U.S. SPM rate (15.9%). Kentucky and West Virginia's "official" poverty rates (18.1% and 17.4%, respectively) are well above the "official" U.S. rate (14.9%), but their SPM poverty rates (13.8% and 13.2%) fall well below the U.S. SPM rate (15.9%).


Figure 14. Poverty Rates by State Using the "Official"* Measure and the SPM:
Three-Year Average 2010-2013

(States Ranked in Ascending Order by Official Poverty Rate; Percentage Point Difference in Parentheses)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the universe.

** Within state difference between official and SPM poverty rates determined at a 90% statistical confidence level.

Figure 15. Poverty Rates by State Using the "Official"* Measure and the SPM: Three-Year Average 2010-2013

(States Ranked in Ascending Order by SPM Poverty Rate; Percentage Point Difference in Parentheses)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the universe.

** Within state difference between official and SPM poverty rates determined at a 90% statistical confidence level.


Marginal Effects of Counting Specified Resources and Expenses on Poverty under the SPM

Figure 16 focuses strictly on the SPM, examining the marginal effects on poverty rates attributable to the inclusion of each selected income/resource or expenditure element on the measure. The marginal effects of each element on the SPM are displayed by age group. Elements that marginally contribute resources, and thereby have a poverty reducing effect when included in the SPM, are ranked from left to right in terms of their effect on poverty reduction among all persons. Similarly, expenditure elements, which are subtracted from resources and thereby marginally increase poverty as measured by the SPM, are ranked from left to right by their marginal poverty increasing effects on all persons.

The figure shows, for example, that the EITC has a greater poverty reducing effect than any of the other depicted resource elements. Overall, the EITC lowers the SPM poverty rate for all persons by 2.9 percentage points. The EITC is followed by SNAP benefits (1.6 percentage point reduction), housing subsidies (1.3 percentage point reduction), school lunch (0.5 percentage point reduction), and WIC (0.2 percentage point reduction) and LIHEAP (0.1 percentage point reduction).

In contrast, on the expenditure side, child support paid to members outside the household has a relatively small effect on increasing the overall poverty rate. Federal income taxes before considering refundable credits, such as the EITC (counted on the resource side), result in an increase in overall poverty of 0.4 percentage points. FICA payroll taxes have a larger effect on marginal poverty (1.5 percentage point increase) than federal income taxes, as do work expenses (1.9 percentage points). Among all of the expense elements presented, medical out of pocket expenses (MOOP) contribute to the largest increase in poverty (3.5 percentage point increase for all persons).

Among the three age groups, the additional resources included in the SPM have a greater effect on reducing poverty among children (persons under age 18) and poverty among working age adults (ages 18 to 64) than on the aged (age 65 and older), with the exception of housing subsidies, which reduce the aged poverty rate by about the same amount as that of children. The EITC has a greater effect of reducing poverty among children (6.4 percentage point reduction) than any of the other added SPM resources.

On the expenditure side, FICA payroll taxes and work expenses have a greater effect on increasing poverty among children (due to a working parent) and non-aged adults than on the aged, who are less likely to be in the labor force and incur work-related taxes and expenses. Notably, under the SPM, MOOP expenses contribute to a substantial increase in poverty among the aged, contributing to a 6.3 percentage point increase in their poverty rate.

The relative distribution of additional resources and expenses in the SPM by age group helps to explain why poverty among children is lower under the SPM than it is under the "official" measure, whereas it is considerably higher for the aged.

Figure 16. Percentage Point Change in Poverty Rates Attributable to Selected
Income and Expenditure Elements Under the Research Supplemental Poverty
Measure, by Age Group: 2013

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

Distribution of the Population by Ratio of Income/Resources Relative to Poverty

Figure 17 shows the distribution of the population by age group according to the degree to which their income and resources fall below or above poverty under the "official" and SPM definitions. The figure breaks out the poor population, depicted by brackets, into the share whose income and resources fall below half of their respective poverty lines (a classification sometimes referred to as "deep poverty") and the remainder. Others are categorized by the extent to which their income/resources exceed poverty under the two definitions, with those who fall below twice the poverty line also demarcated by brackets.

The figure shows, for example, that the share of children in "deep poverty" under the SPM is considerably lower than under the "official" measure (4.4% compared to 9.3%). As shown earlier, the SPM child poverty rate (16.4%) is lower than the "official" rate (20.3%). However, under the SPM, a much greater share of children live in "families" with income/resources between one and two times the poverty line than under the "official" measure (38.2% compared to 22.5%, respectively). Altogether, well over half of the children live in "families" having income/resources below twice the poverty line under the SPM (54.6%) compared to about two-fifths (42.8%) under the "official" measure. Thus, while the SPM appears to result in fewer children being counted as poor than under the "official" measure, under the SPM a greater share than under the "official" measure are concentrated at income levels just above poverty.

Among persons age 65 and over, a greater share are poor under the SPM than under the "official" measure, as shown earlier (14.6% compared to 9.5%), and a greater share are in "deep poverty" under the SPM (4.8%) than under the "official" measure (2.7%). In contrast to the "official" measure, under which one-third (33.1%) of the aged have income below 200% of poverty, somewhat under half (45.1%) have income/resources below that level under the SPM.

Figure 17. Distribution of the Population by Income/Resources to Poverty Ratios Under the "Official"* and Research Supplemental Poverty Measures, by Age Group: 2013

(Percent distribution)

Source: Figure prepared by the Congressional Research Service (CRS), based on Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

* Differs from published "official" poverty rates as unrelated individuals under age 15 are included in the
universe.


Discussion

As a research measure, the SPM offers potential for improved insight leading to better understanding of the nature and circumstances of those deemed to be among the nation's most economically and socially vulnerable. The SPM offers the means to better assess the performance of the economy, government policies, and programs with regard to the population's ability to secure sufficient income/resources to be able to meet basic expenditures for food, clothing, shelter, and utilities (plus "a little bit more").

The SPM counts considerably more elderly as poor than does the "official" measure. Medical expenses appear to be the driving factor in increasing poverty among the elderly under the SPM (see Figure 16). While not negating the improvement in the poverty status of the aged over the years, based on the "official" measure (see Figure 2), the SPM points more directly to the economic vulnerability of the aged, based not on income/resources alone, but rather, medical expenses competing for income that might otherwise be used to meet basic needs (i.e., FCSU plus "a little bit more"). Rising medical costs in society overall and individuals' personal health and insurance statuses pose potential economic risk to the aged being able to meet basic needs, as captured by FCSU-based poverty thresholds. The SPM provides additional insight that poverty reduction among the elderly depends not only on improving income, but also on their ability to reduce exposure to high medical expenses through "affordable" insurance. Rising medical costs in society also place the aged at increased risk of poverty under the SPM. It is worth noting that the SPM does not consider financial assets, other than interest, dividends, and annuity income from those assets, nor non-liquid assets (e.g., home equity) in determining poverty status. The SPM therefore does not address the means or extent to which the aged might tap those assets to meet medical or other needs.

The SPM results in fewer children being counted as poor than under the "official" measure. Still, the incidence of child poverty under the SPM, as under the "official" measure, exceeds that of the aged, but by a much slimmer margin (see Figure 9). Work-based supports, which both encourage work and help to offset the costs of going to work, appear be especially important to families with children, as captured by the SPM. The EITC, not counted under the "official" measure, significantly reduces child poverty as measured by the SPM, helping to offset taxes and work-related expenses working families with children incur (also captured by the SPM, but not under the "official" measure) (see Figure 16). The lack of safe, reliable, and affordable child care may limit parents' attachment to the labor force, contributing to poverty by reducing earnings that parents might otherwise secure. The SPM recognizes child care as a necessary expense many families face in their decisions relating to work by subtracting work-related child care expenses from income/resources that might otherwise go to meeting basic needs (i.e., FCSU plus "a little bit more"). As a consequence, the SPM should be sensitive to measuring the effects of child care programs and policies on child care affordability and poverty. The SPM captures the policy effects of assisting the poor through the provision of in-kind benefits, as opposed to just cash, whereas the "official" measure does not. For example, SNAP benefits, not captured under the "official" poverty measure, appear to have a sizeable effect in reducing child poverty under the SPM. Additionally, the expansion of the economic unit under the SPM to include cohabiting partners and their relatives may also contribute to lower child poverty rates under the SPM than under the "official" poverty measure, which is based on family ties defined by blood, marriage, and adoption.

Appendix A. U.S. Poverty Statistics: 1959-2013

Table A-1. Poverty Rates (Percent Poor) for Selected Groups, 1959-2013

 

Related Children
Under Age18a

Adults

Race/Ethnicityb—All Ages

Year

All
Persons

Total

In
Female-
Headed Families

In All
Other
Families

Ages 18-64

Age
65+

Whiteb

White
Non-Hispanicb

Blackb

Hispanic (any race)

Asianb

2013

14.5

19.5

45.8

10.7

13.6

9.5

12.3b

9.6b

27.2b

23.5b

10.5b

2012

15.0

21.3

47.2

12.5

13.7

9.1

12.7b

9.7b

27.2b

25.6

11.7b

2011

15.0

21.4

47.6

12.1

13.7

8.7

12.8b

9.8b

27.6b

25.3

12.3b

2010r

15.1

21.5

46.6

12.9

13.8

8.9

13.0b

9.9b

27.4b

26.5

12.2b

2009

14.3

20.1

44.4

12.3

12.9

8.9

12.3b

9.4b

25.8b

25.3

12.5b

2008

13.2

18.5

43.5

10.7

11.7

9.7

11.2b

8.6b

24.7b

23.2

11.8b

2007

12.5

17.6

43.0

9.5

10.9

9.7

10.5b

8.2b

24.5b

21.5

10.2b

2006

12.3

16.9

42.1

9.0

10.8

9.4

10.3b

8.2b

24.3b

20.6

10.3b

2005

12.6

17.1

42.8

9.3

11.1

10.1

10.6b

8.3b

24.9b

21.8

11.1b

2004r

12.7

17.3

41.9

9.7

11.3

9.8

10.8b

8.7b

24.7b

21.9

9.8b

2003

12.5

17.2

41.8

9.6

10.8

10.2

10.5b

8.2b

24.4b

22.5

11.8b

2002

12.1

16.3

39.6

9.2

10.6

10.4

10.2b

8.0b

24.1b

21.8

10.1b

2001

11.7

15.8

39.3

8.8

10.1

10.1

9.9

7.8

22.7

21.4

n/a

2000r

11.3

15.6

40.1

8.6

9.6

9.9

9.5

7.4

22.5

21.5

n/a

1999

11.8

16.3

41.9

9.0

10.0

9.7

9.8

7.7

23.6

22.8

n/a

1998

12.7

18.3

46.1

9.7

10.5

10.5

10.5

8.2

26.1

25.6

n/a

1997

13.3

19.2

49.0

10.2

10.9

10.5

11.0

8.6

26.5

27.1

n/a

1996

13.7

19.8

49.3

10.9

11.3

10.8

11.2

8.6

28.4

29.4

n/a

1995

13.8

20.2

50.3

10.7

11.4

10.5

11.2

8.5

29.3

30.3

n/a

1994

14.5

21.2

52.9

11.7

11.9

11.7

11.7

9.4

30.6

30.7

n/a

1993

15.1

22.0

53.7

12.4

12.4

12.2

12.2

9.9

33.1

30.6

n/a

1992r

14.8

21.6

54.6

11.8

11.9

12.9

11.9

9.6

33.4

29.6

n/a

1991r

14.2

21.1

55.5

11.1

11.4

12.4

11.3

9.4

32.7

28.7

n/a

1990

13.5

19.9

53.4

10.7

10.7

12.2

10.7

8.8

31.9

28.1

n/a

1989

12.8

19.0

51.1

10.4

10.2

11.4

10.0

8.3

30.7

26.2

n/a

1988r

13.0

19.0

52.9

10.0

10.5

12.0

10.1

8.4

31.3

26.7

n/a

1987r

13.4

19.7

54.7

10.9

10.6

12.5

10.4

8.7

32.4

28.0

n/a

1986

13.6

19.8

54.4

10.8

10.8

12.4

11.0

9.4

31.1

27.3

n/a

1985

14.0

20.1

53.6

11.7

11.3

12.6

11.4

9.7

31.3

29.0

n/a

1984

14.4

21.0

54.0

12.5

11.7

12.4

11.5

10.0

33.8

28.4

n/a

1983

15.2

21.8

55.5

13.5

12.4

13.8

12.2

10.8

35.7

28.1

n/a

1982

15.0

21.3

56.0

13.0

12.0

14.6

12.0

10.6

35.6

29.9

n/a

1981

14.0

19.5

52.3

11.6

11.1

15.3

11.1

9.9

34.2

26.5

n/a

1980

13.0

17.9

50.8

10.4

10.1

15.7

10.2

9.1

32.5

25.7

n/a

1979

11.7

16.0

48.6

8.5

8.9

15.2

9.0

8.1

31.0

21.8

n/a

1978

11.4

15.7

50.6

7.9

8.7

14.0

8.7

7.9

30.6

21.6

n/a

1977

11.6

16.0

50.3

8.5

8.8

14.1

8.9

8.0

31.3

22.4

n/a

1976

11.8

15.8

52.0

8.5

9.0

15.0

9.1

8.1

31.1

24.7

n/a

1975

12.3

16.8

52.7

9.8

9.2

15.3

9.7

8.6

31.3

26.9

n/a

1974

11.2

15.1

51.5

8.3

8.3

14.6

8.6

7.7

30.3

23.0

n/a

1973

11.1

14.2

52.1

7.6

8.3

16.3

8.4

7.5

31.4

21.9

n/a

1972

11.9

14.9

53.1

8.6

8.8

18.6

9.0

n/a

33.3

n/a

n/a

1971

12.5

15.1

53.1

9.3

9.3

21.6

9.9

n/a

32.5

n/a

n/a

1970

12.6

14.9

53.0

9.2

9.0

24.6

9.9

n/a

33.5

n/a

n/a

1969

12.1

13.8

54.4

8.6

8.7

25.3

9.5

n/a

32.2

n/a

n/a

1968

12.8

15.3

55.2

10.2

9.0

25.0

10.0

n/a

34.7

n/a

n/a

1967

14.2

16.3

54.3

11.5

10.0

29.5

11.0

n/a

39.3

n/a

n/a

1966

14.7

17.4

58.2

12.6

10.5

28.5

11.3

n/a

41.8

n/a

n/a

1959

22.4

26.9

72.2

22.4

17.0

35.2

18.1

n/a

55.1

n/a

n/a

Source: Prepared by the Congressional Research Service using U.S. Bureau of the Census data based on the "official" measure of poverty.

Notes: r = revised estimates. n/a = not available.

a. Beginning in 1979, restricted to children in primary families only. Before 1979, includes children in unrelated subfamilies.

b. Beginning in 2002, CPS respondents could identify themselves as being of more than one race. Consequently, racial data for 2002 and after are not comparable to earlier years. Here, in 2002 and after, the term white means of white race alone, the term black means of black race alone, and the term Asian means Asian alone. Hispanics, who may be of any race, are included among whites and blacks unless otherwise noted.

Appendix B. Metropolitan Area Poverty Estimates

Table B-1. Metropolitan Area Poverty: 2013

 

 

Number Poor

Poverty Rate (Percent Poor)

Metropolitan Area

Total Population

Estimate

Margin of Errora

Poverty Rate

Margin of Errora

Rankb

Abilene, TX

154,458

26,016

+/-3,491

16.8%

+/-2.2%

169

Akron, OH

690,331

106,377

+/-7,877

15.4%

+/-1.1%

237

Albany, GA

150,485

37,441

+/-4,405

24.9%

+/-2.8%

15

Albany, OR

117,252

23,986

+/-4,096

20.5%

+/-3.5%

60

Albany-Schenectady-Troy, NY

846,922

105,640

+/-8,545

12.5%

+/-1.0%

320

Albuquerque, NM

890,054

173,028

+/-10,925

19.4%

+/-1.2%

86

Alexandria, LA

147,861

27,656

+/-3,989

18.7%

+/-2.7%

110

Allentown-Bethlehem-Easton, PA-NJ

804,393

99,692

+/-6,980

12.4%

+/-0.9%

324

Altoona, PA

123,730

20,392

+/-3,212

16.5%

+/-2.6%

182

Amarillo, TX

249,194

39,748

+/-3,969

16.0%

+/-1.6%

212

Ames, IA

84,045

19,770

+/-2,273

23.5%

+/-2.6%

22

Anchorage, AK

386,833

27,596

+/-3,586

7.1%

+/-0.9%

379

Ann Arbor, MI

335,915

56,191

+/-5,089

16.7%

+/-1.5%

175

Anniston-Oxford-Jacksonville, AL

113,722

24,825

+/-3,340

21.8%

+/-2.9%

38

Appleton, WI

226,221

18,291

+/-2,940

8.1%

+/-1.3%

376

Asheville, NC

429,282

68,399

+/-5,793

15.9%

+/-1.4%

214

Athens-Clarke County, GA

186,981

53,388

+/-5,015

28.6%

+/-2.6%

6

Atlanta-Sandy Springs-Roswell, GA

5,430,037

865,858

+/-28,129

15.9%

+/-0.5%

213

Atlantic City-Hammonton, NJ

270,136

48,716

+/-5,187

18.0%

+/-1.9%

123

Auburn-Opelika, AL

144,867

30,038

+/-4,160

20.7%

+/-2.9%

56

Augusta-Richmond County, GA-SC

565,819

111,863

+/-8,976

19.8%

+/-1.6%

80

Austin-Round Rock, TX

1,841,572

262,644

+/-14,918

14.3%

+/-0.8%

281

Bakersfield, CA

831,344

189,484

+/-13,393

22.8%

+/-1.6%

26

Baltimore-Columbia-Towson, MD

2,702,706

301,630

+/-13,812

11.2%

+/-0.5%

344

Bangor, ME

146,466

23,644

+/-3,195

16.1%

+/-2.2%

200

Barnstable Town, MA

212,139

19,313

+/-2,984

9.1%

+/-1.4%

368

Baton Rouge, LA

797,912

149,025

+/-10,622

18.7%

+/-1.3%

111

Battle Creek, MI

130,542

24,261

+/-3,240

18.6%

+/-2.4%

113

Bay City, MI

105,498

18,310

+/-2,533

17.4%

+/-2.4%

145

Beaumont-Port Arthur, TX

387,482

72,048

+/-7,227

18.6%

+/-1.8%

112

Beckley, WV

118,651

25,833

+/-3,422

21.8%

+/-2.8%

40

Bellingham, WA

200,426

34,135

+/-4,708

17.0%

+/-2.3%

160

Bend-Redmond, OR

164,655

26,397

+/-4,828

16.0%

+/-2.9%

207

Billings, MT

161,276

20,745

+/-2,832

12.9%

+/-1.7%

310

Binghamton, NY

236,898

38,784

+/-4,249

16.4%

+/-1.8%

189

Birmingham-Hoover, AL

1,116,257

188,610

+/-9,521

16.9%

+/-0.9%

166

Bismarck, ND

121,277

10,119

+/-1,758

8.3%

+/-1.5%

374

Blacksburg-Christiansburg-Radford, VA

166,843

37,896

+/-4,544

22.7%

+/-2.6%

27

Bloomington, IL

184,309

27,681

+/-3,555

15.0%

+/-1.9%

249

Bloomington, IN

148,709

33,760

+/-3,426

22.7%

+/-2.2%

28

Bloomsburg-Berwick, PA

80,653

13,275

+/-2,443

16.5%

+/-3.0%

183

Boise City, ID

637,683

107,713

+/-12,906

16.9%

+/-2.0%

167

Boston-Cambridge-Newton, MA-NH

4,525,102

470,178

+/-18,981

10.4%

+/-0.4%

357

Boulder, CO

300,101

41,700

+/-4,077

13.9%

+/-1.4%

287

Bowling Green, KY

156,092

30,727

+/-3,873

19.7%

+/-2.4%

82

Bremerton-Silverdale, WA

245,971

27,727

+/-4,028

11.3%

+/-1.6%

342

Bridgeport-Stamford-Norwalk, CT

921,302

88,808

+/-6,895

9.6%

+/-0.7%

359

Brownsville-Harlingen, TX

412,432

134,170

+/-8,943

32.5%

+/-2.2%

2

Brunswick, GA

111,440

22,111

+/-4,204

19.8%

+/-3.8%

76

Buffalo-Cheektowaga-Niagara Falls, NY

1,103,165

164,100

+/-8,568

14.9%

+/-0.8%

257

Burlington, NC

150,206

31,103

+/-4,266

20.7%

+/-2.8%

57

Burlington-South Burlington, VT

205,647

21,596

+/-3,045

10.5%

+/-1.5%

353

California-Lexington Park, MD

106,530

6,831

+/-2,204

6.4%

+/-2.1%

381

Canton-Massillon, OH

394,097

61,713

+/-5,716

15.7%

+/-1.4%

223

Cape Coral-Fort Myers, FL

649,199

107,225

+/-8,880

16.5%

+/-1.4%

181

Cape Girardeau, MO-IL

91,588

16,457

+/-2,819

18.0%

+/-2.9%

124

Carbondale-Marion, IL

120,496

27,530

+/-3,465

22.8%

+/-2.8%

25

Carson City, NV

52,168

7,885

+/-2,319

15.1%

+/-4.4%

245

Casper, WY

79,240

7,448

+/-1,658

9.4%

+/-2.1%

364

Cedar Rapids, IA

255,759

23,609

+/-3,504

9.2%

+/-1.4%

367

Chambersburg-Waynesboro, PA

148,856

19,211

+/-3,790

12.9%

+/-2.5%

305

Champaign-Urbana, IL

217,009

44,185

+/-3,690

20.4%

+/-1.7%

62

Charleston, WV

220,824

36,049

+/-4,747

16.3%

+/-2.1%

191

Charleston-North Charleston, SC

693,815

112,715

+/-7,581

16.2%

+/-1.1%

194

Charlotte-Concord-Gastonia, NC-SC

2,298,466

339,434

+/-15,265

14.8%

+/-0.7%

263

Charlottesville, VA

211,108

33,811

+/-4,219

16.0%

+/-2.0%

208

Chattanooga, TN-GA

527,350

85,002

+/-7,650

16.1%

+/-1.4%

202

Cheyenne, WY

93,972

8,952

+/-2,894

9.5%

+/-3.1%

361

Chicago-Naperville-Elgin, IL-IN-WI

9,375,444

1,347,179

+/-32,543

14.4%

+/-0.3%

277

Chico, CA

217,808

46,895

+/-5,012

21.5%

+/-2.3%

45

Cincinnati, OH-KY-IN

2,084,132

301,214

+/-13,602

14.5%

+/-0.7%

273

Clarksville, TN-KY

262,145

42,952

+/-4,799

16.4%

+/-1.8%

187

Cleveland, TN

116,431

23,016

+/-4,071

19.8%

+/-3.5%

81

Cleveland-Elyria, OH

2,023,498

315,381

+/-14,229

15.6%

+/-0.7%

226

Coeur d'Alene, ID

142,546

17,161

+/-3,928

12.0%

+/-2.8%

330

College Station-Bryan, TX

224,477

63,800

+/-6,284

28.4%

+/-2.8%

7

Colorado Springs, CO

660,782

71,297

+/-7,162

10.8%

+/-1.1%

350

Columbia, MO

161,119

34,118

+/-3,949

21.2%

+/-2.4%

52

Columbia, SC

757,614

125,517

+/-9,093

16.6%

+/-1.2%

180

Columbus, GA-AL

299,327

64,754

+/-6,177

21.6%

+/-2.0%

43

Columbus, IN

77,877

9,387

+/-2,413

12.1%

+/-3.1%

329

Columbus, OH

1,913,546

283,702

+/-15,369

14.8%

+/-0.8%

258

Corpus Christi, TX

436,129

75,592

+/-7,264

17.3%

+/-1.6%

146

Corvallis, OR

81,212

18,762

+/-2,296

23.1%

+/-2.8%

23

Crestview-Fort Walton Beach-Destin, FL

246,364

38,598

+/-5,626

15.7%

+/-2.3%

222

Cumberland, MD-WV

93,006

16,404

+/-2,954

17.6%

+/-3.2%

136

Dallas-Fort Worth-Arlington, TX

6,724,464

1,005,325

+/-30,615

15.0%

+/-0.5%

253

Dalton, GA

140,291

30,592

+/-4,719

21.8%

+/-3.4%

39

Danville, IL

77,461

14,964

+/-2,398

19.3%

+/-3.1%

90

Daphne-Fairhope-Foley, AL

192,943

28,028

+/-5,351

14.5%

+/-2.8%

270

Davenport-Moline-Rock Island, IA-IL

373,851

54,024

+/-5,283

14.5%

+/-1.4%

274

Dayton, OH

776,921

127,254

+/-9,611

16.4%

+/-1.2%

188

Decatur, AL

150,726

26,408

+/-3,888

17.5%

+/-2.6%

139

Decatur, IL

105,437

19,243

+/-3,025

18.3%

+/-2.9%

119

Deltona-Daytona Beach-Ormond Beach, FL

589,119

95,566

+/-8,042

16.2%

+/-1.4%

195

Denver-Aurora-Lakewood, CO

2,663,509

323,179

+/-15,703

12.1%

+/-0.6%

328

Des Moines-West Des Moines, IA

588,147

64,790

+/-5,793

11.0%

+/-1.0%

346

Detroit-Warren-Dearborn, MI

4,252,247

717,584

+/-17,780

16.9%

+/-0.4%

168

Dothan, AL

146,190

26,816

+/-2,595

18.3%

+/-1.8%

116

Dover, DE

164,302

20,334

+/-3,558

12.4%

+/-2.2%

325

Dubuque, IA

92,158

12,633

+/-1,868

13.7%

+/-2.0%

291

Duluth, MN-WI

269,518

45,693

+/-4,614

17.0%

+/-1.7%

163

Durham-Chapel Hill, NC

510,288

86,378

+/-6,899

16.9%

+/-1.3%

165

East Stroudsburg, PA

164,528

17,845

+/-3,781

10.8%

+/-2.3%

349

Eau Claire, WI

157,876

18,956

+/-3,155

12.0%

+/-2.0%

332

El Centro, CA

165,902

36,645

+/-5,905

22.1%

+/-3.5%

35

El Paso, TX

816,158

184,427

+/-12,589

22.6%

+/-1.5%

30

Elizabethtown-Fort Knox, KY

147,225

23,253

+/-3,377

15.8%

+/-2.3%

220

Elkhart-Goshen, IN

195,903

31,743

+/-5,292

16.2%

+/-2.7%

197

Elmira, NY

83,345

14,217

+/-2,131

17.1%

+/-2.6%

158

Erie, PA

267,946

49,005

+/-5,936

18.3%

+/-2.2%

118

Eugene, OR

349,317

75,232

+/-7,088

21.5%

+/-2.0%

44

Evansville, IN-KY

305,403

49,315

+/-5,336

16.1%

+/-1.7%

199

Fairbanks, AK

96,578

7,442

+/-2,543

7.7%

+/-2.6%

378

Fargo, ND-MN

214,216

29,879

+/-3,940

13.9%

+/-1.8%

285

Farmington, NM

125,488

28,442

+/-4,450

22.7%

+/-3.5%

29

Fayetteville, NC

365,455

68,554

+/-5,288

18.8%

+/-1.4%

106

Fayetteville-Springdale-Rogers, AR-MO

480,149

80,859

+/-8,372

16.8%

+/-1.7%

170

Flagstaff, AZ

127,378

30,726

+/-3,789

24.1%

+/-2.9%

17

Flint, MI

409,193

88,579

+/-7,484

21.6%

+/-1.8%

42

Florence, SC

201,368

46,093

+/-5,753

22.9%

+/-2.9%

24

Florence-Muscle Shoals, AL

144,987

23,034

+/-2,993

15.9%

+/-2.1%

218

Fond du Lac, WI

98,663

8,023

+/-1,707

8.1%

+/-1.7%

375

Fort Collins, CO

307,412

43,846

+/-4,203

14.3%

+/-1.4%

280

Fort Smith, AR-OK

275,581

65,557

+/-6,172

23.8%

+/-2.2%

19

Fort Wayne, IN

416,163

66,755

+/-5,712

16.0%

+/-1.4%

206

Fresno, CA

937,990

270,072

+/-12,767

28.8%

+/-1.4%

5

Gadsden, AL

102,633

19,363

+/-3,161

18.9%

+/-3.1%

102

Gainesville, FL

256,894

68,758

+/-5,496

26.8%

+/-2.1%

10

Gainesville, GA

185,118

40,630

+/-5,458

21.9%

+/-2.9%

37

Gettysburg, PA

97,009

8,620

+/-2,132

8.9%

+/-2.2%

372

Glens Falls, NY

124,199

15,784

+/-2,676

12.7%

+/-2.2%

316

Goldsboro, NC

120,867

25,910

+/-5,137

21.4%

+/-4.2%

47

Grand Forks, ND-MN

94,728

14,555

+/-1,687

15.4%

+/-1.8%

238

Grand Island, NE

81,981

12,340

+/-2,849

15.1%

+/-3.5%

246

Grand Junction, CO

143,253

23,910

+/-4,425

16.7%

+/-3.1%

176

Grand Rapids-Wyoming, MI

993,281

139,139

+/-8,997

14.0%

+/-0.9%

284

Grants Pass, OR

82,361

14,035

+/-3,095

17.0%

+/-3.8%

159

Great Falls, MT

80,102

12,814

+/-2,715

16.0%

+/-3.4%

210

Greeley, CO

263,036

35,126

+/-4,926

13.4%

+/-1.9%

300

Green Bay, WI

304,580

36,549

+/-5,101

12.0%

+/-1.7%

333

Greensboro-High Point, NC

722,405

143,646

+/-9,658

19.9%

+/-1.3%

75

Greenville, NC

168,611

43,223

+/-5,197

25.6%

+/-3.1%

11

Greenville-Anderson-Mauldin, SC

826,492

143,919

+/-11,385

17.4%

+/-1.4%

142

Gulfport-Biloxi-Pascagoula, MS

375,050

72,312

+/-7,842

19.3%

+/-2.1%

93

Hagerstown-Martinsburg, MD-WV

246,865

30,667

+/-4,873

12.4%

+/-2.0%

322

Hammond, LA

121,122

26,234

+/-4,042

21.7%

+/-3.3%

41

Hanford-Corcoran, CA

133,031

28,473

+/-5,298

21.4%

+/-4.0%

48

Harrisburg-Carlisle, PA

538,015

61,268

+/-5,964

11.4%

+/-1.1%

339

Harrisonburg, VA

119,953

20,308

+/-3,245

16.9%

+/-2.7%

164

Hartford-West Hartford-East Hartford, CT

1,169,485

125,923

+/-9,009

10.8%

+/-0.8%

351

Hattiesburg, MS

144,861

34,291

+/-4,546

23.7%

+/-3.1%

20

Hickory-Lenoir-Morganton, NC

356,214

61,715

+/-6,542

17.3%

+/-1.8%

148

Hilton Head Island-Bluffton-Beaufort, SC

192,499

30,949

+/-5,259

16.1%

+/-2.7%

204

Hinesville, GA

79,128

16,111

+/-3,079

20.4%

+/-3.9%

63

Homosassa Springs, FL

136,633

22,952

+/-3,284

16.8%

+/-2.4%

172

Hot Springs, AR

94,437

22,668

+/-3,723

24.0%

+/-3.9%

18

Houma-Thibodaux, LA

205,658

27,916

+/-4,139

13.6%

+/-2.0%

292

Houston-The Woodlands-Sugar Land, TX

6,228,091

1,021,922

+/-32,157

16.4%

+/-0.5%

184

Huntington-Ashland, WV-KY-OH

354,931

71,701

+/-6,538

20.2%

+/-1.8%

67

Huntsville, AL

423,978

63,797

+/-6,818

15.0%

+/-1.6%

247

Idaho Falls, ID

135,972

15,189

+/-3,087

11.2%

+/-2.3%

343

Indianapolis-Carmel-Anderson, IN

1,909,800

290,647

+/-12,942

15.2%

+/-0.7%

242

Iowa City, IA

152,657

23,856

+/-3,159

15.6%

+/-2.1%

224

Ithaca, NY

88,377

17,907

+/-2,704

20.3%

+/-2.9%

66

Jackson, MI

150,916

29,064

+/-3,814

19.3%

+/-2.5%

94

Jackson, MS

557,607

122,754

+/-7,806

22.0%

+/-1.4%

36

Jackson, TN

125,360

26,178

+/-3,335

20.9%

+/-2.7%

54

Jacksonville, FL

1,366,441

202,025

+/-12,483

14.8%

+/-0.9%

262

Jacksonville, NC

170,510

28,935

+/-4,900

17.0%

+/-2.8%

161

Janesville-Beloit, WI

156,924

22,915

+/-4,090

14.6%

+/-2.6%

268

Jefferson City, MO

138,359

18,375

+/-3,729

13.3%

+/-2.7%

302

Johnson City, TN

193,692

37,292

+/-4,251

19.3%

+/-2.1%

95

Johnstown, PA

132,298

21,707

+/-2,741

16.4%

+/-2.1%

185

Jonesboro, AR

121,308

25,933

+/-3,668

21.4%

+/-3.1%

50

Joplin, MO

171,028

29,190

+/-4,347

17.1%

+/-2.6%

157

Kahului-Wailuku-Lahaina, HI

158,710

15,013

+/-2,564

9.5%

+/-1.6%

362

Kalamazoo-Portage, MI

322,236

57,240

+/-5,097

17.8%

+/-1.6%

129

Kankakee, IL

107,450

18,358

+/-3,669

17.1%

+/-3.4%

155

Kansas City, MO-KS

2,018,783

255,291

+/-12,778

12.6%

+/-0.6%

318

Kennewick-Richland, WA

266,874

38,878

+/-5,751

14.6%

+/-2.2%

269

Killeen-Temple, TX

401,026

57,065

+/-7,797

14.2%

+/-1.9%

282

Kingsport-Bristol-Bristol, TN-VA

302,495

54,895

+/-5,958

18.1%

+/-2.0%

121

Kingston, NY

173,358

19,549

+/-4,087

11.3%

+/-2.4%

341

Knoxville, TN

831,129

145,567

+/-9,055

17.5%

+/-1.1%

140

Kokomo, IN

81,130

12,612

+/-2,234

15.5%

+/-2.7%

228

La Crosse-Onalaska, WI-MN

130,300

20,554

+/-3,101

15.8%

+/-2.4%

221

Lafayette, LA

468,912

76,884

+/-8,310

16.4%

+/-1.8%

186

Lafayette-West Lafayette, IN

194,061

37,427

+/-5,210

19.3%

+/-2.6%

92

Lake Charles, LA

198,778

30,927

+/-4,825

15.6%

+/-2.4%

227

Lake Havasu City-Kingman, AZ

195,730

41,429

+/-6,226

21.2%

+/-3.1%

53

Lakeland-Winter Haven, FL

608,424

118,007

+/-11,131

19.4%

+/-1.8%

87

Lancaster, PA

514,196

53,694

+/-5,804

10.4%

+/-1.1%

355

Lansing-East Lansing, MI

447,127

80,872

+/-7,023

18.1%

+/-1.6%

122

Laredo, TX

258,684

80,403

+/-7,285

31.1%

+/-2.8%

3

Las Cruces, NM

208,101

57,908

+/-6,390

27.8%

+/-3.1%

8

Las Vegas-Henderson-Paradise, NV

2,002,803

321,455

+/-16,823

16.1%

+/-0.8%

205

Lawrence, KS

105,235

17,967

+/-4,054

17.1%

+/-3.8%

156

Lawton, OK

121,949

24,842

+/-3,444

20.4%

+/-2.8%

61

Lebanon, PA

131,958

14,367

+/-2,930

10.9%

+/-2.2%

348

Lewiston, ID-WA

60,924

8,151

+/-2,133

13.4%

+/-3.5%

299

Lewiston-Auburn, ME

104,601

17,884

+/-3,007

17.1%

+/-2.9%

154

Lexington-Fayette, KY

472,058

80,728

+/-6,536

17.1%

+/-1.4%

153

Lima, OH

101,118

15,154

+/-2,407

15.0%

+/-2.4%

251

Lincoln, NE

302,836

46,833

+/-5,684

15.5%

+/-1.8%

232

Little Rock-North Little Rock-Conway, AR

711,357

107,972

+/-9,231

15.2%

+/-1.3%

244

Logan, UT-ID

125,695

18,371

+/-3,207

14.6%

+/-2.5%

266

Longview, TX

207,330

39,098

+/-5,262

18.9%

+/-2.5%

103

Longview, WA

100,113

14,491

+/-3,004

14.5%

+/-3.0%

272

Los Angeles-Long Beach-Anaheim, CA

12,940,754

2,283,272

+/-40,149

17.6%

+/-0.3%

135

Louisville/Jefferson County, KY-IN

1,237,895

171,328

+/-12,460

13.8%

+/-1.0%

288

Lubbock, TX

292,742

51,653

+/-5,743

17.6%

+/-1.9%

134

Lynchburg, VA

247,740

38,287

+/-5,316

15.5%

+/-2.1%

234

Macon, GA

221,779

55,647

+/-5,641

25.1%

+/-2.5%

14

Madera, CA

144,954

34,242

+/-5,853

23.6%

+/-4.0%

21

Madison, WI

612,386

82,323

+/-6,973

13.4%

+/-1.1%

297

Manchester-Nashua, NH

395,786

38,127

+/-5,228

9.6%

+/-1.3%

360

Manhattan, KS

88,998

18,070

+/-2,763

20.3%

+/-3.0%

65

Mankato-North Mankato, MN

92,795

15,470

+/-2,101

16.7%

+/-2.2%

177

Mansfield, OH

114,496

20,114

+/-3,059

17.6%

+/-2.6%

138

McAllen-Edinburg-Mission, TX

803,934

275,681

+/-16,441

34.3%

+/-2.0%

1

Medford, OR

205,687

38,784

+/-7,040

18.9%

+/-3.4%

104

Memphis, TN-MS-AR

1,319,206

261,291

+/-11,676

19.8%

+/-0.9%

77

Merced, CA

256,177

64,552

+/-6,551

25.2%

+/-2.6%

13

Miami-Fort Lauderdale-West Palm Beach, FL

5,751,004

1,017,832

+/-27,848

17.7%

+/-0.5%

131

Michigan City-La Porte, IN

101,722

17,699

+/-3,213

17.4%

+/-3.2%

143

Midland, MI

82,183

13,625

+/-2,449

16.6%

+/-3.0%

179

Midland, TX

153,451

14,293

+/-3,501

9.3%

+/-2.3%

366

Milwaukee-Waukesha-West Allis, WI

1,539,233

244,752

+/-10,718

15.9%

+/-0.7%

217

Minneapolis-St. Paul-Bloomington, MN-WI

3,397,278

349,161

+/-13,880

10.3%

+/-0.4%

358

Missoula, MT

108,797

19,469

+/-3,626

17.9%

+/-3.3%

125

Mobile, AL

404,637

80,960

+/-7,633

20.0%

+/-1.9%

72

Modesto, CA

518,152

114,628

+/-9,386

22.1%

+/-1.8%

34

Monroe, LA

168,802

42,735

+/-5,063

25.3%

+/-3.0%

12

Monroe, MI

147,322

18,984

+/-2,984

12.9%

+/-2.0%

307

Montgomery, AL

363,458

69,589

+/-6,497

19.1%

+/-1.8%

97

Morgantown, WV

126,795

24,361

+/-2,922

19.2%

+/-2.3%

96

Morristown, TN

112,273

19,831

+/-3,735

17.7%

+/-3.3%

133

Mount Vernon-Anacortes, WA

116,391

20,682

+/-3,644

17.8%

+/-3.1%

128

Muncie, IN

110,512

24,950

+/-2,907

22.6%

+/-2.6%

31

Muskegon, MI

163,873

33,809

+/-3,737

20.6%

+/-2.3%

59

Myrtle Beach-Conway-North Myrtle Beach, SC-NC

400,485

73,380

+/-6,568

18.3%

+/-1.6%

117

Napa, CA

136,394

12,286

+/-2,875

9.0%

+/-2.1%

369

Naples-Immokalee-Marco Island, FL

336,570

43,152

+/-6,178

12.8%

+/-1.8%

311

Nashville-Davidson—Murfreesboro—Franklin, TN

1,718,322

235,823

+/-13,134

13.7%

+/-0.8%

290

New Bern, NC

124,576

19,936

+/-3,616

16.0%

+/-2.8%

209

New Haven-Milford, CT

836,150

107,710

+/-8,771

12.9%

+/-1.0%

308

New Orleans-Metairie, LA

1,221,794

235,888

+/-11,662

19.3%

+/-1.0%

91

New York-Newark-Jersey City, NY-NJ-PA

19,589,817

2,861,640

+/-41,911

14.6%

+/-0.2%

267

Niles-Benton Harbor, MI

150,975

24,561

+/-2,696

16.3%

+/-1.8%

193

North Port-Sarasota-Bradenton, FL

722,807

103,748

+/-8,231

14.4%

+/-1.1%

278

Norwich-New London, CT

261,938

23,568

+/-3,613

9.0%

+/-1.4%

370

Ocala, FL

329,035

64,222

+/-7,962

19.5%

+/-2.4%

83

Ocean City, NJ

94,252

8,835

+/-1,881

9.4%

+/-2.0%

365

Odessa, TX

147,095

21,501

+/-5,010

14.6%

+/-3.4%

265

Ogden-Clearfield, UT

615,823

64,161

+/-7,360

10.4%

+/-1.2%

356

Oklahoma City, OK

1,286,744

191,830

+/-11,090

14.9%

+/-0.9%

256

Olympia-Tumwater, WA

257,962

33,003

+/-5,603

12.8%

+/-2.2%

314

Omaha-Council Bluffs, NE-IA

878,790

111,619

+/-8,137

12.7%

+/-0.9%

317

Orlando-Kissimmee-Sanford, FL

2,221,209

380,933

+/-21,384

17.1%

+/-1.0%

151

Oshkosh-Neenah, WI

161,299

20,803

+/-2,586

12.9%

+/-1.6%

306

Owensboro, KY

114,097

18,450

+/-3,272

16.2%

+/-2.8%

198

Oxnard-Thousand Oaks-Ventura, CA

827,429

98,572

+/-8,115

11.9%

+/-1.0%

334

Palm Bay-Melbourne-Titusville, FL

545,062

81,662

+/-8,274

15.0%

+/-1.5%

252

Panama City, FL

186,734

33,000

+/-4,984

17.7%

+/-2.7%

132

Parkersburg-Vienna, WV

91,264

17,462

+/-2,480

19.1%

+/-2.7%

98

Pensacola-Ferry Pass-Brent, FL

439,944

70,881

+/-7,697

16.1%

+/-1.8%

203

Peoria, IL

372,862

47,768

+/-5,937

12.8%

+/-1.6%

312

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

5,884,173

792,981

+/-24,235

13.5%

+/-0.4%

296

Phoenix-Mesa-Scottsdale, AZ

4,325,550

760,706

+/-27,227

17.6%

+/-0.6%

137

Pine Bluff, AR

85,065

20,736

+/-3,415

24.4%

+/-3.8%

16

Pittsburgh, PA

2,300,779

294,363

+/-10,892

12.8%

+/-0.5%

313

Pittsfield, MA

123,230

15,214

+/-2,321

12.3%

+/-1.9%

326

Pocatello, ID

81,080

13,900

+/-2,892

17.1%

+/-3.5%

152

Port St. Lucie, FL

432,472

74,415

+/-8,455

17.2%

+/-1.9%

150

Portland-South Portland, ME

508,937

57,943

+/-5,961

11.4%

+/-1.2%

340

Portland-Vancouver-Hillsboro, OR-WA

2,281,296

308,138

+/-15,086

13.5%

+/-0.7%

295

Prescott, AZ

211,524

34,138

+/-5,228

16.1%

+/-2.5%

201

Providence-Warwick, RI-MA

1,546,498

221,286

+/-10,882

14.3%

+/-0.7%

279

Provo-Orem, UT

548,963

75,447

+/-6,089

13.7%

+/-1.1%

289

Pueblo, CO

156,624

31,544

+/-4,177

20.1%

+/-2.6%

68

Punta Gorda, FL

160,389

22,628

+/-3,501

14.1%

+/-2.2%

283

Racine, WI

190,473

24,323

+/-3,718

12.8%

+/-1.9%

315

Raleigh, NC

1,185,900

142,633

+/-10,445

12.0%

+/-0.9%

331

Rapid City, SD

137,575

19,947

+/-2,955

14.5%

+/-2.2%

271

Reading, PA

399,792

57,698

+/-6,204

14.4%

+/-1.5%

275

Redding, CA

176,419

35,501

+/-4,281

20.1%

+/-2.4%

69

Reno, NV

432,828

64,933

+/-5,926

15.0%

+/-1.4%

250

Richmond, VA

1,207,277

167,791

+/-9,831

13.9%

+/-0.8%

286

Riverside-San Bernardino-Ontario, CA

4,298,913

781,792

+/-23,534

18.2%

+/-0.5%

120

Roanoke, VA

303,618

43,633

+/-5,158

14.4%

+/-1.7%

276

Rochester, MN

208,650

16,523

+/-2,572

7.9%

+/-1.2%

377

Rochester, NY

1,042,829

153,728

+/-9,277

14.7%

+/-0.9%

264

Rockford, IL

339,554

52,494

+/-5,842

15.5%

+/-1.7%

233

Rocky Mount, NC

147,408

27,825

+/-3,839

18.9%

+/-2.6%

100

Rome, GA

91,478

20,423

+/-4,011

22.3%

+/-4.3%

32

Sacramento—Roseville—Arden-Arcade, CA

2,182,441

363,182

+/-16,433

16.6%

+/-0.8%

178

Saginaw, MI

190,729

34,020

+/-4,382

17.8%

+/-2.3%

127

Salem, OR

387,689

75,096

+/-8,212

19.4%

+/-2.1%

89

Salinas, CA

409,021

73,031

+/-9,276

17.9%

+/-2.3%

126

Salisbury, MD-DE

371,597

57,065

+/-6,429

15.4%

+/-1.7%

239

Salt Lake City, UT

1,124,872

139,442

+/-12,915

12.4%

+/-1.1%

323

San Angelo, TX

110,830

13,518

+/-3,197

12.2%

+/-2.9%

327

San Antonio-New Braunfels, TX

2,235,950

363,769

+/-18,299

16.3%

+/-0.8%

192

San Diego-Carlsbad, CA

3,129,334

475,773

+/-21,393

15.2%

+/-0.7%

243

San Francisco-Oakland-Hayward, CA

4,451,868

510,653

+/-18,671

11.5%

+/-0.4%

337

San Jose-Sunnyvale-Santa Clara, CA

1,891,182

198,842

+/-12,625

10.5%

+/-0.7%

352

San Luis Obispo-Paso Robles-Arroyo Grande, CA

260,653

39,910

+/-4,790

15.3%

+/-1.8%

240

Santa Cruz-Watsonville, CA

258,572

38,616

+/-5,176

14.9%

+/-2.0%

255

Santa Fe, NM

144,957

28,106

+/-3,669

19.4%

+/-2.5%

88

Santa Maria-Santa Barbara, CA

417,118

68,116

+/-7,119

16.3%

+/-1.7%

190

Santa Rosa, CA

489,398

60,812

+/-6,883

12.4%

+/-1.4%

321

Savannah, GA

353,391

61,227

+/-5,819

17.3%

+/-1.6%

147

Scranton—Wilkes-Barre—Hazleton, PA

540,307

83,819

+/-6,826

15.5%

+/-1.3%

230

Seattle-Tacoma-Bellevue, WA

3,555,501

446,327

+/-18,551

12.6%

+/-0.5%

319

Sebastian-Vero Beach, FL

140,482

18,836

+/-3,818

13.4%

+/-2.7%

298

Sebring, FL

96,247

18,094

+/-3,330

18.8%

+/-3.4%

105

Sheboygan, WI

111,769

12,842

+/-2,655

11.5%

+/-2.4%

336

Sherman-Denison, TX

119,767

20,052

+/-3,282

16.7%

+/-2.7%

174

Shreveport-Bossier City, LA

437,810

89,134

+/-7,782

20.4%

+/-1.8%

64

Sierra Vista-Douglas, AZ

116,375

22,254

+/-3,418

19.1%

+/-2.9%

99

Sioux City, IA-NE-SD

164,903

24,384

+/-3,514

14.8%

+/-2.2%

261

Sioux Falls, SD

237,869

21,361

+/-3,670

9.0%

+/-1.5%

371

South Bend-Mishawaka, IN-MI

306,908

61,584

+/-5,763

20.1%

+/-1.9%

70

Spartanburg, SC

310,176

58,165

+/-6,323

18.8%

+/-2.1%

107

Spokane-Spokane Valley, WA

518,992

87,011

+/-6,789

16.8%

+/-1.3%

173

Springfield, IL

207,477

32,420

+/-3,392

15.6%

+/-1.7%

225

Springfield, MA

590,986

99,343

+/-7,727

16.8%

+/-1.3%

171

Springfield, MO

435,561

81,533

+/-7,592

18.7%

+/-1.7%

108

Springfield, OH

132,887

24,653

+/-3,250

18.6%

+/-2.5%

114

St. Cloud, MN

183,531

24,877

+/-3,933

13.6%

+/-2.2%

293

St. George, UT

145,575

23,122

+/-4,312

15.9%

+/-3.0%

219

St. Joseph, MO-KS

119,933

18,614

+/-3,170

15.5%

+/-2.6%

229

St. Louis, MO-IL

2,740,729

352,550

+/-13,984

12.9%

+/-0.5%

309

State College, PA

139,046

27,490

+/-3,453

19.8%

+/-2.5%

79

Staunton-Waynesboro, VA

111,589

12,717

+/-2,542

11.4%

+/-2.2%

338

Stockton-Lodi, CA

690,366

137,663

+/-9,607

19.9%

+/-1.4%

73

Sumter, SC

105,762

21,047

+/-3,419

19.9%

+/-3.2%

74

Syracuse, NY

635,056

101,432

+/-7,069

16.0%

+/-1.1%

211

Tallahassee, FL

353,498

76,104

+/-5,983

21.5%

+/-1.7%

46

Tampa-St. Petersburg-Clearwater, FL

2,822,199

435,739

+/-20,238

15.4%

+/-0.7%

235

Terre Haute, IN

155,430

34,599

+/-4,388

22.3%

+/-2.7%

33

Texarkana, TX-AR

143,188

30,643

+/-4,351

21.4%

+/-2.9%

49

The Villages, FL

98,007

10,283

+/-2,179

10.5%

+/-2.2%

354

Toledo, OH

590,850

114,978

+/-7,622

19.5%

+/-1.3%

84

Topeka, KS

229,113

35,331

+/-4,404

15.4%

+/-1.9%

236

Trenton, NJ

352,368

41,667

+/-6,207

11.8%

+/-1.8%

335

Tucson, AZ

970,384

188,765

+/-11,845

19.5%

+/-1.2%

85

Tulsa, OK

945,445

139,947

+/-6,432

14.8%

+/-0.7%

259

Tuscaloosa, AL

224,068

38,697

+/-4,511

17.3%

+/-2.0%

149

Tyler, TX

211,205

35,817

+/-6,103

17.0%

+/-2.9%

162

Urban Honolulu, HI

951,718

89,684

+/-7,816

9.4%

+/-0.8%

363

Utica-Rome, NY

283,034

49,420

+/-4,952

17.5%

+/-1.7%

141

Valdosta, GA

139,018

37,443

+/-4,673

26.9%

+/-3.3%

9

Vallejo-Fairfield, CA

414,410

53,992

+/-6,058

13.0%

+/-1.5%

303

Victoria, TX

94,588

14,419

+/-3,427

15.2%

+/-3.6%

241

Vineland-Bridgeton, NJ

145,220

29,978

+/-4,515

20.6%

+/-3.1%

58

Virginia Beach-Norfolk-Newport News, VA-NC

1,636,396

212,866

+/-11,713

13.0%

+/-0.7%

304

Visalia-Porterville, CA

448,360

135,066

+/-9,722

30.1%

+/-2.2%

4

Waco, TX

246,267

52,469

+/-6,245

21.3%

+/-2.5%

51

Walla Walla, WA

57,958

10,668

+/-3,003

18.4%

+/-4.9%

115

Warner Robins, GA

180,041

28,665

+/-5,206

15.9%

+/-3.0%

216

Washington-Arlington-Alexandria, DC-VA-MD-WV

5,846,655

495,683

+/-19,944

8.5%

+/-0.3%

373

Waterloo-Cedar Falls, IA

161,729

24,304

+/-3,456

15.0%

+/-2.1%

248

Watertown-Fort Drum, NY

113,014

18,002

+/-3,646

15.9%

+/-3.2%

215

Wausau, WI

133,632

14,731

+/-2,808

11.0%

+/-2.1%

345

Weirton-Steubenville, WV-OH

120,609

19,551

+/-2,770

16.2%

+/-2.3%

196

Wenatchee, WA

112,492

16,636

+/-3,885

14.8%

+/-3.5%

260

Wheeling, WV-OH

138,642

21,491

+/-2,879

15.5%

+/-2.1%

231

Wichita Falls, TX

137,071

25,865

+/-3,446

18.9%

+/-2.4%

101

Wichita, KS

626,159

93,560

+/-7,251

14.9%

+/-1.2%

254

Williamsport, PA

110,934

14,991

+/-3,104

13.5%

+/-2.8%

294

Wilmington, NC

260,957

51,668

+/-6,726

19.8%

+/-2.5%

78

Winchester, VA-WV

124,642

8,432

+/-1,934

6.8%

+/-1.5%

380

Winston-Salem, NC

636,242

127,378

+/-10,165

20.0%

+/-1.6%

71

Worcester, MA-CT

895,779

119,575

+/-10,053

13.3%

+/-1.1%

301

Yakima, WA

243,340

50,581

+/-6,289

20.8%

+/-2.6%

55

York-Hanover, PA

428,323

47,161

+/-5,805

11.0%

+/-1.4%

347

Youngstown-Warren-Boardman, OH-PA

536,084

93,178

+/-6,320

17.4%

+/-1.2%

144

Yuba City, CA

166,398

31,142

+/-4,962

18.7%

+/-3.0%

109

Yuma, AZ

193,953

34,449

+/-4,738

17.8%

+/-2.4%

130

Source: Table prepared by the Congressional Research Service (CRS) based on U.S. Census Bureau 2013 American Community Survey (ACS) data,
table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available on the Internet at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical
confidence interval bounding the estimate.

b. Ranks are based on areas' poverty rate estimates for 2013. Because of sampling variability, an area's rank generally does not statistically differ from other areas with overlapping margins of error.

Appendix C. Poverty Estimates by Congressional District

Table C-1. Poverty by Congressional District: 2013

 

 

Number Poor

Poverty Rate (Percent Poor)


Congressional District

Total Population

Estimate

Margin of Errora

Estimate

Margin of Errora

Rankb

Alabama

 

 

 

 

 

 

1st

680,039

134,336

+/-9,624

19.8%

1.4%

94

2nd

669,393

131,402

+/-8,126

19.6%

1.2%

97

3rd

677,175

134,678

+/-9,229

19.9%

1.3%

93

4th

676,562

118,192

+/-8,753

17.5%

1.3%

149

5th

684,710

108,037

+/-7,907

15.8%

1.1%

192

6th

684,445

78,856

+/-6,788

11.5%

1.0%

340

7th

643,781

177,870

+/-9,741

27.6%

1.5%

16

 

 

 

 

 

 

 

Alaska

 

 

 

 

 

 

(at Large)

718,359

67,016

+/-4,778

9.3%

0.7%

388

 

 

 

 

 

 

 

Arizona

 

 

 

 

 

 

1st

695,472

155,250

+/-8,082

22.3%

1.2%

58

2nd

693,316

118,822

+/-9,247

17.1%

1.3%

162

3rd

698,447

163,662

+/-11,048

23.4%

1.5%

46

4th

705,492

122,569

+/-12,447

17.4%

1.7%

156

5th

755,207

68,362

+/-7,732

9.1%

1.0%

391

6th

733,123

82,235

+/-8,134

11.2%

1.1%

348

7th

740,117

273,768

+/-16,029

37.0%

1.9%

3

8th

729,202

81,101

+/-10,398

11.1%

1.4%

354

9th

726,815

140,691

+/-12,946

19.4%

1.7%

103

 

 

 

 

 

 

 

Arkansas

 

 

 

 

 

 

1st

700,752

151,217

+/-9,195

21.6%

1.3%

64

2nd

735,135

115,908

+/-10,118

15.8%

1.4%

193

3rd

739,766

140,429

+/-10,887

19.0%

1.5%

113

4th

697,687

157,915

+/-8,674

22.6%

1.2%

53

California

 

 

 

 

 

 

1st

686,482

127,948

+/-7,972

18.6%

1.1%

123

2nd

698,111

95,297

+/-7,216

13.7%

1.0%

267

3rd

692,439

113,156

+/-9,888

16.3%

1.4%

181

4th

691,590

76,856

+/-8,030

11.1%

1.2%

354

5th

704,754

91,858

+/-7,901

13.0%

1.1%

291

6th

720,620

173,402

+/-12,209

24.1%

1.6%

41

7th

710,789

98,887

+/-10,134

13.9%

1.4%

258

8th

693,599

151,099

+/-9,870

21.8%

1.4%

62

9th

713,742

141,208

+/-11,730

19.8%

1.6%

94

10th

710,043

135,348

+/-10,141

19.1%

1.4%

111

11th

725,609

86,409

+/-8,468

11.9%

1.1%

326

12th

724,204

100,585

+/-7,012

13.9%

1.0%

258

13th

715,115

127,993

+/-9,097

17.9%

1.3%

139

14th

713,923

59,242

+/-6,449

8.3%

0.9%

402

15th

724,469

63,947

+/-7,598

8.8%

1.0%

397

16th

695,284

228,299

+/-14,216

32.8%

1.8%

5

17th

723,712

54,067

+/-5,872

7.5%

0.8%

416

18th

718,830

52,354

+/-7,304

7.3%

1.0%

419

19th

736,944

106,113

+/-9,730

14.4%

1.3%

241

20th

693,918

121,640

+/-11,271

17.5%

1.6%

150

21st

666,828

198,925

+/-13,312

29.8%

1.9%

9

22nd

721,442

162,392

+/-13,227

22.5%

1.7%

57

23rd

705,535

139,601

+/-12,652

19.8%

1.7%

94

24th

687,555

108,598

+/-9,062

15.8%

1.3%

193

25th

703,152

98,322

+/-10,491

14.0%

1.4%

255

26th

701,251

91,980

+/-7,814

13.1%

1.1%

288

27th

705,546

97,711

+/-8,203

13.8%

1.2%

263

28th

702,945

116,658

+/-7,486

16.6%

1.0%

174

29th

686,505

154,924

+/-11,036

22.6%

1.4%

53

30th

736,172

101,938

+/-8,322

13.8%

1.1%

263

31st

704,960

149,510

+/-11,708

21.2%

1.6%

70

32nd

695,234

111,294

+/-10,142

16.0%

1.4%

190

33rd

699,130

71,356

+/-8,189

10.2%

1.1%

375

34th

694,761

204,453

+/-11,977

29.4%

1.5%

10

35th

712,143

123,251

+/-10,376

17.3%

1.4%

158

36th

710,157

150,803

+/-12,089

21.2%

1.7%

70

37th

721,328

150,105

+/-9,344

20.8%

1.1%

78

38th

716,149

91,962

+/-8,974

12.8%

1.2%

299

39th

713,500

79,713

+/-8,186

11.2%

1.1%

348

40th

713,330

208,796

+/-13,307

29.3%

1.6%

12

41st

721,684

145,863

+/-11,526

20.2%

1.6%

85

42nd

737,375

88,252

+/-11,066

12.0%

1.5%

323

43rd

721,992

154,696

+/-12,164

21.4%

1.6%

67

44th

697,779

169,473

+/-13,325

24.3%

1.7%

39

45th

724,246

59,876

+/-6,525

8.3%

0.9%

402

46th

708,339

147,887

+/-11,656

20.9%

1.6%

75

47th

714,775

127,302

+/-9,765

17.8%

1.4%

145

48th

720,127

81,814

+/-8,059

11.4%

1.1%

343

49th

699,611

87,453

+/-8,497

12.5%

1.2%

306

50th

722,543

96,900

+/-10,044

13.4%

1.3%

282

51st

710,971

175,732

+/-13,156

24.7%

1.7%

37

52nd

690,588

72,736

+/-6,238

10.5%

0.9%

372

53rd

731,261

102,840

+/-12,694

14.1%

1.6%

252

 

 

 

 

 

 

 

Colorado

 

 

 

 

 

 

1st

759,232

128,553

+/-9,726

16.9%

1.3%

168

2nd

735,914

86,969

+/-6,347

11.8%

0.8%

330

3rd

704,491

114,613

+/-8,093

16.3%

1.1%

181

4th

730,209

81,105

+/-7,648

11.1%

1.1%

354

5th

719,869

80,961

+/-7,616

11.2%

1.1%

348

6th

758,469

88,906

+/-7,620

11.7%

1.0%

335

7th

743,277

86,339

+/-8,100

11.6%

1.1%

336

 

 

 

 

 

 

 

Connecticut

 

 

 

 

 

 

1st

701,540

87,123

+/-7,184

12.4%

1.0%

310

2nd

673,205

57,137

+/-5,536

8.5%

0.8%

400

3rd

692,492

82,119

+/-8,301

11.9%

1.2%

326

4th

722,098

72,043

+/-6,567

10.0%

0.9%

378

5th

696,018

75,478

+/-8,848

10.8%

1.3%

365

 

 

 

 

 

 

 

Delaware

 

 

 

 

 

 

(at Large)

900,322

111,327

+/-9,589

12.4%

1.1%

310

 

 

 

 

 

 

 

District of Columbia

 

 

 

 

 

 

Delegate District (at Large)

611,788

115,551

+/-7,400

18.9%

1.2%

116

 

 

 

 

 

 

 

Florida

 

 

 

 

 

 

1st

695,249

111,431

+/-9,748

16.0%

1.4%

190

2nd

664,146

138,714

+/-8,918

20.9%

1.3%

75

3rd

667,485

124,771

+/-8,585

18.7%

1.2%

120

4th

689,505

84,028

+/-7,680

12.2%

1.1%

315

5th

711,039

198,766

+/-12,906

28.0%

1.7%

15

6th

708,733

106,156

+/-8,431

15.0%

1.2%

224

7th

692,433

94,550

+/-9,495

13.7%

1.4%

267

8th

696,381

103,069

+/-9,922

14.8%

1.4%

229

9th

760,571

156,829

+/-15,275

20.6%

1.9%

82

10th

722,655

97,486

+/-10,462

13.5%

1.4%

277

11th

693,689

112,549

+/-10,734

16.2%

1.5%

185

12th

708,043

81,956

+/-6,680

11.6%

0.9%

336

13th

686,676

101,749

+/-10,216

14.8%

1.4%

229

14th

721,858

157,423

+/-12,447

21.8%

1.7%

62

15th

702,978

104,517

+/-10,034

14.9%

1.3%

227

16th

717,345

103,612

+/-8,227

14.4%

1.2%

241

17th

698,886

126,399

+/-10,568

18.1%

1.5%

133

18th

698,549

94,808

+/-9,038

13.6%

1.4%

270

19th

724,927

108,843

+/-8,829

15.0%

1.2%

224

20th

713,673

170,473

+/-12,666

23.9%

1.7%

44

21st

735,327

82,380

+/-7,434

11.2%

1.0%

348

22nd

725,143

110,474

+/-9,617

15.2%

1.3%

212

23rd

715,782

98,480

+/-9,106

13.8%

1.3%

263

24th

710,949

176,066

+/-11,881

24.8%

1.5%

34

25th

730,690

134,139

+/-12,580

18.4%

1.7%

125

26th

727,003

130,805

+/-11,568

18.0%

1.6%

135

27th

710,235

142,860

+/-10,599

20.1%

1.5%

90

 

 

 

 

 

 

 

Georgia

 

 

 

 

 

 

1st

696,283

135,297

+/-8,201

19.4%

1.2%

103

2nd

651,114

177,017

+/-10,143

27.2%

1.5%

21

3rd

698,416

114,099

+/-10,317

16.3%

1.4%

181

4th

713,620

130,139

+/-10,983

18.2%

1.4%

131

5th

681,675

171,956

+/-10,676

25.2%

1.5%

32

6th

723,162

77,563

+/-8,599

10.7%

1.1%

369

7th

727,932

89,058

+/-11,176

12.2%

1.5%

315

8th

671,524

145,324

+/-9,494

21.6%

1.4%

64

9th

698,289

140,702

+/-9,000

20.1%

1.3%

90

10th

675,678

131,630

+/-9,051

19.5%

1.4%

100

11th

718,088

100,655

+/-9,127

14.0%

1.2%

255

12th

672,726

167,385

+/-9,407

24.9%

1.4%

33

13th

711,290

131,182

+/-12,045

18.4%

1.6%

125

14th

681,117

131,761

+/-11,132

19.3%

1.6%

105

 

 

 

 

 

 

 

Hawaii

 

 

 

 

 

 

1st

682,599

60,920

+/-5,936

8.9%

0.9%

395

2nd

685,063

87,448

+/-8,573

12.8%

1.2%

299

 

 

 

 

 

 

 

Idaho

 

 

 

 

 

 

1st

794,263

123,653

+/-11,335

15.6%

1.4%

197

2nd

788,648

122,897

+/-8,979

15.6%

1.1%

197

 

 

 

 

 

 

 

Illinois

 

 

 

 

 

 

1st

706,988

142,867

+/-10,464

20.2%

1.3%

85

2nd

688,548

156,163

+/-11,550

22.7%

1.5%

52

3rd

726,153

95,484

+/-10,108

13.1%

1.3%

288

4th

706,214

159,724

+/-13,235

22.6%

1.8%

53

5th

724,010

77,359

+/-7,300

10.7%

1.0%

369

6th

718,055

37,073

+/-4,607

5.2%

0.6%

434

7th

694,980

178,591

+/-11,471

25.7%

1.4%

30

8th

708,838

78,817

+/-9,914

11.1%

1.3%

354

9th

690,182

88,569

+/-10,431

12.8%

1.4%

299

10th

697,471

68,938

+/-8,091

9.9%

1.2%

379

11th

702,136

72,337

+/-8,170

10.3%

1.2%

374

12th

680,740

124,967

+/-8,488

18.4%

1.2%

125

13th

671,586

126,612

+/-6,875

18.9%

1.0%

116

14th

723,626

46,575

+/-6,539

6.4%

0.9%

427

15th

678,016

102,470

+/-6,889

15.1%

1.0%

216

16th

675,968

88,029

+/-7,114

13.0%

1.0%

291

17th

687,108

125,847

+/-7,516

18.3%

1.1%

129

18th

696,061

74,971

+/-6,694

10.8%

0.9%

365

 

 

 

 

 

 

 

Indiana

 

 

 

 

 

 

1st

700,997

116,370

+/-8,928

16.6%

1.3%

174

2nd

696,539

127,392

+/-9,910

18.3%

1.4%

129

3rd

714,127

109,110

+/-7,422

15.3%

1.0%

208

4th

706,708

88,829

+/-6,945

12.6%

1.0%

302

5th

725,857

80,839

+/-6,625

11.1%

0.9%

354

6th

701,236

107,593

+/-6,591

15.3%

0.9%

208

7th

724,464

174,561

+/-9,964

24.1%

1.4%

41

8th

689,998

103,928

+/-7,111

15.1%

1.0%

216

9th

707,964

106,505

+/-6,825

15.0%

0.9%

224

 

 

 

 

 

 

 

Iowa

 

 

 

 

 

 

1st

740,372

87,534

+/-6,449

11.8%

0.9%

330

2nd

747,690

103,748

+/-6,748

13.9%

0.9%

258

3rd

771,820

89,857

+/-6,573

11.6%

0.9%

336

4th

731,788

97,988

+/-5,435

13.4%

0.7%

282

 

 

 

 

 

 

 

Kansas

 

 

 

 

 

 

1st

693,632

107,739

+/-7,630

15.5%

1.1%

203

2nd

686,122

105,331

+/-7,651

15.4%

1.1%

206

3rd

730,158

73,746

+/-6,487

10.1%

0.9%

377

4th

701,810

106,542

+/-7,729

15.2%

1.1%

212

 

 

 

 

 

 

 

Kentucky

 

 

 

 

 

 

1st

697,666

141,016

+/-8,612

20.2%

1.2%

85

2nd

715,427

122,473

+/-7,098

17.1%

1.0%

162

3rd

724,794

116,814

+/-9,917

16.1%

1.4%

187

4th

718,449

101,001

+/-7,858

14.1%

1.1%

252

5th

690,896

184,181

+/-7,867

26.7%

1.1%

24

6th

719,324

135,150

+/-9,981

18.8%

1.4%

118

 

 

 

 

 

 

 

Louisiana

 

 

 

 

 

 

1st

766,678

103,791

+/-7,676

13.5%

1.0%

277

2nd

766,962

203,181

+/-11,910

26.5%

1.4%

26

3rd

753,214

125,639

+/-10,136

16.7%

1.3%

173

4th

739,473

157,598

+/-9,017

21.3%

1.2%

69

5th

706,617

175,044

+/-9,880

24.8%

1.4%

34

6th

762,045

122,766

+/-11,353

16.1%

1.4%

187

 

 

 

 

 

 

 

Maine

 

 

 

 

 

 

1st

655,033

78,463

+/-7,100

12.0%

1.1%

323

2nd

638,794

102,176

+/-6,263

16.0%

1.0%

190

 

 

 

 

 

 

 

Maryland

 

 

 

 

 

 

1st

706,758

75,818

+/-6,510

10.7%

0.9%

369

2nd

726,237

86,928

+/-7,834

12.0%

1.0%

323

3rd

722,483

57,302

+/-5,320

7.9%

0.7%

410

4th

733,322

66,977

+/-7,518

9.1%

1.0%

391

5th

729,944

55,364

+/-6,138

7.6%

0.9%

415

6th

724,866

70,066

+/-7,963

9.7%

1.1%

381

7th

699,540

123,371

+/-9,075

17.6%

1.2%

148

8th

745,009

49,745

+/-5,484

6.7%

0.7%

423

 

 

 

 

 

 

 

Massachusetts

 

 

 

 

 

 

1st

705,884

110,719

+/-8,093

15.7%

1.1%

196

2nd

700,887

97,862

+/-9,167

14.0%

1.3%

255

3rd

723,728

88,513

+/-7,272

12.2%

1.0%

315

4th

720,531

53,523

+/-6,566

7.4%

0.9%

417

5th

726,369

59,426

+/-6,659

8.2%

0.9%

404

6th

733,179

64,388

+/-7,237

8.8%

1.0%

397

7th

700,909

147,321

+/-8,826

21.0%

1.2%

73

8th

742,643

68,341

+/-6,059

9.2%

0.8%

389

9th

702,400

80,420

+/-6,601

11.4%

0.9%

343

 

 

 

 

 

 

 

Michigan

 

 

 

 

 

 

1st

677,511

105,897

+/-6,121

15.6%

0.9%

197

2nd

697,928

108,808

+/-8,067

15.6%

1.1%

197

3rd

702,211

104,450

+/-8,143

14.9%

1.2%

227

4th

680,380

125,254

+/-7,395

18.4%

1.1%

125

5th

672,090

143,625

+/-9,525

21.4%

1.4%

67

6th

692,828

116,451

+/-7,098

16.8%

1.0%

171

7th

677,666

98,989

+/-7,538

14.6%

1.1%

237

8th

693,631

84,016

+/-7,169

12.1%

1.0%

319

9th

706,738

104,802

+/-8,448

14.8%

1.1%

229

10th

702,803

81,835

+/-7,088

11.6%

1.0%

336

11th

712,460

47,489

+/-5,567

6.7%

0.8%

423

12th

692,599

124,184

+/-8,814

17.9%

1.2%

139

13th

665,000

218,929

+/-10,358

32.9%

1.5%

4

14th

695,668

183,707

+/-10,674

26.4%

1.4%

27

 

 

 

 

 

 

 

Minnesota

 

 

 

 

 

 

1st

646,253

74,282

+/-5,790

11.5%

0.9%

341

2nd

669,895

56,383

+/-6,891

8.4%

1.0%

401

3rd

679,780

43,492

+/-6,880

6.4%

1.0%

427

4th

668,045

90,824

+/-6,299

13.6%

0.9%

270

5th

677,566

113,609

+/-9,034

16.8%

1.3%

171

6th

661,749

54,232

+/-5,275

8.2%

0.8%

404

7th

644,866

76,505

+/-4,410

11.9%

0.7%

326

8th

644,194

83,095

+/-4,924

12.9%

0.8%

296

 

 

 

 

 

 

 

Mississippi

 

 

 

 

 

 

1st

737,103

152,530

+/-10,500

20.7%

1.4%

80

2nd

696,410

226,515

+/-10,737

32.5%

1.5%

6

3rd

722,227

160,723

+/-10,624

22.3%

1.5%

58

4th

738,028

156,147

+/-11,579

21.2%

1.6%

70

 

 

 

 

 

 

 

Missouri

 

 

 

 

 

 

1st

716,639

154,322

+/-10,183

21.5%

1.4%

66

2nd

756,366

44,541

+/-6,446

5.9%

0.8%

430

3rd

740,733

83,265

+/-7,766

11.2%

1.0%

348

4th

718,835

141,025

+/-7,822

19.6%

1.1%

98

5th

746,309

134,121

+/-8,703

18.0%

1.1%

135

6th

723,996

96,173

+/-7,357

13.3%

1.0%

286

7th

737,825

131,801

+/-7,212

17.9%

1.0%

139

8th

720,306

145,818

+/-9,106

20.2%

1.3%

85

 

 

 

 

 

 

 

Montana

 

 

 

 

 

 

(at Large)

990,603

163,637

+/-9,336

16.5%

0.9%

176

 

 

 

 

 

 

 

Nebraska

 

 

 

 

 

 

1st

608,570

78,276

+/-7,294

12.9%

1.2%

296

2nd

622,083

84,591

+/-6,643

13.6%

1.1%

270

3rd

584,912

76,566

+/-6,430

13.1%

1.1%

288

 

 

 

 

 

 

 

Nevada

 

 

 

 

 

 

1st

664,608

150,284

+/-10,320

22.6%

1.6%

53

2nd

678,429

96,988

+/-7,124

14.3%

1.0%

246

3rd

716,933

69,515

+/-7,275

9.7%

1.1%

381

4th

690,506

116,789

+/-11,216

16.9%

1.5%

168

 

 

 

 

 

 

 

New Hampshire

 

 

 

 

 

 

1st

642,184

50,458

+/-5,547

7.9%

0.9%

410

2nd

638,997

61,037

+/-6,572

9.6%

1.0%

384

 

 

 

 

 

 

 

New Jersey

 

 

 

 

 

 

1st

719,415

97,145

+/-8,049

13.5%

1.1%

277

2nd

711,019

111,174

+/-8,251

15.6%

1.1%

197

3rd

726,173

39,334

+/-4,382

5.4%

0.6%

433

4th

726,617

69,746

+/-7,488

9.6%

1.1%

384

5th

719,355

50,882

+/-6,204

7.1%

0.9%

421

6th

712,290

84,373

+/-8,494

11.8%

1.2%

330

7th

735,736

35,040

+/-5,067

4.8%

0.7%

435

8th

751,289

144,504

+/-10,611

19.2%

1.4%

108

9th

755,519

113,758

+/-8,908

15.1%

1.2%

216

10th

714,062

148,640

+/-9,667

20.8%

1.3%

78

11th

721,415

33,693

+/-5,311

4.7%

0.7%

436

12th

728,120

70,260

+/-7,796

9.6%

1.1%

384

 

 

 

 

 

 

 

New Mexico

 

 

 

 

 

 

1st

685,428

133,437

+/-9,937

19.5%

1.4%

100

2nd

676,488

154,795

+/-8,582

22.9%

1.2%

51

3rd

683,486

160,229

+/-9,712

23.4%

1.4%

46

 

 

 

 

 

 

 

New York

 

 

 

 

 

 

1st

701,326

49,336

+/-5,985

7.0%

0.9%

422

2nd

712,372

46,878

+/-6,011

6.6%

0.8%

425

3rd

712,917

38,868

+/-5,395

5.5%

0.8%

432

4th

702,715

50,575

+/-6,569

7.2%

0.9%

420

5th

756,885

110,838

+/-9,391

14.6%

1.2%

237

6th

713,917

96,359

+/-9,486

13.5%

1.3%

277

7th

751,238

200,749

+/-13,007

26.7%

1.6%

24

8th

729,789

180,209

+/-12,403

24.7%

1.5%

37

9th

731,047

146,945

+/-9,562

20.1%

1.2%

90

10th

698,689

118,623

+/-11,829

17.0%

1.5%

166

11th

721,525

99,117

+/-8,696

13.7%

1.2%

267

12th

700,886

87,458

+/-7,326

12.5%

1.1%

306

13th

753,771

231,790

+/-14,629

30.8%

1.8%

7

14th

708,751

132,359

+/-10,918

18.7%

1.4%

120

15th

734,051

292,239

+/-13,036

39.8%

1.5%

2

16th

716,038

92,855

+/-8,224

13.0%

1.1%

291

17th

722,094

81,843

+/-8,290

11.3%

1.2%

346

18th

694,344

72,932

+/-6,695

10.5%

1.0%

372

19th

678,168

84,606

+/-7,505

12.5%

1.1%

306

20th

695,685

94,550

+/-7,852

13.6%

1.1%

270

21st

674,976

102,053

+/-7,404

15.1%

1.1%

216

22nd

678,127

111,998

+/-7,800

16.5%

1.1%

176

23rd

671,906

114,125

+/-7,004

17.0%

1.0%

166

24th

688,710

103,904

+/-6,705

15.1%

1.0%

216

25th

698,713

112,337

+/-7,799

16.1%

1.1%

187

26th

696,725

134,322

+/-7,785

19.3%

1.1%

105

27th

688,608

67,777

+/-5,673

9.8%

0.8%

380

 

 

 

 

 

 

 

North Carolina

 

 

 

 

 

 

1st

691,089

185,667

+/-10,290

26.9%

1.4%

23

2nd

760,912

122,942

+/-9,604

16.2%

1.2%

185

3rd

715,163

123,544

+/-8,781

17.3%

1.2%

158

4th

733,092

133,671

+/-10,387

18.2%

1.3%

131

5th

726,793

137,941

+/-8,718

19.0%

1.1%

113

6th

742,799

111,853

+/-9,124

15.1%

1.2%

216

7th

750,313

144,958

+/-7,926

19.3%

1.0%

105

8th

726,125

148,570

+/-9,452

20.5%

1.3%

84

9th

774,136

61,437

+/-6,755

7.9%

0.9%

410

10th

725,747

128,534

+/-8,986

17.7%

1.2%

147

11th

720,043

136,702

+/-9,351

19.0%

1.3%

113

12th

746,929

204,194

+/-11,575

27.3%

1.4%

19

13th

775,136

75,384

+/-9,217

9.7%

1.2%

381

 

 

 

 

 

 

 

North Dakota

 

 

 

 

 

 

(at Large)

698,199

82,398

+/-5,117

11.8%

0.7%

330

 

 

 

 

 

 

 

Ohio

 

 

 

 

 

 

1st

702,707

125,501

+/-8,795

17.9%

1.3%

139

2nd

714,389

110,534

+/-8,590

15.5%

1.2%

203

3rd

717,654

167,292

+/-10,487

23.3%

1.4%

48

4th

679,889

91,334

+/-7,236

13.4%

1.1%

282

5th

710,347

89,156

+/-7,234

12.6%

1.0%

302

6th

689,436

123,434

+/-7,424

17.9%

1.0%

139

7th

703,754

91,533

+/-7,137

13.0%

1.0%

291

8th

703,535

97,089

+/-8,397

13.8%

1.2%

263

9th

700,743

155,919

+/-9,614

22.3%

1.3%

58

10th

698,963

122,937

+/-8,690

17.6%

1.2%

148

11th

672,657

185,770

+/-8,488

27.6%

1.2%

16

12th

724,734

80,548

+/-8,542

11.1%

1.1%

354

13th

697,304

137,989

+/-8,203

19.8%

1.1%

94

14th

714,373

64,344

+/-6,724

9.0%

0.9%

394

15th

709,268

95,574

+/-9,204

13.5%

1.2%

277

16th

709,000

57,988

+/-7,104

8.2%

1.0%

404

 

 

 

 

 

 

 

Oklahoma

 

 

 

 

 

 

1st

761,062

116,136

+/-6,147

15.3%

0.8%

208

2nd

722,939

148,957

+/-6,518

20.6%

0.9%

82

3rd

737,954

105,777

+/-6,092

14.3%

0.8%

246

4th

747,633

113,007

+/-6,680

15.1%

0.9%

216

5th

765,619

143,029

+/-8,923

18.7%

1.1%

120

 

 

 

 

 

 

 

Oregon

 

 

 

 

 

 

1st

784,374

88,715

+/-8,193

11.3%

1.0%

346

2nd

761,782

137,247

+/-10,784

18.0%

1.4%

135

3rd

781,957

140,701

+/-9,197

18.0%

1.2%

135

4th

755,543

157,618

+/-9,833

20.9%

1.3%

75

5th

769,215

117,857

+/-9,483

15.3%

1.2%

208

 

 

 

 

 

 

 

Pennsylvania

 

 

 

 

 

 

1st

708,585

179,930

+/-12,234

25.4%

1.6%

31

2nd

679,969

187,309

+/-12,997

27.5%

1.7%

18

3rd

675,518

96,849

+/-6,914

14.3%

1.0%

246

4th

686,631

75,245

+/-7,028

11.0%

1.0%

362

5th

653,380

106,830

+/-6,726

16.4%

1.0%

180

6th

702,677

54,490

+/-5,690

7.8%

0.8%

413

7th

698,765

38,806

+/-5,427

5.6%

0.8%

431

8th

699,630

42,426

+/-5,300

6.1%

0.8%

429

9th

677,732

105,526

+/-6,659

15.6%

1.0%

197

10th

677,709

78,320

+/-6,295

11.6%

0.9%

336

11th

678,516

87,815

+/-7,528

12.9%

1.1%

296

12th

689,855

66,446

+/-5,751

9.6%

0.8%

384

13th

699,764

94,833

+/-9,401

13.6%

1.3%

270

14th

677,236

129,813

+/-6,629

19.2%

1.0%

108

15th

690,532

83,356

+/-7,214

12.1%

1.0%

319

16th

691,003

99,925

+/-7,985

14.5%

1.1%

239

17th

673,191

99,143

+/-7,599

14.7%

1.1%

235

18th

692,563

63,343

+/-6,733

9.1%

1.0%

391

 

 

 

 

 

 

 

Puerto Rico

 

 

 

 

 

 

Resident Commissioner District (at Large)

3,581,841

1,626,879

+/-25,081

45.4%

0.7%

1

 

 

 

 

 

 

 

Rhode Island

 

 

 

 

 

 

1st

507,705

83,640

+/-7,475

16.5%

1.4%

176

2nd

503,122

60,806

+/-6,404

12.1%

1.2%

319

 

 

 

 

 

 

 

South Carolina

 

 

 

 

 

 

1st

702,942

93,237

+/-8,472

13.3%

1.2%

286

2nd

652,110

88,521

+/-7,452

13.6%

1.1%

270

3rd

640,182

122,644

+/-8,617

19.2%

1.4%

108

4th

672,211

117,654

+/-11,317

17.5%

1.7%

150

5th

665,846

116,437

+/-8,915

17.5%

1.3%

150

6th

635,209

173,720

+/-12,202

27.3%

1.8%

19

7th

663,301

148,167

+/-9,307

22.3%

1.4%

58

 

 

 

 

 

 

 

South Dakota

 

 

 

 

 

 

(at Large)

815,049

115,454

+/-6,396

14.2%

0.8%

249

 

 

 

 

 

 

 

Tennessee

 

 

 

 

 

 

1st

691,578

134,589

+/-8,688

19.5%

1.3%

100

2nd

704,547

115,039

+/-8,035

16.3%

1.1%

181

3rd

704,206

130,627

+/-8,498

18.5%

1.2%

124

4th

708,757

109,876

+/-9,823

15.5%

1.4%

203

5th

717,954

125,063

+/-10,508

17.4%

1.5%

156

6th

711,308

117,565

+/-8,039

16.5%

1.1%

176

7th

710,702

105,314

+/-7,598

14.8%

1.1%

229

8th

690,620

99,693

+/-7,837

14.4%

1.1%

241

9th

695,623

189,006

+/-10,197

27.2%

1.4%

21

 

 

 

 

 

 

 

Texas

 

 

 

 

 

 

1st

687,535

131,109

+/-9,739

19.1%

1.4%

111

2nd

713,206

77,574

+/-9,226

10.9%

1.3%

363

3rd

761,975

61,299

+/-7,654

8.0%

1.0%

407

4th

692,508

116,697

+/-7,652

16.9%

1.1%

168

5th

702,251

121,143

+/-9,754

17.3%

1.4%

158

6th

723,550

98,531

+/-9,801

13.6%

1.3%

270

7th

739,161

95,951

+/-12,599

13.0%

1.6%

291

8th

723,034

106,875

+/-12,178

14.8%

1.7%

229

9th

732,651

170,582

+/-15,618

23.3%

1.9%

48

10th

743,786

93,471

+/-9,733

12.6%

1.3%

302

11th

707,102

100,293

+/-8,202

14.2%

1.1%

249

12th

715,352

90,096

+/-9,395

12.6%

1.2%

302

13th

666,624

113,891

+/-7,896

17.1%

1.2%

162

14th

685,799

120,238

+/-10,196

17.5%

1.5%

150

15th

712,583

206,766

+/-14,700

29.0%

1.9%

13

16th

713,506

149,716

+/-11,335

21.0%

1.6%

73

17th

697,313

144,209

+/-9,217

20.7%

1.3%

80

18th

719,940

174,321

+/-12,611

24.2%

1.6%

40

19th

676,937

118,302

+/-7,916

17.5%

1.2%

150

20th

738,710

149,099

+/-11,527

20.2%

1.5%

85

21st

725,911

89,810

+/-8,990

12.4%

1.2%

310

22nd

776,804

62,068

+/-8,980

8.0%

1.1%

407

23rd

704,310

132,124

+/-12,368

18.8%

1.6%

118

24th

737,662

81,517

+/-7,194

11.1%

1.0%

354

25th

698,238

84,487

+/-7,903

12.1%

1.1%

319

26th

754,463

60,365

+/-7,755

8.0%

1.0%

407

27th

700,545

121,425

+/-7,765

17.3%

1.1%

158

28th

716,462

185,834

+/-13,454

25.9%

1.8%

29

29th

725,214

205,537

+/-15,022

28.3%

1.9%

14

30th

730,093

181,102

+/-12,840

24.8%

1.7%

34

31st

755,146

81,363

+/-8,607

10.8%

1.1%

365

32nd

712,450

102,395

+/-10,552

14.4%

1.4%

241

33rd

718,202

211,105

+/-14,105

29.4%

1.7%

10

34th

699,377

214,124

+/-14,414

30.6%

1.9%

8

35th

734,454

176,578

+/-13,103

24.0%

1.6%

43

36th

691,375

100,042

+/-9,328

14.5%

1.3%

239

 

 

 

 

 

 

 

Utah

 

 

 

 

 

 

1st

710,878

79,725

+/-6,787

11.2%

0.9%

348

2nd

701,563

99,573

+/-8,257

14.2%

1.1%

249

3rd

703,653

94,122

+/-6,866

13.4%

1.0%

282

4th

735,493

87,761

+/-9,686

11.9%

1.3%

326

 

 

 

 

 

 

 

Vermont

 

 

 

 

 

 

(at Large)

602,538

74,058

+/-5,273

12.3%

0.9%

313

 

 

 

 

 

 

 

Virginia

 

 

 

 

 

 

1st

744,218

66,370

+/-8,115

8.9%

1.1%

395

2nd

702,902

75,806

+/-7,226

10.8%

1.0%

365

3rd

713,004

169,763

+/-8,239

23.8%

1.2%

45

4th

706,932

88,229

+/-8,412

12.5%

1.2%

306

5th

705,461

106,670

+/-7,457

15.1%

1.0%

216

6th

698,956

106,450

+/-8,106

15.2%

1.2%

212

7th

746,510

55,579

+/-5,781

7.4%

0.8%

417

8th

768,254

59,318

+/-7,803

7.7%

1.0%

414

9th

691,431

125,424

+/-7,101

18.1%

1.0%

133

10th

775,923

35,554

+/-5,723

4.6%

0.7%

437

11th

756,953

49,570

+/-6,419

6.5%

0.8%

426

 

 

 

 

 

 

 

Washington

 

 

 

 

 

 

1st

701,188

64,725

+/-7,748

9.2%

1.1%

389

2nd

679,236

98,032

+/-9,375

14.4%

1.4%

241

3rd

684,902

95,133

+/-7,202

13.9%

1.0%

258

4th

688,694

123,122

+/-10,455

17.9%

1.5%

139

5th

652,983

116,228

+/-7,411

17.8%

1.1%

145

6th

661,196

97,169

+/-8,145

14.7%

1.2%

235

7th

689,597

84,405

+/-6,843

12.2%

1.0%

315

8th

694,338

79,365

+/-8,803

11.4%

1.2%

343

9th

697,715

107,630

+/-9,397

15.4%

1.3%

206

10th

686,413

101,473

+/-9,503

14.8%

1.3%

229

 

 

 

 

 

 

 

West Virginia

 

 

 

 

 

 

1st

593,790

101,747

+/-6,634

17.1%

1.1%

162

2nd

613,973

93,591

+/-6,940

15.2%

1.1%

212

3rd

590,503

137,009

+/-8,296

23.2%

1.4%

50

 

 

 

 

 

 

 

Wisconsin

 

 

 

 

 

 

1st

693,828

81,867

+/-7,526

11.8%

1.1%

330

2nd

716,614

100,772

+/-7,885

14.1%

1.1%

252

3rd

680,168

94,351

+/-5,668

13.9%

0.8%

258

4th

697,611

182,145

+/-7,715

26.1%

1.1%

28

5th

709,150

62,002

+/-5,486

8.7%

0.8%

399

6th

689,717

70,099

+/-4,808

10.2%

0.7%

375

7th

701,635

86,042

+/-5,816

12.3%

0.8%

313

8th

704,474

78,273

+/-5,835

11.1%

0.8%

354

 

 

 

 

 

 

 

Wyoming

 

 

 

 

 

 

(at Large)

569,307

62,039

+/-5,844

10.9%

1.0%

363

Source: Table prepared by the Congressional Research Service (CRS) based on analysis of U.S. Census Bureau 2013 American Community Survey (ACS) data, table series S1701: Poverty Status in the Past 12 Months, from the Census Bureau's American FactFinder, available on the Internet at http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.

a. Margin of error of an estimate based on a 90% statistical confidence level. When added to and subtracted from an estimate, the range reflects a 90% statistical confidence interval bounding the estimate.

b. Ranks are based on the Congressional Districts' poverty rate estimates for 2013. Because of sampling variability, a District's rank does not generally statistically differ from other Districts with overlapping margins of error.


Footnotes

1.

Supporting data are based on the following: U.S. Census Bureau, Income and Poverty in the United States: 2013; Current Population Report No. P60-249, September 2014; and unpublished Census Bureau tables, available on the Internet at http://www.census.gov/hhes/www/poverty/data/incpovhlth/2013/index.html.

2.

Periods of recession are officially defined by the National Bureau of Economic Research (NBER) Business Cycle Dating Committee. See http://www.nber.org/cycles/main.html.

3.

The poverty rate of non-aged adults was 17.0% in 1959. Comparable estimates are not available from 1960 through 1965. By 1966, the non-aged poverty rate stood at 10.5%. See Table A-1.

4.

CRS Report RL33615, Parents' Work and Family Economic Well-Being, by [author name scrubbed] and [author name scrubbed].

5.

For a more complete discussion of the U.S. poverty measure, see CRS Report R41187, Poverty Measurement in the United States: History, Current Practice, and Proposed Changes, by [author name scrubbed].

6.

The Department of Health and Human Services (HHS) releases poverty income guidelines that are derived directly from Census poverty thresholds. These guidelines, a simplified approximation of the Census poverty thresholds, are used by HHS and other federal agencies for administering programs, particularly for determining program eligibility. For current guidelines and methods for their computation, see http://aspe.hhs.gov/poverty/index.shtml.

7.

See http://www.census.gov/hhes/www/poverty/data/threshld/index.html.

8.

The poverty measure was adopted as the "official poverty measure" by a directive issued in 1969 by the Bureau of the Budget, now the Office of Management and Budget (OMB). The directive was revised in 1978 to include revisions to poverty thresholds and procedures for updating thresholds for inflation using the Consumer Price Index (CPI). See OMB Statistical Policy Directive 14, available on the Internet at http://www.census.gov/hhes/povmeas/methodology/ombdir14.html.

9.

Based on U.S. Department of Labor Bureau of Labor Statistics Consumer Expenditure Survey data, in 2013 the average family spent an estimated 10.3% of pre-tax income on food (including food consumed at home and away from home), as opposed to one-third in the mid-1950s. This implies that the multiplier for updating poverty thresholds based on food consumption would be 9.7 (i.e., 1/0.103), or 3.2 times the multiplier of 3 subsumed under poverty thresholds developed in the 1960s. Author's calculations from http://www.bls.gov/cex/2013/aggregate/age.pdf.

10.

Beginning with the March 2003 CPS, the Census Bureau allows survey respondents to identify themselves as belonging to one or more racial groups. In prior years, respondents could select only one racial category. Consequently, poverty statistics for different racial groups for 2002 and after are not directly comparable to earlier years' data. The terms black and white, above, refer to persons who identified with only a single racial group. The term Hispanic refers to individuals' ethnic, as opposed to racial, identification. Hispanics may be of any race.

11.

The CPS asks several questions to determine whether individuals are considered to have a work disability. Persons are identified as having a work disability if they (1) reported having a health problem or disability that prevents them from working or that limits the kind or amount of work they can do; (2) ever retired or left a job for health reasons; (3) did not work in the survey week because of long-term physical or mental illness or disability which prevents the performance of any kind of work; (4) did not work at all in the previous year because they were ill or disabled; (5) are under 65 years of age and covered by Medicare; (6) are under age 65 years of age and a recipient of Supplemental Security Income (SSI); or (7) received veteran's disability compensation. Persons are considered to have a severe work disability if they meet any of the criteria in (3) through (6), above. See http://www.census.gov/hhes/www/disability/disabcps.html.

12.

See http://www.census.gov/hhes/www/cpstables/032014/pov/pov26_000.htm

13.

Two states' poverty rates are statistically different at the 90% statistical confidence interval if the confidence intervals bounding their respective poverty rates do not overlap with one another. However, some states with overlapping confidence intervals may also statistically differ at the 90% statistical confidence interval. In order to precisely determine whether two states' poverty rates differ from one another, a statistical test of differences must be performed. The standard error for the difference between two estimates may be calculated as: . Two estimates are considered statistically different if at the 90% statistical confidence interval the absolute value of the difference is greater than 1.645 times the standard error of the difference (i.e., . Note that the standard error for a state's poverty estimate may be obtained by dividing the margin of error depicted in Figure 6 by 1.645.

14.

Statistically significant differences are based on a 90% statistical confidence interval.

15.

Beginning in 2006, a portion of the population living in non-institutional group quarters has been included in the ACS in estimating poverty. The population living in institutional group quarters, military barracks, and college dormitories has been excluded in the ACS poverty estimates for all years. The part of the non-institutional group quarters population that has been included in the poverty universe since 2006 (e.g., people living in group homes or those living in agriculture workers' dormitories) is considerably more likely to be in poverty than people living in households. Consequently, estimates of poverty in 2006 and after are somewhat higher than would be the case if all group quarters residents were excluded—thus, comparisons with earlier year estimates are not strictly comparable.

16.

Kathleen Short, The Supplemental Poverty Measure: 2013, U.S. Census Bureau, P60-251, Washington, DC, October 2014, http://census.gov/content/dam/Census/library/publications/2014/demo/p60-251.pdf.

17.

For a discussion of the history and development of the U.S. poverty measure, and efforts to improve poverty measurement, see CRS Report R41187, Poverty Measurement in the United States: History, Current Practice, and Proposed Changes, by [author name scrubbed].

18.

National Research Council, Panel on Poverty and Family Assistance, "Measuring Poverty: A New Approach," Constance F. Citro and Robert T. Michael, eds. (Washington, DC: National Academy Press, 1995). (Hereinafter cited as Citro and Michael, Measuring Poverty…)

19.

The working group included representatives from BLS, the Census Bureau, the Council of Economic Advisors, the Department of Commerce, the Department of Health and Human Services, and OMB.

20.

The ITWG's guidance is available at http://www.census.gov/hhes/www/poverty/SPM_TWGObservations.pdf.

21.

Census Bureau to Develop Supplemental Poverty Measure, March 2, 2010 News Release, Economics and Statistics Administration, U.S. Department of Commerce. Available on the Internet at http://www.esa.doc.gov/news/2010/03/02/census-bureau-develop-supplemental-poverty-measure.

22.

The NAS panel recommended that the reference family for establishing initial thresholds be based on families with two adults and two children. The ITWG suggested that initial thresholds be based on consumer units with exactly two children, as children reside in a variety of family types (such as single parent families, presence of one or more grandparents, and families with cohabiting adult partners). The NAS panel recommended that initial thresholds be established at between 78% and 83% of median expenditures on FCSU of reference families, which empirically ranged between the 30th and 35th percentiles. The ITWG suggested that initial thresholds be set at a range around the 33rd percentile of expenditures on FCSU for the reference consumer units. The ITWC suggested that five years of CE data be used in establishing thresholds to smooth the change in the thresholds from one year to the next.

23.

The 1.2 multiplier applied to FCSU equals the midpoint of the NAS panel's recommended multiplier of between 1.15 and 1.25.

24.

"Official" published estimates of poverty exclude unrelated children under the age of 15 in the universe for whom poverty is determined. For comparison with the SPM measure, these children are included in both the "adjusted official" poverty measure and the SPM. Under the "official" published poverty measure, the overall poverty rate was 14.5% in 2013; under the adjusted measure shown in this report, the overall "official" poverty rate in 2013 was 14.6%.

25.

For further discussion, see Ashley J. Provencher, Unit of Analysis for Poverty Measurement: A Comparison of the Supplemental Poverty Measure and the Official Poverty Measure, U.S. Census Bureau, SEHSD Working Paper # 2011-22, Washington, DC, August 2, 2011, http://www.census.gov/hhes/povmeas/methodology/supplemental/research/Provencher_JSM.pdf.

26.

The Census Bureau defines Metropolitan Statistical Areas (MSAs) containing a core urban area with a population of 50,000 or more, consisting of one or more counties, that includes the counties containing the urban core area as well as any adjacent counties that have a high degree of social and economic integration (as measured by commuting to work) with the urban core. See http://www.census.gov/population/metro/.

27.

Significant differences based on a 90% statistical confidence level.

28.

Significant difference at a 90% statistical confidence level.