Source: David Baer, AARP Public Policy Institute, Research Report, Pub ID: D19014, April 2008
From the summary:
As state and local economic conditions and demographic patterns change, policymakers may consider adjusting their policies on taxes and spending programs. These adjustments become more difficult when economic and demographic changes depart from historical trends.
Policymakers, public officials, policy analysts and others concerned about such issues will find useful state-level data on population, poverty rates, per capita state personal income, state and local revenues, expenditures, tax rates, and property tax relief programs in this seventh edition of the AARP Public Policy Institute’s biennial databook by David Baer. Since 1993, the reference book has been contributing to more informed public policy decisions by providing economic, demographic, and fiscal information.
The handbook facilitates state-by-state and state-national comparisons, featuring economic, demographic, and fiscal summaries of the entire United States, each state, the District of Columbia, the Virgin Islands, and Puerto Rico. Gender and age comparisons are provided for some of the data. Tables and maps of selected data are included.
Source: Laura Wheaton, Jamyang Tashi, Urban Institute, April 24, 2008
From the abstract:
In 2004, 36.6 million people–or 12.6 percent of the U.S. population–were poor. The “poverty gap”–the amount of additional income required to remove all Americans from poverty–was $105.6 billion. Poverty rates were highest for African Americans, Hispanics, women, and persons under 25. Without government benefits, 61 million people would be poor. Social Security and other social insurance programs remove 21 million people from poverty. Means tested programs remove 3 million people from poverty. If food and housing assistance were counted as income for poverty purposes, an additional 7.6 million people would be counted as not poor.
Source: PBS NOW, April 11, 2008
This month, millions of Americans are filing their taxes and hoping for the best, but are rich people actually paying a smaller percentage of taxes than the poor? NOW looks at plans in many states to raise sales taxes and lower property taxes in an effort to generate revenue. But those changes may come at an even bigger price. Anti-poverty advocates say this shift would place the heaviest tax burden on the poorest households–and benefit higher-income Americans. Despite the charge, it’s a model many states have long embraced. NOW travels to one of these states, Alabama, to document the personal impact of regressive tax policies on three very different families. They include a working Mom who shows us how a ten percent sales tax on groceries makes a significant difference in what her family eats; a couple living in a ramshackle house in the backwoods who’ve always held jobs but still face hunger; and a well-to-do suburban couple who benefit from huge tax breaks.
Source: Brookings Institution
Public policies rarely account for regional differences in living costs across the country. Applying cost-of-living adjustments to measurements of economic wellbeing and eligibility standards for social programs in 98 central cities reveals that:
• Federal poverty guidelines, often used to determine eligibility for social programs, change significantly when indexed for cost of living (COL) differences. Out of 38 large cities in higher-cost areas in the Northeast and West, 36 experience increases in the federal poverty guidelines. Conversely, more than half of the large cities located in lower-cost areas in the South and Midwest (38 of 60) see shifts in the opposite direction.
• The percentage, number, and distribution of families that are considered poor under federal poverty guidelines would change dramatically in many central cities if regional differences in the cost of living were recognized. In high-cost areas on the East and West coasts, the poor population would increase substantially both in real and proportional terms. Cities like New York, NY and Los Angeles, CA rank among those with the greatest increases in both the number and proportion of poor families under COL-adjusted standards. However, cities in lower-cost areas of the South and West, such as El Paso, TX and Shreveport, LA, have among the largest declines in the number and share of poor families once living costs are taken into account.
• Adjusting federal poverty guidelines for regional differences in the cost of living has a considerable impact on the number of families eligible for public programs. Overall, the share of families eligible for Early Head Start and Head Start as well as the National School Lunch Program would increase 29 percent in large cities across the country. San Francisco, CA, San Jose, CA, and Bridgeport, CT experience the largest increases in eligibility for these programs, while San Antonio, TX, Corpus Christi, TX, and El Paso, TX see the largest declines in the eligible population under COL-adjusted guidelines.
Full Report (PDF; 1.2 MB)
Source: National Governors Association
This document provides a list of organizations, individuals and reports that states can use as resources when developing policies to reduce poverty and promote family economic opportunity. Information in the guide includes a list of free technical assistance providers that states can access; descriptions of poverty research centers and institutes; issue specific resources and organizations; and information on national anti-poverty reports, task forces, and projects.
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Source: Sunhwa Lee, Lois Shaw, AARP Policy & Research, Pub ID: 2008-03, February 2008
From press release:
Women are nearly twice as likely to be poor as men as they reach pre-retirement and retirement ages, according to a new report by AARP’s Public Policy Institute (PPI). The study, titled “From Work To Retirement: Tracking Changes in Women’s Poverty Status,” found that variables such as marital status, labor force participation, and health status affect the risk of poverty for women as they age.
Women’s longer life expectancies play a large role in determining their lifetime financial security. They are more likely to lose a spouse – nearly 40 percent of women 65 and older were unmarried and living alone compared to only 16 percent of men – and they are also more likely to encounter health related problems.
▪ In Brief
Source: Dan Murphy, Sheila R. Zedlewski, Barbara Butrica, Urban Institute, March 7, 2008
From the abstract:
This paper uses data from the 2004 Health and Retirement Study to demonstrate how the poverty rate of adults 65 and older changes using alternative resource and threshold measures. Results show that alternative poverty measures that account for health spending produce higher poverty rates than the official measure, even those that include the value of housing and financial assets. Poverty remains concentrated among singles (disproportionately women), blacks and Hispanics, and adults 85 and older regardless of how it is measured because these populations have relatively little housing equity or financial assets.
Source: Department of Health and Human Services, January 23, 2008
There are two slightly different versions of the federal poverty measure:
• The poverty thresholds, and
• The poverty guidelines.
The poverty thresholds are the original version of the federal poverty measure. They are updated each year by the Census Bureau (although they were originally developed by Mollie Orshansky of the Social Security Administration). The thresholds are used mainly for statistical purposes — for instance, preparing estimates of the number of Americans in poverty each year. (In other words, all official poverty population figures are calculated using the poverty thresholds, not the guidelines.) Poverty thresholds since 1980 and weighted average poverty thresholds since 1959 are available on the Census Bureau’s Web site. For an example of how the Census Bureau applies the thresholds to a family’s income to determine its poverty status, see “How the Census Bureau Measures Poverty” on the Census Bureau’s web site.
• Federal Register Notice with 2008 Guidelines – Full Text
• Prior Poverty Guidelines and Federal Register References Since 1982
• Frequently Asked Questions (FAQs)
• Further Resources on Poverty Measurement, Poverty Lines, and Their History
• Computations for the 2008 Poverty Guidelines
Source: Ann McLarty Jackson, Christopher Baker, AARP Policy & Research, January 2008
The Low Income Home Energy Assistance Program (LIHEAP) is a federal block grant that provides funding to the 50 states and other jurisdictions to operate home energy assistance programs for low-income households. LIHEAP serves as a social safety net protecting at-risk households spending a high proportion of their income on home energy from the dangers of inadequate heating and cooling.
● Fact Sheet
Source: USDA Economic Research Service
The effectiveness of the Food Stamp Program (FSP) depends on the extent to which it reaches those who are entitled to benefits. In the mid- to late 1990s, participation fell sharply. In recent years, it rebounded somewhat, reaching 65.1 percent in 2005. Changes in participation patterns can be attributed partly to economic fluctuations, but they were also shaped by the rapidly changing State policy environment. This study combines data from the Survey of Income and Program Participation, 1996-2003, with data on State-level food stamp, welfare, minimum wage, and Earned Income Tax Credit policy to investigate the effects of policy on food stamp participation. The findings show strong evidence that some FSP policy reforms made after 1999 (such as more lenient vehicle-exemption policies, longer recertification periods, and expanded categorical eligibility) increased food stamp participation. The use of biometric technology, such as fingerprinting, however, lowered participation. The study shows less consistent evidence that more lenient immigrant eligibility rules, simplified reporting, Electronic Benefit Transfers, or outreach spending raised food stamp participation.
Disclaimer: This study was conducted by The Urban Institute under research agreement number 43-3AEM-3-80085 with the Economic Research Service. The views expressed are those of the authors and not necessarily those of ERS or USDA.
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