Ask the Scholar

{
    "localId": "26412093",
    "label": "Welfare - Caseloads - Brookings",
    "core": "doc",
    "dtoType": "document",
    "citationUrl": "https://catalog.archives.gov/id/26412093"
}

{
    "id": "26412093",
    "sourceUrl": "https://catalog.archives.gov/id/26412093",
    "contentType": "document",
    "title": "Welfare - Caseloads - Brookings",
    "citationUrl": "https://catalog.archives.gov/id/26412093",
    "collections": [
        "Records of the Domestic Policy Council (Clinton Administration)",
        "Cynthia Rice's Subject Files"
    ],
    "iiifBase": "https://s3.amazonaws.com/NARAprodstorage/opastorage/live/93/4120/26412093/content/presidential-libraries/clinton/foia/2009-1414-S/2009-1414-S-JPG/Box_030/42-t-647851-20091414-S-030-002-2015/42_t_647851_20091414_S_030_002_2015_Page_001.JPG",
    "thumbnailUrl": "https://s3.amazonaws.com/NARAprodstorage/opastorage/live/93/4120/26412093/content/presidential-libraries/clinton/foia/2009-1414-S/2009-1414-S-JPG/Box_030/42-t-647851-20091414-S-030-002-2015/42_t_647851_20091414_S_030_002_2015_Page_001.JPG",
    "largeImageUrl": "https://s3.amazonaws.com/NARAprodstorage/opastorage/live/93/4120/26412093/content/presidential-libraries/clinton/foia/2009-1414-S/2009-1414-S-JPG/Box_030/42-t-647851-20091414-S-030-002-2015/42_t_647851_20091414_S_030_002_2015_Page_001.JPG",
    "imageCount": 12,
    "hasImages": true,
    "source": "import",
    "hasTranscription": false
}

{
    "url": "https://catalog.archives.gov/id/26412093",
    "naId": 26412093,
    "levelOfDescription": "fileUnit",
    "otherTitles": [
        "42-t-647851-20091414-S-030-002-2015"
    ],
    "recordType": "description",
    "ocrSource": "nara-archive"
}

{
    "seq": 12,
    "pageIndex": 0,
    "type": "document",
    "url": "https://s3.amazonaws.com/NARAprodstorage/opastorage/live/93/4120/26412093/content/presidential-libraries/clinton/foia/2009-1414-S/2009-1414-S-PDF/Box_030/42-t-647851-20091414-S-030-002-2015.pdf",
    "mediaId": "6d58aa02000ac371",
    "ocrText": "WOOKINGS THE B INSTITUTION\nTHE BROOKINGS INSTITUTION\nWASHINGTON D.C.\nCENTER ON URBAN AND METROPOLITAN POLICY\n1775 MASSACHUSETTS AVENUE, N.W. WASHINGTON, D.C. 20036-2188\nFAX TRANSMISSION\nTo:\nAndrea Kane\nFrom:\nKate Allen\n([email protected])\nFax:\n202/456-7431\nFax:\n(202) 797-2965\nPhone:\n202/456-5573\nPhone:\n(202) 797-6075\nNumber of pages (including cover sheet): 10\nCOMMENTS:\nandrea-\n)\nplease call if you or any\nof your collergues have any\nquestions regarding the\nsurvey.\n-Kate\nCelebrating 80 Years\nP.1\n889'0N\nBROOKINGS 202 797 2965\n2::22 6661'91'833\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999*\nTHE BROOKINGS CENTER ON URBAN & METROPOLITAN POLICY\nThe State of Welfare Caseloads in America's Cities: 1999\nWelfare caseloads are rapidly declining in America's cities. Between\n1994 and 1998, the county welfare rolls in 30 of the largest American\ncities declined by 35 percent.\nBut these urban welfare rolls are shrinking more slowly than statewide\ncaseloads. The states that are home to these counties saw their welfare\nrolls decline by 44 percent between 1994 and 1998- a rate nearly 10\npercentage points faster than the urban county reductions.\nDoes this\nAt the same time, state welfare caseloads are increasingly concentrated\nshift mean\nin urban areas. Between 1994 and 1998, these counties saw their\nshare of the states' welfare burden grow from 45 to 53 percent.\nthe state % par\nis\nWhile these 30 urban counties make up 20 percent of the total U.S.\nalso shifting\npopulation, they are home to nearly 40 percent of the nation's welfare\npopulation, up from 33 percent in 1994.\nTHE IMPACT OF WELFARE REFORM IN THE 30 LARGEST U.S. CITIES\nNational welfare rolls are at their lowest levels in 30 years. Since 1993, the rolls have declined\nby 44 percent, to just under 8 million people. Many large cities and urban counties have also seen\ntheir welfare rolls decline significantly in the past few years. The importance of these dedines should\nnot be diminished, but caseload decline does not tell the whole story of welfare reform in America.\nThe largest American cities are becoming home to a larger and larger share of the national welfare\nburden. In 1996, the urban counties containing the 30 largest cities were home to 20 percent of the\ntotal U.S. population; yet in August of 1998, these counties contained 39 percent of the nation's\nwelfare cases, nearly double their share of the general population This disparity has only worsened\nwith the implementation of welfare reform. Just four years ago, these counties contained only 33\npercent of U.S welfare cases. Thus, while the absolute numbers are declining, the proportion of\nnational welfare cases is rising in these urban counties, 6 percent since 1994.\nHow is welfare reform playing out in cities? Why are these trends occurring? What is the\nproper policy response? In an attempt to answer these questions, the Brookings Center on Urban and\nMetropolitan Policy has been tracking welfare caseloads in some of America's largest cities since 1998\nto determine the impact of welfare reform and other demographic trends on urban areas.' This survey\ndoes not explain where former recipients go when they leave the welfare rolls, nor does it describe\nthe characteristics of the remaining caseload. It does, however, add a more precise spatial dimension\nto a discussion that frequently focuses solely on national and state-level data and ignores the fact that\n1\n2.2\n889 ON\n2965 262 202 BROKINGS\n2:22PM\n1999\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nwelfare reform will succeed or fail at a local level. This discussion about welfare and cities is\nincreasingly important, as more states hit their time limits and the cases remaining on the welfare rolls\nare increasingly difficult to serve. Brookings hopes to offer a clearer picture of how urban areas are\nfaring in the current policy environment.\nURBAN VS. STATE CASELOAD TRENDS\nState welfare agencies provided annual welfare caseload data for 1990 through 1997 and monthly data\nfor 1998 (January through August) for the thirty largest cities in the United States. (Data was collected for\nWashington, DC, but was excluded from the analysis because there is no corresponding state for comparison.)\nThe 1998 data used in the analyses is an eight-month average (January through August) of the caseload figures\navailable at the time the survey was conducted. Because the counties have traditionally administered welfare\nprograms, the welfare caseload data is principally county data, not city data. Baltimore (a city), New Orleans (a\nparish), and New York City (a city which contains 5 counties) are three major exceptions to this rule. In some\nareas, like Philadelphia and Denver, the county border is coterminous with the city border, and thus the county\nand city data are the same. In other areas, like Houston-Harris County and Columbus-Franklin County, the\nuse of county data may distort city-specific trends with the inclusion of non-central city suburban jurisdictions.\nFaster caseload declines in suburban areas of the county might mask slower declines and higher caseload\nconcentrations in the county's truly urban areas.\nThe welfare data was analyzed in two ways: (1)\ncaseload concentration and (2) relative speed of decline.\nPHILADELPHIA WELFARE CASELOAD\n1994-1998\nConcentration of Remaining Welfare Cases\nPhiladelphia\nThe county's share of the state welfare caseload is the\n39%\n\"caseload concentration\" figure. In most places, state welfare\ncases have become more concentrated in urban counties- a\nRest of\nState\nphenomenon especially pronounced after the implementation\n61%\n1994\nof welfare reform.\nOf the twenty-nine counties surveyed, fourteen saw\ntheir share of the state welfare burden increase over the past\nfour years (\"concentrating\" counties). Eight counties'\nproportional welfare caseloads remained constant with less\nPhiladelphia\nthan one percent change (\"stable\" counties), and seven\nRest of State\n47%\ncounties actually experienced a reduction in their shares of the\n53%\nstate welfare rolls (\"deconcentrating\" counties). Milwaukee\n1998\nCounty contained the greatest absolute share of the state\ncaseload, with 86 percent of Wisconsin's welfare rolls, while\nSan Francisco County contained the smallest share, with a\nmere one percent of California's caseload. The greatest\nincrease in state caseload concentration was also in Milwaukee County: its share of Wisconsin's caseload grew\nfrom 57 percent in 1994 to 86 percent in 1998. In the same period, Jacksonville-Duval County's share of\nFlorida's welfare caseload was cut nearly in half, from 7.7 percent in 1994 to 4.3 percent in 1998 the largest\ndecrease in concentration for the counties surveyed. (See Appendix A.)\n2\nE'd\n889 ON\nBROOKINGS 202 797 2965\n2:22PM\n6661 333\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nPace of Urban Caseload Declines\nBrookings compared the urban counties' welfare caseload decline with the respective state's rate of\ndecline. To determine the \"relative speed of decline\" between the counties and their states, this survey\nexpressed the county caseload change (1994-1998) as a percentage of the state caseload change to convey the\ndifference between the two rates of decline.\"\nOf the twenty-nine counties examined, a majority (14) had rates of welfare caseload decline that\nlagged behind their respective states (the \"slower\" counties). Six counties (the \"same\" counties) experienced\ncaseload declines at rates that were approximately the same as their respective states (that is, at rates 95 to 105\npercent of the state rate). Nine counties' welfare rolls declined at rates faster than their respective states (the\n\"faster\" counties). The slowest jurisdiction surveyed (relative to its state) was El Paso County, where the\nwelfare rolls declined 41 percent slower than the statewide rolls, or at 59 percent of Texas's rate of decline.\nThe fastest county by far was San Jose-Santa Clara County where the county's welfare caseloads dropped 108\npercent faster, or at 208 percent of California's. (See Appendix C.)\nIt is important to note that the differing state rates of caseload decline may distort the relative pace of\nthe counties. California's relatively slow rate of decline (27 percent) makes most California counties-San\nDiego (29 percent), San Francisco (34 percent) and Santa Clara (44 percent) counties--- \"fast\" in comparison.\nConversely, the state of Wisconsin has experienced such dramatic caseload declines (87 percent) that\nMilwaukee County's hefty 72 percent decline in the number of families on welfare is \"slow\" in comparison.\n(For a list of absolute declines in these counties and states, see Appendix D.)\nNot surprisingly, there is a great deal of overlap between the counties with increasing concentration of\nstate caseload and slower rates of decline than their states. All of the \"slower\" counties except San Antonio-\nBexar County were also experiencing an increase in their concentration of the state welfare rolls. Conversely,\nall of the concentrating counties except for Portland-Multnomah County have experienced slower caseload\ndecline than their respective states.\nTo illustrate, Memphis-Shelby County's rate of welfare caseload decline was 28 percent slower than\nTennessee's statewide rate of decline. (The percentage declines for Shelby County and Tennessee were 33\nand 49 percent. respectively.) Despite significant welfare caseload declines, Shelby County's rate of decline\nlagged behind the state's overall rate, and subsequently the county's share of Tennessee's caseload increased\nfrom 28 to 35 percent between 1994 and 1998. (Shelby County contains only 16 percent of Tennessee's\ngeneral population.)\nBEHIND THE TRENDS\nWhat explains the diverse experiences of these urban counties with welfare reform? What trends are\ndriving the divergent urban welfare caseload declines? A comparison of the welfare caseload trends with other\nsocial and economic indicators and regional trends suggests that several factors may be at work.\nUrban areas with higher levels of concentrated poverty tend to have higher concentrations of state\nwelfare caseloads. The survey findings are consistent with what research has told us about concentrated\npoverty. Concentrated poverty is associated with the social characteristics and activities that define the\nhard-to-serve welfare population: illiteracy. chronic unemployment, substance abuse, school dropout, and\nteenage pregnancy and out-of-wedlock births. Concentrated poverty is an urban phenomenon; thus data\nused in this analysis is for central cities. The counties with concentrating caseloads have significantly higher\n3\n889 ON\nS962 262 202 BROKINGS\nWHES:2\n6661\n16.\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\npercentages of their populations living in high-poverty census tracts\" than the stable or deconcentrating\ncounties. The fourteen concentrating counties averaged 15.4 percent of their populations living in these\nhigh-poverty neighborhoods. Portland, Los Angeles, Nashville, and Oklahoma City were outliers with\nrelatively low rates of concentrated poverty (3.6, 5.8, 6.2 and 6.8 percent, respectively). The eight stable\ncounties' concentrated poverty average was exactly half the rate of the concentrating counties-an average\nof 7.7 percent. The average concentrated poverty rate for the seven deconcentrating counties was 4.8\npercent-roughly one-third of the concentrating counties' average. The two de-concentrating Texas\ncounties (Houston-Harris County with 9.2 percent and Dallas County with 7.5 percent) had significantly\nhigher concentrated poverty rates than the remaining de-concentrating and stable counties. The national\nconcentrated poverty average for all central cities is 9.8 percent, and the average for the thirty largest U.S.\ncities (including Washington, DC) examined in this survey was 10.1 percent. (See Appendix A for\nconcentrated poverty rates.)\nOlder cities in the South, Northeast, and Midwest tend to have increasing shares of state welfare rolls.\nThere are some exceptions: Jacksonville-Duval County, Boston-Suffolk County, Columbus-Franklin\nCounty and Indianapolis-Marion County. Welfare rolls in the Southwestern and Western counties were\ngenerally declining either faster than or at the same rate as their respective states. The exceptions to this\ntrend were two counties that encompass relatively poor Southwestern cities---EI Paso County and\nOklahoma County-and Los Angeles County and Portland-Multnomah County in the West. The West\nand Southwest counties may simply be large enough that when aggregated data is used, any central city\neffect in caseload reduction and concentration is distorted and minimized. The Southern, Northeastern,\nand Midwestern counties may encompass a smaller \"buffer zone\" of non-central-city population, and thus\nproportionately more of the city caseload and its trends are reflected in the county-level data.\nUnemployment rates are higher in cities where caseloads are concentrating. Central city unemployment\nrates\" (August 1998) for the concentrating counties averaged 6.3 percent, while the stable and\ndeconcentrating counties averaged 3.6 and 3.7 percent respectively. El Paso County (10.1 percent) and\nBaltimore city (9.8 percent) had the dubious honor of having the highest unemployment rates. Nashville-\nDavidson County and Indianapolis-Marion County had the lowest rate, with only 2.4 percent\nunemployment in those counties (data was unavailable for these central cities). The central city with the\nlowest unemployment was Columbus (2.8 percent). Average unemployment nationally for August 1998\nwas 4.5 percent. Central city unemployment rates may be even higher when looking at the specific\nneighborhoods or populations most impacted by welfare reform.\nPOLICY IMPLICATIONS\nThese findings have important policy implications for the way that federal, state and local leaders effectively\nimplement welfare reform, particularly in communities where welfare recipients are most concentrated.\nFEDERAL IMPLICATIONS\nProvide flexible funding to cities. The federal government should take steps to ensure that urban areas---\nthe counties that currently administer welfare and the cities themselves--- aren't the losers in the\ndevolution game. Under TANF (the 1996 welfare reform law), federal funding for welfare is block-granted\nto states with few guidelines on subsequent allocations to localities. One federal program is specially\ntargeted at cities: the Department of Labor's Welfare-to-Work grants. Some states like Ohio turned down\nthis additional funding citing constraining federal rules and regulations. The Administration should eliminate\nunnecessary restrictions on funding to maximize states' and cities' flexibility and innovation as well as\n4\nS'd\n889 ON\nBROOKINGS 202 797 2965\n2:24PM\nFEB. 16. 1999\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\naccountability. The expansion of eligibility in the Administration's proposed reauthorization of the Welfare-\nto-Work grants is a good start.\nCoordinate funding streams. The implementation of the Workforce Investment Act of 1998 is another\nimportant piece of the welfare puzzle. The new job training block grant to states should compliment\nwelfare-to-work efforts by enhancing access to education, training, and employment across jurisdictional\nboundaries. This way, city residents can connect to regional opportunities and overcome the social and\nspatial isolation associated with concentrated poverty.\nSTATE IMPLICATIONS\nInvest in innovation. Large TANF surpluses ($3 billion from last year alone) pose a dilemma for most state\npolicymakers. With shrinking welfare rolls and excess federal funding. many are tom between pumping\nmoney into programs now or saving the reserves for a \"rainy day\" when the economy worsens and\ncaseloads increase. However, this either-or, spend-or-save dichotomy is misleading. Investments in social\npolicy innovations now could save states money in the long run, if poverty is reduced along with\ndependence on public assistance.\nAccount for concentrated poverty. States should also re-examine their allocation formulas as welfare cases\ncontinue to accumulate in urban areas. Additional funding--- in excess of a per capita allocation- may be\nnecessary to compensate for the higher cost of concentrated poverty.*\nRespond to the urban challenge. For political reasons, states may have difficulty relating to their primary\n\"welfare reservoirs\"-places that contain a quarter or more of the state caseload. Over the past four years,\nnearly 80 percent (11/14) of counties with increasing concentrations of their state's welfare rolls contained\none-quarter or more of their state's caseload. Of the counties where state caseloads did not become\nmore concentrated (and either remained the same or decreased) only 13 percent (2/15) contained more\nthan 25 percent of the state caseload. States need to recognize the magnitude of the multiple challenges\nconverging in large cities--- concentrated poverty, population and job loss, and bloated bureaucracies. The\nurban welfare problem is qualitatively different from the suburban problem and therefore requires uniquely\ntailored solutions.\nLOCAL & REGIONAL IMPLICATIONS\nUnderstand the hardest-to-serve. Local governments should understand the barriers facing the families\nwho remain on welfare in order to help them become self-sufficient. Cities and counties also need to start\nthinking beyond welfare--- the pool of people who may cycle back onto public assistance in the event of a\nrecession, as well as those who are prevented from receiving benefits due to time limits.\nThink regionally. Urban jurisdictions must connect to their larger metropolitan areas. Welfare recipients\nneed not be trapped in job-poor jurisdictions because of bureaucratic fragmentation. Welfare and\nworkforce programs should coordinate across parochial boundaries to connect low-income central city\nresidents with metropolitan employment and training opportunities.\nLeverage existing neighborhood institutions. Community institutions-both faith-based and secular-tend\nto focus principally on the production and preservation of affordable housing. They could play a useful role\nin helping welfare recipients make the transition to work (e.g. recruiting, connecting to suburban\n5\n9'd\n889'0N\nS962 262 202 BROKINGS\n2:24PM\nFEB. 16. 1999\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nemployers). A number of CDCs, for example, have begun to make these job linkages and provide job\nsupports.\nAchieve transportation equity. Cities and urban counties should strive to make low-income transportation\nstrategies an integral part of the mainstream transportation system. In many areas, urban transportation\nsystems have failed to connect low-income central city residents to the metropolitan labor market. As the\nDepartment of Transportation implements the Job Access portion of the new highway legislation,\ncommunities should leverage this short-term funding opportunity to expand or streamline existing transit\nservices and explore a range of non-transit solutions like subsidized car ownership for welfare recipients.\nThis kind of innovation will help bridge the gap between central city workers and suburban jobs.\nBuild a sophisticated information network. Local jurisdictions need a basic understanding of the\ndemographic and economic dimensions of their region. Identifying the regional job centers, the\nneighborhoods where the bulk of the region's welfare recipients live, and the adequacy of transit lines that\nconnect the two are crucial steps in designing programs that will help people move closer to self-sufficiency\nand reduce welfare caseloads in the process. Comprehensive mapping and analysis of the Cleveland area\nled to legislative changes in transit routes to better connect low-income central city residents to entry-level\njobs out in the suburbs.\n6\nP.7\n889 ON\n$962 262 202 BROKINGS\nWass:2\nFEB. 16. 1999\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nAPPENDIX A: COUNTY PERCENTAGE OF STATE WELFARE CASELOAD & TOTAL POPULATION\nPERCENT OF\nPERCENT OF\nPERCENT CONC.\nPERCENT\nJURISDICTION\nCASELOAD TREND\nPERCENT OF TOTAL\nSTATE CASELOAD\nSTATE CASELOAD\n1994-98\nPOVERTY:\nUNEMPLOYMENT:\nSTATE POPULATION\n1994\n1998\nCENTRAL CITY 1990\nCENTRAL CITY 8/98\n1996\n. =county data\nMilwaukee Co.\n56.6%\n85.9%\n+\n20.6%\n5.5%\n17.9%\nNew York City\n68.0%\n69.5%\n+\n12.9%\n7.3%\n40.7%\nCook Co. (Chicago)\n64.0%\n67.0%\n+\n13.2%\n5.5%\n43 0%\nBaltimore City\n48.3%\n56.2%\n+\n13.7%\n9.8%\n13.3%\nWayne Co. (Detroit)\n42 4%\n47.9%\n+\n32.3%\n6.3%\n21.0%\nPhiladelphia Co.\n38.5%\n47.4%\n+\n13.8%\n5.9%\n12.3%\nLos Angeles Co.\n34.4%\n35.6%\n+\n5.8%\n7.5%\n28.7%\nShelby Co. (Memphis)\n28 4%\n35.1%\n+\n21.2%\n4.8%\n16.3%\nOrleans Parish (New Orleans)\n27.9%\n29.0%\n+\n29.0%\n6.7%\n11.0%\nOklahoma Co.\n25.2%\n28.5%\n+\n6.8%\n3.5%\n19.1%\nMultnomah Co. (Portland)\n24.2%\n25.2%\n+\n3.6%\n5.1%\n19.5%\nCuyahoga Co. (Cleveland)\n19.4%\n24.2%\n+\n19.9%\n7.9%\n12.6%\nDavidson Co. (Nashville)\n13.2%\n14.5%\n+\n6.2%\n2.4%*\n10.1%\nEl Paso Co.\n5.1%\n6.6%\n+\n16.0%\n10.1%\n3.6%\nMarion Co. (Indianapolis)\n22.1%\n21.8%\n=\n3.2%\n2.4%*\n14.0%\nSuffolk Co. (Boston)\n21.5%\n21.3%\n=\n3.9%\n3.2%\n10.6%\nFranklin Co. (Columbus)\n10.6%\n10.2%\n=\n10.5%\n2.8%\n9.1%\nBexar Co. (San Antonio)\n8.1%\n8.7%\n=\n16.3%\n4.4%\n6.9%\nSan Diego Co.\n7.4%\n6.7%\n=\n3.2%\n3.9%\n8.3%\nTarrant Co. (Fort Worth)\n5.0%\n4.3%\n=\n4,9%\n4.5%\n6.8%\nTravis Co. (Austin)\n2.6%\n2.5%\nII\n5.8%\n3.2%\n3.6%\nSan Francisco Co.\n1.5%\n1.2%\n=\n1.7%\n4.0%\n2.3%\nMaricopa Co. (Phoenix)\n54.0%\n51.0%\n-\n4.5%\n3.2%\n58.9%\nDenver Co.\n27.4%\n242%\n-\n49%\n3.5%\n13.0%\nKing Co. (Seattle)\n23.8%\n21.7%\n-\n3.6%\n33%\n29.3%\nHarris Co. (Houston)\n19.7%\n14.4%\n-\n9.2%\n5.2%\n16.4%\nDallas Co.\n11.1%\n10.1%\n-\n7.5%\n4.4%\n10.5%\nDuval Co. (Jacksonville)\n7.7%\n4.3%\n-\n42%\n2.5%*\n5.0%\nSanta Clara Co. (San Jose)\n3.5%\n2.5%\n-\n0.0%\n4.1%\n5.0%\n7\n8'd\n889 ON\n$962\n262\n202\nMASS:2\n6661\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nAPPENDIX B: WELFARE CASES IN COUNTIES & STATES SURVEYED\nYear\nWelfare\nWelfare\nCounties'\nCases in\nCases in\nConcentration\n29 Counties\n19 States\nof State Cases\n1994\n1,674,452\n3,720,928\n45.0%\n1998\n1,113,889\n2,121,815\n52.5%\n% Declines\n33.5%\n43.0%\n94-98\nAPPENDIX C: RELATIVE SPEED* OF WELFARE CASELOAD DECLINE, 1994-1998\n(*COUNTY CASELOAD DECLINE EXPRESSED AS A PERCENTAGE OF STATE DECLINE)\nSLOWER\nSAME\nFASTER\n(< 95% of State Rate)\n(95%-105% of State Rate)\n( > 105% of State Rate)\nEl Paso Co.\n58.9%\nMultnomah Co. (Portland)\n97.0%\nDenver Co.\n110.8%\nPhiladelphia Co.\n59.5%\nMarion Co. (Indianapolis)\n101.6%\nDallas Co.\n112.7%\nCuyahoga Co. (Cleveland)\n69.7%\nSuffolk Co. (Boston)\n101.8%\nTarrant Co. (Fort Worth)\n118.5%\nShelby Co. (Memphis)\n71.7%\nTravis Co. (Austin)\n102.9%\nKing Co. (Seattle)\n126.6%\nBaltimore city\n79.7%\nFranklin Co. (Columbus)\n104.5%\nDuval Co. (Jacksonville)\n137.3%\nWayne Co. (Detroit)\n85.0%\nMaricopa Co. (Phoenix)\n104.9%\nSan Diego Co.\n137.6%\nMilwaukee Co.\n85.3%\nHarris Co. (Houston)\n138.4%\nLos Angeles Co\n86.2%\nSan Francisco Co.\n163.8%\nOklahoma Co.\n86.7%\nSanta Clara Co. (San Jose)\n208.1%\nDavidson Co. (Nashville)\n87.7%\nBexar Co. (San Antonio)\n89.3%\nCook Co. (Chicago)\n89.9%\nNew York City\n94.2%\nOrleans Parish (N.Orleans)\n94.6%\n8\n6'd\n869'0N\nS962 262 202 BROKINGS\n2:25:2\n6661 '9T 833\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nP.10\nAPPENDIX D: ACTUAL STATE & COUNTY CASELOADS & PERCENT DECLINES\nSTATE\nNUMBER OF WELFARE\nPERCENT REDUCTION\nJURISDICTION IN STATE\nNUMBER OF WELFARE\nPERCENT REDUCTION\nFAMILIES, SEPT. 1998\nSINCE JAN. 1994\nFAMILIES, AUGUST 1998\nSINCE 1994\nNO.638\nWISCONSIN\n10,247\n86.9%\nMilwaukee County\n10,519\n71.6%\nNO.\nFLORIDA\n96,241\n62.1%\nDuval County (Jacksonville)\n4,549\n74.4%\nCOLORADO\n17,121\n58.9%\nDenver County\n4,904\n57.4%\nOREGON\n17,721\n58.5%\nMultnomah County (Portland)\n4,729\n54.2%\nTEXAS\n126,607\n55.7%\nBexan County (San Antonio)\n14,252\n36.7%\nDallas County\n16,458\n46.3%\nEl Paso County\n10,784\n24.2%\nHarris County (Houston)\n23,543\n56.9%\nTarrant County (Fort Worth)\n7,031\n49.3%\nTravis County (Austin)\n4,125\n42.3%\nOKLAHOMA\n21,644\n54.4%\nOklahoma County\n6,728\n43.1%\nMICHIGAN\n108,286\n52.0%\nWayne County (Detroit)\n57,791\n39.1%\nOHIO\n123,902\n50.6%\nCuyahoga County (Cleveland)\n33,003\n31.1%\n202 797 2965\nFranklin County (Columbus)\n13,972\n46.6%\nTENNESSEE\n57,131\n49.0%\nDavidson County (Nashville)\n8,351\n40.0%\nShelby County (Memphis)\n20,188\n32.7%\nARIZONA\n37,082\n48.6%\nMaricopa County (Phoenix)\n16,886\n55.7%\nINDIANA\n38,213\n48.5%\nMarion County (Indianapolis)\n7,609\n50.1%\nMARYLAND\n42,134\n47.2%\nBaltimore city\n69,962\n35.4%\nLOUISIANA\n46,760\n47.0%\nOrleans Parish (New Orleans)\n13,904\n40.5%\n2:26PM BROOKINGS\nMASSACHUSETTS\n62,436\n44.7%\nSuffolk County (Boston)\n13,880\n40.0%\nPENNSYLVANIA\n124,661\n40.1%\nPhiladelphia County\n63,053\n21.6%\nILLINOIS\n152.165\n36.3%\nCook County (Chicago)\n113,419\n28.5%\nWASHINGTON\n66,821\n35.2%\nKing County (Seattle)\n16,682\n31.9%\nNEW YORK\n316,035\n29.8%\nNew York City\n230,942\n26.0%\nCALIFORNIA\n656,608\n27.3%\nLos Angeles County\n252,646\n18.1%\nSan Diego County\n47,562\n28.9%\nSan Francisco County\n8,590\n34.4%\nSanta Clara County (San Jose)\n17,827\n43.7%\nFEB.16.1999\nFEB.\n8\nEMBARGOED UNTIL 12 pm, FEBRUARY 18, 1999\nENDNOTES\nIn May 1998, Brookings released \"The State of Welfare Caseloads in America's Cities,\" a predecessor\nto this report. That study looked at 23 jurisdictions in all, and when possible, both city and county-level data was\nexamined. These 23 jurisdictions were selected randomly, but had a heavy Northeastern orientation. This\nreport broadens the survey to examine the 30 largest cities in America. Thus, no generalizations or comparisons\ncan be made between the two studies. The earlier report is available in Adobe Acrobat format at\nhttp://www.brookings.edu/ES/Urban/welfarekate.pdf\nii\nTo illustrate, if State A's caseload declines by 4 percent and City A's caseload by 2 percent, there is a\ntwo percentage point difference in absolute decline, but City A's rate of decline is 50 percent (or 2/4) of the\nState's. If State B's caseload declines by 40 percent and City B's by 38 percent, there is also a 2 percentage point\ndifference, but City B's rate of decline is 95 percent (or 38/40) of the State's\nIII\nThus, the \"relative speed of dedine\" analysis is useful to gauge a county's pace relative to its own state-\nnot to gauge a county's pace relative to other counties outside that same state.\nlv\nConcentrated poverty data is from the 1990 Census and was analyzed by the U.S. Department of\nHousing and Urban Development in January 1998.\nV\nEl Paso is a notable exception to this rule. The central city concentrated poverty rate in El Paso is 16\npercent, yet the suburban concentrated poverty rate is an astonishing 53.7 percent.\nVI\n\"High-poverty\" census tracts are defined by the Census as those tracts with 40 percent or more of the\npopulation in poverty.\nVII\nThe Columbus/Indianapolis exceptions are especially interesting, as these two Midwestern cities are\n\"elastic\" cities (to use David Rusk's term). That is, they are able, either through city-county consolidation or\nannexation, to expand beyond their original boundaries and acquire new land and population. It is uncommon\nfor Midwestern cities to be \"elastic.\" The aggregation of suburban and urban populations within these city/county\nborders may mask increased welfare caseload concentrations in the \"core\" central city, and help explain the\nstable caseload shares in these two counties.\nCentral city unemployment data is for August 1998, and is not seasonally adjusted. Bureau of Labor\nStatistics website: http://stats.bls.gov/lauhome.htm.\nJared Bernstein, Low-Wage Labor Market Indicators by City and State: The Constraints Facing Welfare\nReform, Economic Policy Institute Working Paper No. 118 (October 1997).\nHigh poverty cities spend more per capita on primary poverty functions (like welfare and health care),\nand also spend more per capita on other public functions (like education, sanitation, and police services) than do\ncities with low poverty. \"The effect of poverty is large, $27.75 in otherexpenditures per capita per additional\npercentage point in the city's poverty rate.\" Janet Rothenberg Pack. Poverty and Urban Public Expenditures.\nURBAN STUDIES, vol.35, no. 1. page 2009 (1998).\n9\n899'0N\n$962 262 202 SSNIMOOBA\nW892:2\nFEB. 16. 1999"
}