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Evaluating WELFARE REFORM in an Era of Transition -- Copyright -- Acknowledgments -- Contents -- Executive Summary -- THE PANEL -- FINDINGS -- KEY QUESTIONS OF INTEREST -- Populations of Interest -- Outcomes of Interest -- Research Questions of Interest -- EVALUATION METHODS FOR THE QUESTIONS OF INTEREST -- DATA FOR MONITORING AND EVALUATING SOCIAL WELFARE PROGRAMS -- 1 Introduction -- THE PANEL -- POLICY BACKGROUND -- STRUCTURE OF THE REPORT -- 2 Welfare Reform Monitoring and Evaluation: The Current Landscape -- Major Supporting Funders -- DESCRIPTIVE AND MONITORING STUDIES -- STUDIES OF WELFARE LEAVERS AND RELATED GROUPS -- RANDOMIZED EXPERIMENTS -- CASELOAD AND OTHER ECONOMETRIC MODELS -- PROCESS, IMPLEMENTATION, AND QUALITATIVE STUDIES -- OTHER WELFARE REFORM STUDIES -- STUDIES ON TOPICS RELATED TO WELFARE REFORM -- 3 Research Questions and Populations of Interest -- POPULATIONS OF INTEREST -- OUTCOMES OF INTEREST -- QUESTIONS OF INTEREST -- Monitoring the Well-Being of the Low-Income Population -- Characterizing and Tracking Policies, Programs, and Administrative Practices -- Formally Evaluating the Impact of Welfare Reform -- NATIONWIDE VERSUS INDIVIDUAL STATE ASSESSMENTS -- ASSESSMENT -- 4 Evaluation Methods and Issues -- OVERVIEW OF EVALUATION METHODS -- Experimental Methods -- Nonexperimental Methods -- Process Analysis and Qualitative Methods -- EVALUATION METHODS FOR THE QUESTIONS OF INTEREST -- Estimating the Overall Effects of Structural Welfare Reform -- Estimating the Effects of Individual Broad Reform Components -- Estimating the Effects of Detailed Reform Strategies -- Conclusions -- ISSUES IN EVALUATION METHODOLOGY -- Assessing the Reliability of Nonexperimental Evaluation Methods -- Specification Tests -- Sensitivity Testing -- Applying Nonexperimental Methods to Experimental Data.

AbstractUsing USDA's National Household Food Acquisition and Purchase Survey (FoodAPS), we estimated block group‐level spending‐weighted mean distance to the retailers where consumers actually shop (3.9 miles overall; 7.0 miles in rural areas and 3.0 miles in urban areas). Holding constant retailer type, every 10% increase in travel distance was associated with a 3.5% decrease in the food‐at‐home spending share. If consumers travel farther to get food, it may signal that they lack satisfactory options at closer distances. Spending‐weighted mean travel distance could support measures of food retail gaps that are consistent both with consumers' revealed preferences and retailers' incentives.

AbstractFor households participating in the Supplemental Nutrition Assistance Program (SNAP), food expenditures spike upon benefit arrival. We show the price component of expenditure is sensitive to benefit arrival: by the beginning of the fourth week of the benefit month, households pay 20% less on food items. This decline is not induced by changes in shopping behaviors (usage of SNAP, bulk purchases, on‐sale, branded, and discount stores) but rather concentrated among a subset of households with financial constraints and/or a preference for higher‐quality foods. Increased benefit adequacy and/or financial stability for participants could help households smooth their purchasing power.

AbstractPolicymakers have dedicated increasing attention to whether Americans have access to healthful food. As a result, various methods for measuring food store access at the national level have been developed to identify areas that lack access. However, these methods face definitional, data, and methodological limitations. The focus on neighborhoods instead of individuals underestimates the barriers that some individuals face in accessing healthy food, and overestimates the problem in other neighborhoods. This paper reviews and critiques currently available national‐level measures of food access. While multiple measures of food access are needed to understand the problem, we recommend greater attention be paid to individual measures of food store access.

Front Matter -- Acknowledgments -- Contents -- Introduction -- 1 Designing Surveys Acknowledging Nonresponse -- 2 Methods for Obtaining High Response Rates in Telephone Surveys -- 3 High Response Rates for Low-Income Population In-Person Surveys -- 4 Paying Respondents for Survey Participation -- 5 Adjusting for Missing Data in Low-Income Surveys -- 6 Measurement Error in Surveys of the Low-Income Population -- 7 Matching and Cleaning Administrative Data -- 8 Access and Confidentiality Issues with Administrative Data -- 9 Measuring Employment and Income for Low-Income Populations with Administrative and Survey Data -- 10 Administrative Data on the Well-Being of Children On and Off Welfare -- 11 The Right (Soft) Stuff: Qualitative Methods and the Study of Welfare Reform -- 12 Studies of Welfare Leavers: Data, Methods, and Contributions to the Policy Process -- 13 Preexit Benefit Receipt and Employment Histories and Postexit Outcomes of Welfare Leavers -- 14 Experienced-Based Measures of Heterogeneity in the Welfare Caseload -- Appendix -- Index.

DATA AND METHODOLOGICAL ISSUES FOR TRACKING FORMER WELFARE RECIPIENTS: A WORKSHOP SUMMARY -- Copyright -- Acknowledgments -- Contents -- INTRODUCTION -- PURPOSE OF THE WORKSHOP -- PURPOSE OF THE WELFARE LEAVER STUDIES -- POLICY QUESTIONS OF INTEREST -- CHILD OUTCOMES -- FAMILY STRUCTURE AND FAMILY FORMATION OUTCOMES -- OTHER OUTCOMES -- POPULATIONS OF INTEREST -- EVALUATION METHODOLOGIES -- DATA ISSUES FOR TRACKING WELFARE LEAVERS -- OVERALL DATA ISSUES -- Who Does the Sample Population Represent? -- Tools -- ADMINISTRATIVE DATA -- SURVEY DATA -- Survey Design and Implementation -- Response Rates -- Recall Concerns -- CONCLUSION -- References -- Appendix A Workshop Agenda -- WORKSHOP ON EVALUATING STATE WELFARE REFORM PROGRAMS: METHODS AND DATA -- Friday, November 13 -- Appendix B Workshop Participants -- Members, Panel on Data and Methods for Measuring the Effects of Changes in Social Welfare Programs -- State and County Awardee Representatives -- Invited Guests -- Staff, Committee on National Statistics.

Front Matter -- Acknowledgments -- Contents -- Executive Summary -- 1 Introduction -- 2 Program Eligibility -- 3 Current Methods for Estimating Eligibility and Full-Funding Participation -- 4 Potential Biases in Eligibility Estimates -- 5 Estimates of Full-Funding Participation -- 6 Methodological Issues for Future Consideration -- References -- APPENDIX A Glossary -- APPENDIX B Meeting and Workshop Agendas -- APPENDIX C March Current Population Survey Income Supplement -- APPENDIX D The TRIM3 Microsimulation Model -- APPENDIX E Biographical Sketches of Panel Members and Staff.

AbstractThe Supplemental Nutrition Assistance Program's (SNAP) excess shelter deduction acknowledges that high shelter expenses can compete with food spending. We examined whether the deduction facilitates benefit targeting. We estimated that 48%–67% of SNAP households received the deduction, with the likelihood varying by region, household composition, and housing tenure. Households living in the Northeast or renting unsubsidized housing were especially likely to face limits on the deduction amount. According to our results, the excess shelter deduction likely helps households obtain higher SNAP benefit allotments albeit to a limited extent.