Biographical Sketches of Panel Members and Staff

ERIC A. HANUSHEK (Chair) is professor of economics and political science at the University of Rochester. During 1983-1985, he served as deputy director of the Congressional Budget Office. He previously held academic appointments at Yale University and the U.S. Air Force Academy and governmental appointments at the Cost of Living Council and the Council of Economic Advisers. He is a past president of the Association for Public Policy Analysis and Management. His primary academic interests have involved applied public finance and public policy analysis with special reference to schooling and aspects of income determination. He received a B.S. degree from the United States Air Force Academy and a Ph.D. degree in economics from the Massachusetts Institute of Technology.

DAVID M. BETSON is an associate professor in the Department of Economics at the University of Notre Dame. He was previously a research associate at the Institute for Research on Poverty at the University of Wisconsin and a staff economist at the U.S. Department of Health, Education, and Welfare. His research has dealt with the impact of federal tax and transfer programs on the economy and the distribution of income. He received a B.A. degree from Kalamazoo College and a Ph.D. degree from the University of Wisconsin.

LYNNE BILLARD is professor of statistics and associate to the dean of the Franklin College of Arts and Sciences at the University of Georgia. She was formerly head of the statistics and computer science department at the university and has held faculty positions and visiting positions at other U.S. universities and in England and Canada. Her current research interests include time series,



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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Biographical Sketches of Panel Members and Staff ERIC A. HANUSHEK (Chair) is professor of economics and political science at the University of Rochester. During 1983-1985, he served as deputy director of the Congressional Budget Office. He previously held academic appointments at Yale University and the U.S. Air Force Academy and governmental appointments at the Cost of Living Council and the Council of Economic Advisers. He is a past president of the Association for Public Policy Analysis and Management. His primary academic interests have involved applied public finance and public policy analysis with special reference to schooling and aspects of income determination. He received a B.S. degree from the United States Air Force Academy and a Ph.D. degree in economics from the Massachusetts Institute of Technology. DAVID M. BETSON is an associate professor in the Department of Economics at the University of Notre Dame. He was previously a research associate at the Institute for Research on Poverty at the University of Wisconsin and a staff economist at the U.S. Department of Health, Education, and Welfare. His research has dealt with the impact of federal tax and transfer programs on the economy and the distribution of income. He received a B.A. degree from Kalamazoo College and a Ph.D. degree from the University of Wisconsin. LYNNE BILLARD is professor of statistics and associate to the dean of the Franklin College of Arts and Sciences at the University of Georgia. She was formerly head of the statistics and computer science department at the university and has held faculty positions and visiting positions at other U.S. universities and in England and Canada. Her current research interests include time series,

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations sequential analysis, stochastic processes, and AIDS. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. She has held many professional offices, including president of the Biometric Society, Eastern North American Region, and associate editor and associate book editor for the Journal of the American Statistical Association. She is currently a member of the International Council of the Biometric Society and the Council of the International Statistical Institute. She received a B.S. honors degree in mathematics and statistics and a Ph.D. degree in statistics from the University of New South Wales, Australia. CONSTANCE F. CITRO (Study Director) is a member of the staff of the Committee on National Statistics. She is a former vice president and deputy director of Mathematica Policy Research, Inc., and was an American Statistical Association/National Science Foundation (NSF)/Census research fellow at the Bureau of the Census. For the Committee on National Statistics, she has served or is serving as study director for the Panel to Study the NSF Scientific and Technical Personnel Data System, the Panel on Decennial Census Methodology, the Panel on Statistics on Supply and Demand for Precollege Science and Mathematics Teachers, and the Panel to Evaluate the Survey of Income and Program Participation. Her research has focused on the usefulness and accessibility of large, complex microdata files, as well as analysis related to income measurement and demographic change. She is a fellow of the American Statistical Association. She received a B.A. degree from the University of Rochester and M.A. and Ph.D. degrees in political science from Yale University. MICHAEL L. COHEN is an assistant professor in the School of Public Affairs at the University of Maryland. He was formerly a visiting lecturer at the Department of Statistics, Princeton University, and a research associate at the Committee on National Statistics. His general area of research is the use of statistics in public policy, and his current research concerns whether and how to adjust the census for undercoverage. He received a B.S. degree in mathematics from the University of Michigan and M.S. and Ph.D. degrees in statistics from Stanford University. SHELDON DANZIGER is professor of social work and public policy and faculty associate at the Institute of Public Policy Studies at the University of Michigan, where he also directs the Research and Training Program on Poverty, the Underclass, and Public Policy. From 1983 to 1988 he was director of the Institute for Research on Poverty, professor of social work, and Romnes Faculty Fellow at the University of Wisconsin. He is the coeditor of several books on poverty and public policy and the author of numerous scholarly articles on poverty, income inequality, and social welfare programs and policies. He is a member of the Committee on Child Development Research and Public Policy

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations at the National Research Council and of the Social Science Research Council's Committee for Research on the Urban Underclass. He was a member of the Panel on Employment, Income, and Occupations for the National Research Council's Committee on the Status of Black Americans. He received a B.A. degree from Columbia University and a Ph.D. degree in economics from the Massachusetts Institute of Technology. EUGENE P. ERICKSEN is a professor and chair of the Department of Sociology at Temple University. He is currently cochair of the Special Advisory Panel on the 1990 Census, which was convened by the U.S. Secretary of Commerce for advice on the possibility of adjusting the census results for the differential undercount. His research interests focus on the estimation of local-area characteristics from sample data, survey sampling, and problems of survey measurement in general. He received a B.S. degree in mathematics from the University of Chicago and an M.S. degree in statistics and a Ph.D. degree in sociology from the University of Michigan. THOMAS J. ESPENSHADE is professor of sociology and faculty associate of the Office of Population Research at Princeton University. He was formerly director of the Program in Demographic Studies and senior fellow at the Urban Institute in Washington, D.C. His research interests have included the cost of raising children; changes in U.S. family structure; the demographic and economic consequences of slowing population growth in developed countries; and the demographic, economic, and social implications of immigration to the United States. He has written widely on contemporary U.S. immigration and immigrant policy and has testified before Congress on changes in U.S. immigration policy. His current research is related to developing models of undocumented migration to the United States; the role of undocumented migrants in California agriculture; determinants of public attitudes toward undocumented migrants and illegal migration; and proposed reforms of U.S. policy toward legal immigration. He received a B.A. degree in economics from the College of Wooster and a Ph.D. degree in economics and demography from Princeton University. HARVEY GALPER is a principal in the Policy Economics Group of KPMG Peat Marwick in Washington, D.C. His prior positions include senior fellow at the Brookings Institution, senior public finance resident at the Advisory Commission on Intergovernmental Relations, and director of the Office of Tax Analysis of the U.S. Department of the Treasury. He has also served on the staffs of the Urban Institute and the Board of Governors of the Federal Reserve System and has taught at Dartmouth College, the University of California at Berkeley, Yale University, and the Georgetown University Law Center. He is a past member of the board of directors of the National Tax Association-Tax Institute of America and of the Advisory Group of the Commissioner of the

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Internal Revenue Service, and currently serves on the editorial board of the National Tax Journal. He received a B.A. degree from Dartmouth College and M.A. and Ph.D. degrees, both in economics, from Yale University. LOUIS GORDON is professor of mathematics at the University of Southern California. Previously, he was statistician at the ALZA Corporation and at the Energy Information Administration of the U.S. Department of Energy. His interests are in the analysis of computation-intensive statistical algorithms. He is a member of the Committee on National Statistics at the National Research Council and a fellow of the Institute of Mathematical Statistics. He received a B.S. degree in mathematics from Michigan State University and a Ph.D. degree in statistics from Stanford University. KEVIN M. HOLLENBECK is senior economist at the W.E. Upjohn Institute for Employment Research. He was formerly with Mathematica Policy Research, Inc., where he developed a number of components of a microsimulation model. He has also been a consultant to a number of federal agencies to conduct studies with and to evaluate specific microsimulation models. His current research interests are in analysis and evaluation of education and training programs. He received a B.S. degree in mathematics from Michigan State University and M.S. and Ph.D. degrees in economics from the University of Wisconsin. GORDON H. LEWIS is associate professor of sociology at Carnegie Mellon University, with appointments in the School of Urban and Public Affairs and in the Department of Engineering and Public Policy. His major research interest involves income transfers, especially modeling of the interactions among transfer programs; his recent work has focused substantively on areas of child support, tax rebates, and marriage policy. Other research interests include organizational design, organizational management, and individual decision making. He received both undergraduate and graduate degrees in sociology from Stanford. ROBERT MOFFITT is a professor of economics at Brown University. He is a fellow of the Institute for Research on Poverty at the University of Wisconsin and a research associate of the National Bureau of Economic Research. His research interests cover the economics of welfare and other government benefit programs, statistical methods for the evaluation of public policy programs, and the study of labor force and demographic trends of American women. He has served on a committee for the Social Science Research Council on the Survey of Income and Program Participation and on the U.S. Department of Labor's National Advisory Panel for the National Longitudinal Study, and has been a consultant to the National Research Council's Panel on the Evaluation of AIDS Programs. He is chief editor of the Journal of Human Resources. He received a B.A. degree from Rice University and M.A. and Ph.D. degrees from Brown University.

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations CHRISTINE M. ROSS, who served as research associate during the first year of the study, is currently an economist with Mathematica Policy Research, Inc., in Princeton, N.J. Before working with the panel, she was associate analyst with the Human Resources and Community Development Division of the Congressional Budget Office, where she worked with microsimulation models on food stamp and Aid to Families with Dependent Children program policy. She received a B.A. degree in economics from Williams College and M.A. and Ph.D. degrees in economics from the University of Wisconsin. GAIL R. WILENSKY, who served as a member of the panel during 1989, is now administrator of the Health Care Financing Administration of the U.S. Department of Health and Human Services (HHS). Previously, she was vice president for the division of health affairs of Project Hope and a senior research manager at the HHS National Center for Health Services Research where she designed and directed the analysis of the National Medical Care Expenditure Survey. She has served on the faculties of the University of Michigan and George Washington University and held a senior research appointment at the Urban Institute. She has published extensively on health economics and health policy. She is a member of the Institute of Medicine, and was a member of the Physician Payment Review Commission and the Health Advisory Committee of the General Accounting Office. She received a Ph.D degree in economics from the University of Michigan. MICHAEL C. WOLFSON is director general of the Analytical Studies Branch, Statistics Canada. At Statistics Canada he led the group that developed a personal computer-based public-use microsimulation model of Canada's income tax/transfer system, the Social Policy Simulation Database and Model (SPSD/M). More recently, he has been engaged in a fundamental review of the health statistics program. From 1974 to 1985 he held a variety of positions with the Canadian federal public service, including the Treasury Board Secretariat, Department of Finance, and Privy Council Office, with responsibilities in the areas of program evaluation and tax and pension policy. He is also an appointed fellow of the Canadian Institute for Advanced Research in the Program in Population Health. He received a B.Sc. degree in computer science and economics from the University of Toronto and a Ph.D. degree in economics from Cambridge University.

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Index A Access to data, see User accessibility Actuarial Research Corporation, 198 Administrative data, 22, 37, 44, 69-70, 104, 124, 163, 169, 199, 244-245, 248 evaluation of, 14, 15, 71, 141-142 survey data links, 62, 66-69, 129, 131-133, 137, 141-147 (passim), 149, 151, 169, 216-217, 277 see also Matching see also specific data file names Agency for Health Care Policy and Research, 201, 208, 275 Aging, see Data aging; Elderly persons Aid to Families with Dependent Children (AFDC), 9, 21-22, 21-24, 42-51, 77, 78, 129, 142, 156, 233-241, 248-259, 279 aging of data, 167 baseline, 107, 256 behavioral response, 170, 176 benefit-calculator models, 141, 186, 234, 278 costs, 21, 22, 23, 45, 47, 168, 233, 234, 245, 248, 250 distributional analysis, 45, 234, 248, 254-255 minimum benefit standard, 3, 24, 74, 257 participation, 136-137, 235, 246-247 AIDS, 196 American Housing Survey, 61 Archiving, see Documentation and archiving Assistant Secretary for Planning and Evaluation (DHHS), 2, 22, 23, 24, 25, 26, 37, 42, 45-49 (passim), 97, 107, 110-111, 112, 113, 114 , 119, 137, 142, 161, 166, 185, 186, 187, 199-200, 201, 209, 221, 233, 274, 278 B BAFPLAN model, 108 Baseline simulation, 105, 107, 124, 163, 291-298 (passim) TRIM2, 107, 124, 256 see also Eligibility data and modeling Basic research, 13, 38, 107-108, 281-283 Behavioral research, 17-18, 164-165, 176, 177, 181, 201-203, 217-219, 276, 282, 283-289 Behavioral response, 17, 41, 50-51, 120, 282-289, 303 AFDC, 170, 176 cost factors, models, 175, 181 design for, 153, 154, 155, 156, 163, 164, 170-181 DYNASIM2, 170, 171, 179 health care policy, 201-203, 209, 211 individual, 17, 48 MATH, 170, 171, 172, 174-175 model descriptions, specific, 216, 290-298 (passim) parameterization, 17-18, 75-76, 107, 163, 173-174, 177, 180, 211, 218, 229, 282 PRISM, 170, 171 retirement income policies, 215, 217-219 337

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations taxpayers, 170, 171-172, 176, 178, 179, 226, 228-229 TRIM2, 170, 171, 172 uncertainty, 17-18, 75-76, 107, 173, 177, 180, 211, 282 see also Participation data and modeling Second-round effects Benefit-calculator models, 47, 141, 186, 234, 278, 279 Bias, estimation, 92, 94, 237, 240 Black Americans, 71, 126 Bootstrap techniques, 12, 91, 239-240 Brookings Institution, 109, 110, 204, 209, 220, 222, 294 Budget Act, 22, 36 Budget analysis, general, 1-24, 36-37 history, 35-37, 234 OMB, 37, 38, 49, 62, 63, 83, 137 state-level, 83-84 statistical programs budget, 6, 37, 56-59 tax policy, 220 see also Congressional Budget Office Bureau of Economic Analysis, 59, 129 Bureau of Labor Statistics, 59, 69, 146, 205, 274 C Calibration, 136, 237, 291-298 (passim), 303-304 see also Weighting Canada, see Statistics Canada CANSIM, 287 Carter Administration, 111, 171, 185, 194-195 CASE, 11, 190 Cell-based models, 2, 43-45, 53, 79, 111, 116, 199-200, 204, 213, 215, 238, 286, 304 see also specific models Census Bureau, 33, 49, 59, 70, 221 confidentiality, 66, 67-68, 135, 216, 227, 228 databases, general, 11, 12, 15-16, 70, 71, 123-152 errors and error analysis, 14, 69, 70, 71-72, 86, 129, 140, 141, 151, 152 expanded role, 11, 12, 15-16, 149-152 see also Current Population Survey Survey of Income and Program Participation Committee on National Statistics, 2, 25, 26-27, 64, 68, 210 Community, microsimulation modeling, see Organizational factors Computable general equilibrium models, 179, 180, 304-305 Computing technology, 11-12, 18, 27, 83, 121, 148, 182-193, 276 cost factors, 10, 18, 45-46, 121, 148, 163, 164, 182, 183, 184, 186 , 188, 189, 191, 193, 284, 285 design issues, 156, 158, 159, 191 DYNASIM2, 182, 183-184, 185, 186, 192 efficiency, 121, 158, 159 historical perspectives, 1. 24, 31, 34, 37, 162, 182-185, 186, 189 MATH, 185, 192 model descriptions, specific, 290-298 (passim) personal computers, 34, 54, 108, 121, 186-187, 190, 191-192, 279 SPSD/M, 187-188, 191 TRIM2, 185, 186, 187-189, 191-192 validation and, 18, 192-193 workstations, 188, 189, 190, 191 see also Matching; Software User accessibility Conferences, 20, 282-283 Confidence intervals, 79-80, 86, 87, 88, 92, 93-96, 244 Confidentiality, 66-69, 135, 207, 216, 227, 228 Congressional Budget Office, 21-24, 36-37, 42, 45, 46, 48, 61, 112 , 187, 190, 199, 203, 206, 221, 234 Consumer Expenditure Survey, 60, 136 Consumer Price Index (CPI), 58, 196 Continuous Medicare History Sample, 207 Contracts and contractors, 19, 261-262 external validation by, 5, 12, 19, 235-236, 262-263, 279-280 model vendors, 278, 290-298 (passim) Control files, 136-137 see also Matching CORSIM, 186 Cost factors, 24, 25, 56, 63-64, 76, 81, 105, 211, 213, 232, 285 AFDC. 21, 22, 23, 45, 47, 168, 233, 234, 245, 248, 250 aging of data, 17, 18, 49, 105, 154, 170, 237 behavioral response modeling, 175, 181 computing technology, 10, 18, 45-46, 121, 148, 163, 164, 182, 183, 184, 186, 188, 189, 191, 193, 284, 285 cost-effectiveness, 1, 2, 10, 26, 54, 110, 117-118, 148, 160, 181, 189, 192, 193, 196-197, 211, 215, 259, 260, 261, 265, 275, 279 health care financing, 53, 61, 111, 195-212 historical perspectives, 1, 37-38, 53, 182 matching files, CPS-SSA, 7-8, 67, 136 model design, 9, 11, 54, 153, 154, 155, 156, 160, 175, 181, 208

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations model development and operation, 9, 14, 45-46, 99, 112, 117-118, 156, 164, 181, 184, 186, 188, 189, 191, 208 sensitivity analysis, 259 statistical programs budget, 6, 37, 56-59 validation, 259, 260, 261 see also Budget analysis Coverage of population, 11, 14, 71-72, 126-127, 138, 140, 151-152, 249-252 Cross-sectional models, 47, 221 see also specific models Current Beneficiary Survey, 207, 210 Current Population Survey (CPS), 44, 45-46, 60, 69, 71 databases, 11, 15-16, 70, 71 described in detail, 126, 298-299 income data, 145, 146, 151, 174 linkages with SIPP, 144, 145, 146, 147, 228 March supplement, 15, 44-45, 67, 104, 120, 123-152 (passim), 174, 185, 204, 205, 207, 217, 220, 222, 227, 228, 244, 245, 248, 250, 256, 259-260 matching files, CPS-IRS, 67-68, 104, 110, 124, 134, 220, 224, 226-227, 228, 299, 307 matching files, CPS-SSA, 7-8, 18, 67, 104, 123-124, 134-135, 136, 216-217, 245, 277, 299 Current services estimates, 35 D Data aging, 17, 18, 49, 105, 154, 163, 164-170, 205, 276 cost factors, 17, 18, 49, 105, 154, 170, 237 defined, 165, 302-303 design for, 154, 163, 164-170 dynamic, 49, 105, 106, 164, 166, 167-169, 214, 291, 295, 296 health care policy models, 198-199 MATH, 167, 168, 169, 170, 245, 302 retirement income models, 105, 214-215 static, 49, 105, 163, 164, 165-167, 169, 170, 198-199, 214-215, 221 , 225-226, 228, 249-250, 292, 293, 298, 302-303 tax policy models, 221, 225-226, 228, 229 TRIM2, 49, 107, 163, 166-168, 169, 170, 233 Databases, 1, 8, 9-10, 11, 15-16, 70, 104, 123-152, 277 access to, 14, 16, 66-69, 158, 159 Census Bureau, general, 11, 12, 15-16, 70, 71, 123-152 Current Population Survey, 11, 15-16, 70, 71, 123-152 (passim) documentation and archiving, 12, 14, 19-20, 85-86 errors in, 78-79, 90-91, 119-120 evaluation, 15, 137 health care financing, 203-207 individual descriptions, 298-302 Integrated Quality Control System, 15, 43, 46, 131-132, 141, 142, 250, 253-255, 257, 299-300 models, specific descriptions, 290-298 (passim) retirement income, 216-217 Survey of Income and Program Participation, 123, 137-152 tax policy, 223-226 see also Matching Data collection, 7, 13-14, 63-65, 70, 141, 209, 211 see also Questionnaires Surveys and survey data Data quality, 3, 5-8, 13-14, 26-27, 55-72, 119-120, 123-152, 256 census statistics, 125-132, 139-152 existing data, value added, general, 69-72, 210, 277 health care financing, 203-208 input errors, 79, 90, 91 omissions, 131-137 quality profiles, 19, 51, 92, 140, 263-264 Decennial census, errors, 71-72, 86 Demography, see Population factors Department of Agriculture, 2, 22, 25, 36, 97, 110 see also Food and Nutrition Service Department of Health, Education, and Welfare, 35-36 Department of Health and Human Services, 2, 13, 18, 22, 37, 97, 110 , 201, 208, 209, 210, 211, 212, 274 see also specific agencies Department of Labor, 111, 112, 198 Department of the Treasury, 3, 23, 35, 109, 110, 179, 220, 229 Office of Tax Analysis, 98, 110, 136, 220, 223-226, 274, 296 Treasury Individual Income Tax Simulation Model, 296-297 see also Internal Revenue Service Design and development, models, 11, 16-18, 153-181 aging of data, 154, 163, 164-170 behavioral response, 153, 154, 155, 156, 163, 164, 170-181 computing technology and, 156, 158, 159, 191 cost factors, 9, 11, 54, 153, 154, 155, 156, 160, 175, 181, 208

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations cost factors, development and operation, 9, 14, 45-46, 99, 112, 117 -118, 156, 164, 181, 184, 186, 188, 189, 191, 208 Current Population Survey, March supplement, 126-129, 143 documentation and, 16, 157, 158, 159, 160, 190, 266, 267 health care policy models, 208-210 misspecification, 3, 79, 83, 87, 90, 91-92, 93, 94 modular, 16, 111, 155, 156-157, 157, 159, 161, 248 prototyping, 159, 160, 192-193, 199 second-round effects, 153, 154, 155, 163, 164, 178-181 SIPP, 138, 140, 146-148 standards, 16-17, 193, 276 validation and, 16, 157, 158, 159-160 Disaggregation, 61, 132-133, 179 Distributional analysis, 221, 237, 240, 305 AFDC, 45, 234, 248, 254-255 health care outlays, 198, 199 Documentation and archiving, 12, 14, 18, 19-20, 27, 140, 193, 157, 265-272 databases, 12, 14, 19-20, 85-86 design and, 16, 157, 158, 159, 160, 190, 266, 267 MATH, 26, 268, 269 social welfare models and data, 84-86 software, 12, 14, 17, 19, 85-86, 267-269, 270-271 standards, 12, 14, 19, 266-271 user accessibility and, 266, 267, 269, 270 validation and, 85, 157, 158, 266, 270, 271 DRI, 36, 307 Dynamic models, general, 18, 109, 51, 111-112, 123-124, 192-193, 213, 215, 284, 305 aging of data, 49, 105, 106, 164, 166, 167-169, 214, 291, 295, 296 see also specific models Dynamic Simulation of Income Model (DYNASIM), 111-112, 183-184, 214 , 245-246 Dynamic Simulation of Income Model2 (DYNASIM2), 37, 98, 112, 123-124, 161, 169, 198, 213-218, 290-291 behavioral response, 170, 171, 179 computing requirements, 182, 183-184, 185, 186, 192 history, 111-112, 183-184, 214, 245-246 DYNASIM, see Dynamic Simulation of Income Model E Econometric models, general, 17, 35, 53, 92, 176 Economic factors budget deficit, federal, 25, 37, 60, 61, 80, 163 statistical concepts and methods, changing, 58 see also Cost factors; Funding Economic indicators, see CPI GNP Elderly persons, 196, 201, 205 see also Medicare Retirement income policies Eligibility data and modeling, 45, 47, 105, 130, 131, 142, 205, 213 , 234, 256 filing units, 104, 229, 305-306 vs participation, 44, 64-65, 136-137, 151 Employee Benefit Survey, 205 Employment and unemployment, 43-45, 50, 65, 146, 170, 179, 202, 234 Errors and error analysis, 3, 14, 51, 55, 61, 74, 75, 76, 78-79, 83, 84, 90-91, 137, 142, 215, 247 bounds, 83, 86, 87, 88 census data, 14, 69, 70, 71-72, 86, 129, 140, 141, 151, 152 confidence intervals, 79-80, 86, 87, 88, 92, 93-96, 244 databases, 78-79, 90-91, 119-120 decennial census, 71-72, 86 internal validation and, 5, 78-79, 85 missing detail, 129-130, 132-137 see also Imputations model misspecification, 3, 79, 83, 87, 90, 91-92, 93, 94 quality profiles, 19, 51, 92, 140, 141, 263-264 sampling, 3, 69, 78-79, 86, 88, 90, 91, 95, 129, 215, 244, 257 sensitivity analysis, 12, 18, 73, 78, 79-80, 82, 83, 91-92, 93, 94 , 99, 157, 158, 177, 192, 215, 218, 228, 229, 233, 235, 237-239, 242, 246, 248-261 see also Coverage of population Data quality; Uncertainty, Validation and evaluation Estimation processes, 17, 26-27, 42-51, 70, 72-74, 78-79 bias, 92, 94, 237, 240 conditional/unconditional, 74-75, 76, 93-95 42-51 problems of, 48-51 see also Data aging; Errors and error analysis; Uncertainty, Variance estimation Evaluation, see Validation and evaluation Exact matches, see Matching External validation, 5, 12, 19, 77-78, 82, 87-88, 94-95, 99, 233, 235-236, 238, 246, 262-263, 271, 279-280 TRIM2, 99, 233-239

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations F Family Support Act, 3, 6, 21-24, 31, 41-51, 77, 176, 187, 195, 234 , 261, 279 Family Support Administration, 22, 49 Federal government, general, 1, 35 budget deficit, 25, 37, 60, 61, 80, 163 data quality, 6-7 interagency coordination, 6-7, 10, 12-13, 37, 62-63, 146, 149-150, 209, 210, 212, 274-277; see also Matching see also Laws, specific federal Policy analysis agencies Statistical agencies specific departments and agencies Filing units, 104, 229, 305-306 Food and Nutrition Service, 2, 22, 25, 26, 36, 97, 110, 113, 114, 142, 274, 278, 283 Food stamp program, 6, 23, 43, 46, 56, 110, 113, 123, 131, 133, 141-142 , 202, 244-245 Food Stamp Reform Act, 36, 110 Foreign countries, 108, 109, 193 see also specific countries FOSTERS, 113, 143 Frankfurt model, 108 Funding, 19, 26, 59, 82, 278 research, 19, 39, 82 SIPP-CPS, 144, 146, 228 statistical agencies, general, 6, 37, 56-59, 61-62 G General Accounting Office, 15, 77 Germany, Federal Republic, 108 Gramm-Rudman-Hollings Act, 37-38, 163, 219 Graphical interfaces, 121, 189, 190, 193 Gross National Product (GNP), 58, 60, 195, 196, 197 H Health Benefits Simulation Model, 113, 205 Health Care Financing Administration, 22, 23, 190, 199, 201, 204, 207, 275 Health care policies, general, 18, 98, 111, 113, 124, 178, 194, 195-212, 274-275 cost, 53, 61, 111, 195-212 databases, 203-207 history, 197-200, 203-207, 208 long-term care, 202, 204, 209, 295-296 Medicaid, 65, 196, 198, 199, 201, 202-203, 204, 205, 207 Medicare, 61, 124, 178, 195, 199, 201, 202, 203, 207, 225 research, 201-203, 209, 211-212 see also Long-Term Care Financing Model; Pension and Retirement Income Simulation Model Health Financing Model, 111, 199-200 Health insurance, 196, 198, 199, 201, 202, 206, 209 Medigap policies, 201, 225 see also Medicaid; Medicare Health Interview Survey, 60, 200, 205, 207, 210 Health Resources Administration, 199 Historical perspectives, 1, 33, 154, 161-163, 269 aging of data, 166 budget analysis, 35-37, 234 computing technology, 1, 24, 31, 34, 37, 162, 182-185, 186, 189 cost factors, 1, 37-38, 53, 182 DYNASIM/DYNASIM2, 111-112, 183-184, 214, 245-246 health care models and surveys, 197-200, 203-207, 208 macroeconomic models, 35 MATH, 110, 111, 112, 114, 161, 162, 185 microsimulation modeling, general, 2, 9, 24, 25, 27, 31, 33-38, 97 , 107-114 quality profiles, 140 retirement income policy, 212-213 SIPP, 137 social science research, 283-284, 286-287 social welfare policy, 20-24, 34, 278 statistical agencies, 8, 57-62 tax policy, 75-76, 86-87, 108, 110, 212, 219-220, 278 TRIM/TRIM2, 16, 155, 156-157, 157, 159, 161, 248 validation studies. 241-259 HITSM, see Household Income and Tax Simulation Model Hot deck, see Imputations Household Income and Tax Simulation Model (HITSM), 98, 113-114, 136 , 268, 291-292 Human-computer interface, see User accessibility Hungary, 109 I Imputations, 90, 95, 124, 133-136, 145, 146, 149, 174, 204, 249 model descriptions, specific, 291-298 (passim) tax policy models, 221, 223, 225, 226-227, 228, 229-230, 306 see also Matching Income and income support models, 2, 61, 98, 109, 148, 274 CPS, 145, 146, 151, 174 HITSM, 98, 113-114, 136, 268, 291-292 ISDP, 132-133, 137 MRPIS, 98, 113, 161-162, 179, 180, 293-294

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations PENSIM, 183 PRISM, 98, 51, 112, 124, 162, 163, 167, 170, 171, 198, 204, 213-218, 245, 294-295 Reforms in Income Maintenance, 36, 109-110, 161 vs tax models, 221-222 TRIM, 110-111, 161, 184-185 see also Aid to Families with Dependent Children Dynamic Simulation of Income Model 2 Eligibility data and modeling Micro Analysis of Transfers to Households Participation data and modeling Retirement income policies Statistics of Income; Survey of Income and Program Participation data and modeling Tax models and policy Transfer Income Model 2 Income Survey Development Program (ISDP), 132-133, 137 Individual behavior, 17, 48 see also Behavioral response Individual Retirement Accounts, 215, 217-219 Institute of Electrical and Electronics Engineers, 267-268, 269, 270 Integrated Quality Control System (IQCS), 15, 43, 46, 131-132, 141 , 142, 250, 253-255, 257, 299-300 Interagency Forum on Aging-Related Statistics, 210 Internal Revenue Service (IRS), 59, 67-67, 145 linkages with SIPP, 228 matching files, CPS-IRS, 67-68, 104, 110, 124, 134, 220, 224, 226-227, 228, 299, 307 see also Statistics of Income Internal validation, 5, 78-80, 85 International perspectives, 108, 109, 193 see also specific countries Investment, 52-55, 63 data production, 56, 57-64 validation, 80-81 see also Funding J Job Training Partnership Act, 30 Joint Committee on Taxation, 23, 24, 25, 136, 220, 222, 274 K Kasten-Greenberg-Betson (KGB) model, 47-48, 111, 161, 170, 171 L Laws, specific federal Budget Act, 22, 36 Family Support Act, 3, 6, 21-22, 31, 41-51, 77, 176, 187, 195, 234 , 261, 279 Food Stamp Reform Act, 36, 110 Gramm-Rudman-Hollings Act, 37-38, 163, 219 Job Training Partnership Act, 30 Medicare Catastrophic Coverage Act, 6, 206-207, 225 Omnibus Budget Reconciliation Act, 42 Paperwork Reduction Act, 63 Privacy Act, 66 Social Security Act, 112 Tax Reform Act, 3, 23, 24, 66, 113, 176, 221, 225, 226, 229 Lewin/ICF, Inc., 112-113, 124, 200, 205, 213, 291, 294, 295 Linkages, 65, 157, 159, 216-217 administrative/survey data, 62, 66-69, 129, 131-133, 137, 141-147 (passim), 149, 151, 169, 216-217, 277 see also Matching interagency coordination, 6-7, 10, 12-13, 37, 62-63, 146, 149-150, 209, 210, 212, 274-277 see also Matching of models, 16, 53, 112, 157, 159, 131, 190, 199 Longitudinal data, 47, 76, 147, 197, 291 CPS/SIPP, 15, 131, 145, 146, 147 Long-term approaches, 146-148 Long-term care, 202, 204, 209, 295-296 Long-Term Care Financing Model, 204-205, 209, 295-296 Loss functions, 240-241 M Macroeconomic-Demographic Model, 200, 213, 215 Macroeconomic models, 2, 22, 31, 35, 49, 52, 56, 95-96, 109, 116, 178, 200, 213, 215, 238, 307 linkage with microsimulation models, 53, 112, 199 validation, 74-75, 76 Matching, files, 149, 163, 307-308 CPS-IRS, 67-68, 104, 110, 124, 134, 220, 224, 226-227, 228, 299, 307 CPS-SIPP, 144, 145, 146, 147, 228 CPS-SSA, 7-8, 18, 67, 104, 123-124, 134-135, 136, 216-217, 245, 277 , 299 exact, 7, 18, 67-68, 104, 134-135, 216-217, 228-229, 308 IRS-SIPP-CPS, 228 SIPP-SSA, 217 statistical, 104, 110, 127, 133, 135-136, 204, 222, 228, 307-308 MATH, see Micro Analysis of Transfers to Households Mathematica Policy Research, Inc., 110, 292 Medicaid, 65, 196, 199, 201, 202-203, 205 TRIM2, 198, 204, 207

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Medicare, 61, 124, 178, 195, 199, 201, 202, 203, 207, 225 Medicare Catastrophic Coverage Act, 6, 206-207, 225 Medigap policies, 201, 225 MERGE files, 110, 220 Micro Analysis of Transfers to Households (MATH), 2, 36, 47-48, 104 , 132, 133, 162, 198, 244-245, 292-293 aging of data, 167, 168, 169, 170, 245, 302 behavioral response, 170, 171, 172, 174-175 computing requirements, 185, 192 documentation, 26, 268, 269 historical perspectives, 110, 111, 112, 114, 161, 162, 185 Microanalytic Simulation of Households, 183 Microcomputers, see Personal computers Microsimulation models, general, 2, 8-9, 22, 42-51, 97-98, 101-109, 114-122, 284, 308 historical perspectives, 2, 9, 24, 25, 27, 31, 33-38, 97, 107-114 see also specific models Minority groups, 71, 126 Models, general, 1-2, 89-90 defined, 34-35, 76-77, 89 see also specific models MOSES, 108 MRPIS, see Multi-Regional Policy Impact Simulation MS-DOS, 186, 188 Multiple imputation, see Imputation Multi-Regional Policy Impact Simulation (MRPIS), 98, 113, 161-162, 179, 180, 293-294 N National Ambulatory Medical Care Survey, 61 National Bureau of Economic Research, 222 National Center for Health Statistics, 59, 69, 201, 208, 210 National Health Insurance Experiment, 202 National Health Interview Survey, 60, 64, 206, 210 National Health and Nutrition Examination Survey, 60, 61, 129 National Income and Product Accounts, 61, 129 National Institute on Aging, 200, 201, 213, 219, 275 National Longitudinal Surveys of Labor Market Experience, 60, 285 National Long-Term Care Channeling Demonstration, 202 National Medical Care Expenditure Survey, 205, 206, 208, 211, 300-301 National Medical Care Utilization and Expenditure Survey, 113, 205 , 206, 208, 211, 300-301 National Medical Expenditure Survey, 205, 206, 208, 211, 300-301 National Nursing Home Survey, 61, 205, 211 National Science Foundation, 286-287 Near-term approaches, 12, 38, 54, 143-146, 277 Nonparametric techniques, 91 Nonresponse, household/person/item see Imputations; Response rates O Office of Management and Budget (OMB), 37, 38, 49, 62, 63, 83, 137 Office of Tax Analysis, 98, 110, 136, 220, 223-226, 274, 296 Omnibus Budget Reconciliation Act, 42 Orcutt, Guy, 36, 109, 283-284, 287, 289 Organizational factors, 12-13, 62-63 interagency coordination, 6-7, 10, 12-13, 37, 62-63, 146, 149-150, 209, 210, 212, 274-277 see also Matching microsimulation modeling community structure, 12-13, 20, 121-122, 273-289 validation of models, 260-263 Organization for Economic Cooperation and Development, 109, 193 P Panel Study of Income Dynamics, 174, 218, 285 Panel techniques, general, 64, 144, 146, 147, 174, 219 Paperwork Reduction Act, 63 Parameterization, 107, 156, 185 behavioral, 17-18, 75-76, 107, 163, 173-174, 177, 180, 211, 218, 229, 282 policy-related, 74, 75, 107, 173 Participation data and modeling, 46-47, 51, 82, 171, 234, 246-247 AFDC, 136-137, 235, 246-247 vs eligibility data, 44, 64-65, 136-137, 151 see also Survey of Income and Program Participation PENSIM, 183 Pension and Retirement Income Simulation Model (PRISM), 98, 51, 112 , 124, 162, 163, 167, 213-218, 294-295 behavioral response, 170, 171 health care financing, 198, 204 validation, 245-246 see also Long-Term Care Financing Model Personal computers, 34, 54, 108, 121, 186-187, 190, 191-192, 279 Policy analysis, general, 8-13, 24-26, 29-31, 52-88

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Family Support Act, 20-24, 41-51, 187, 234, 261 model validation, 72-84, 86-88 vs policy research, 38, 281-283 social welfare, 34, 38-41, Policy analysis agencies, 52-55, 148, 169, 274-276, 278-281 coordination with statistical agencies, 7, 70-71, 276-277 and decisionmaking staff, 4-5, 80-82, 280-281 interagency coordination, 6-7, 10, 12-13, 37, 62-63, 146, 149-150, 209, 210, 212, 274-277 see also Matching uncertainty, information on, 81-84, 86-87, 86-88 see also specific agencies Political factors, 29, 30, 39-41, 60 POPSIM, 286 Population factors, 1, 21-22, 23, 24, 25, 27, 30, 115, 196, 218, 283-289 black Americans, 71, 126 elderly persons, 196, 201, 205 Macroeconomic-Demographic Model, 200, 213, 215 minorities, 71, 126 stochastic processes, 197 Poverty CPS, 144 SIPP, 138, 147 see also Aid to Families with Dependent Children Medicaid Social welfare policy PRISM, see Long-Term Care Financing Model Pension and Retirement Income Simulation Model Privacy, see Confidentiality Privacy Act, 66 Private sector health insurance, 196, 198, 199, 201, 202, 206, 209, 225 retirement pensions, 212, 216 Program for Better Jobs and Income, 111, 185, 194-195 Prototyping, 159, 160, 192-193, 199 Q Quality of Employment Survey, 65 Quality profiles, 19, 51, 92, 140, 141, 263-264 Questionnaires CPS, 143, 145, 146 duplication of items, 13, 63-64, 277 SIPP, 138, 147 R Reagan administration, 195 Reforms in Income Maintenance (RIM), 36, 109-110, 161 Regression analysis, general, 78-79, 89-90, 91, 93-94, 116, 134, 159, 247, 249 Research, 38-41, 119, 120, 219 academic, 219, 281-282, 285-286 basic, 13, 38, 107-108, 281-283 behavioral, general, 17-18, 164-165, 176, 177, 181, 201-203, 217-219, 276, 282, 283-289 conferences, 20, 282-283 data quality, 69, 119-120 funding, 19, 39, 82 health care policies, 201-203, 209, 211-212 policy analysis vs policy research, 38, 281-283 social science microsimulation, 283-289 validation methods, 20, 241-263, 276, 282, 285-286 validation studies, grants and fellowships, 19, 263 Response rates, 61, 127, 129, 133-134, 137, 145 Retirement History Survey, 174, 218, 219 Retirement income policies, 18, 23, 67, 105, 170, 212-219 see also Dynamic Simulation of Income Model Pension and Retirement Income Simulation Model S Sampling error, 3, 69, 78-79, 86, 88, 90, 91, 95, 129, 215, 244, 257 reuse techniques, 12, 91, 93, 157, 158, 239-240 size, 56, 60, 112, 120, 138, 143, 144, 146, 183, 206, 223-224, 236 weighting, 138, 144, 147, 151 see also Response rates Variance estimation Second-round effects, 23, 53, 107, 113, 115, 178, 229 computable general equilibrium models, 179, 180, 304-305 design for, 153, 154, 155, 163, 164, 178-181 health care policy models, 202-203, 209 MRPIS, 98, 113, 161-162, 179, 180, 293-294 Self-contained modules, 16, 111, 155, 156-157, 157, 159, 161, 248 Sensitivity analysis, 12, 18, 73, 78, 79-80, 82, 83, 91-92, 93, 94 , 99, 157, 158, 177, 192, 215, 218, 228, 229, 233, 235, 237-239, 242, 246, 248-261 Simulated Tax and Transfer System (STATS), 110 Social Policy Simulation Database/Model (SPSD/M), 108, 186, 187-188, 191

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Social science, see Research Social Security Act, 112 Social Security Actuary, 213 Social Security Administration (SSA), 23, 49, 145, 215 matching files, CPS-SSA, 7-8, 18, 67, 104, 123-124, 134-135, 136, 216-217, 216-217, 245, 277, 299 matching files, SIPP-SSA, 217 Social welfare policy, general, 2, 15, 21-24, 25-26, 29-96, 173 administrative data evaluation, 15, 216-217 eligibility, social welfare, 44, 45, 47, 64-65, 105, 136 historical perspectives, 20-24, 34, 278 participation data, 44, 46-47, 51, 64-65, 82, 136, 171 uncertainty, 72-96 (passim) Social Welfare Research Institute, 293 SOCSIM, 286-287 Software, general, 186, 188, 189, 190, 193 CASE, 11, 190 documentation of, 12, 14, 17, 19, 85-86, 267-269, 270-271 graphical interfaces, 121, 189, 190, 193 MS-DOS, 186, 188 see also Documentation and archiving Models specific models SPSD/M, see Social Policy Simulation Database/Model Standards, 4, 158, 159-160 AFDC minimum benefit, 3, 24, 74, 257 design and development, 16-17, 193, 276 documentation, 12, 14, 19, 266-271 State-level factors, 48, 44, 46, 49-50, 83-84, 86, 142, 146, 196, 234 Static models, general, 18, 108, 109-111, 123, 162, 192, 308 aging of data, 49, 105, 163, 164, 165-167, 169, 170, 198-199 214-215, 221, 225-226, 228, 249-250, 292, 293, 298, 302-303 see also specific models Statistical agencies, general coordination with policy analysis agencies, 7, 70-71, 276-277 data quality, 69-72, 124 funding, 6, 37, 56-59, 61-60 historical perspective, 8, 57-62 interagency coordination, 6-7, 10, 12-13, 37, 62-63, 146, 149-150, 209, 210, 212, 274-277 see also Matching social welfare, 59, 57-62 see also specific agencies Statistical matching, 104, 110, 127, 133, 135-136, 204, 222, 307-308 Statistics Canada, 108, 136, 150, 186, 187-188 Statistics of Income, 15, 104, 124, 136, 141, 221, 222, 228, 274, 301 matching files, 67-68, 104, 110, 124, 134, 220, 224, 226-227, 228, 299, 307 STATS, see Simulated Tax and Transfer System Stochastic processes, 197 ''Sunset" provision, 17, 160 Supplemental Security Income, 56, 110 Survey of Consumer Finances, 205 Survey of Economic Opportunity, 220 Survey of Family Growth, 61 Survey of Income and Education, 198, 200 Survey of Income and Program Participation (SIPP), 11, 15-16, 58, 60, 64, 65, 68, 70, 71, 113, 120, 123, 125, 132-133, 137-152 (passim), 228, 285, 301-302 design, 138, 140, 146-148 linkages with CPS, 144, 145, 146, 147, 228 linkages with IRS, 228 matching files, SIPP-SSA, 217 Survey of Institutionalized Persons, 198, 200 Surveys and survey data, 8, 37, 60-61, 63-64 administrative data links, 62, 66-69, 129, 131-132, 137, 141-147 (passim), 149, 151, 169, 216-217, 277 see also Matching census databases, 11, 12, 15-16, 70, 71, 123-152 duplication of items, 13, 63-64, 277 evaluation of, 14, 71, 139-141 health care financing, 203-208 household, 60, 61, 104, 120 see also Questionnaires Response rates specific surveys Sweden, 108-109 T TATSIM, 175 Tax models and policy, 3-4, 9, 23, 25, 34, 35, 53-54, 65, 75-76, 98, 219-230, 274 aging of data, 221, 225-226, 228, 229 behavioral response, 170, 171-172, 176, 178, 179, 226, 228-229 confidentiality, 67-68, 227, 228 databases, 223-226 historical perspectives, 75-76, 86-87, 108, 110, 212, 219-220, 278 HITSM, 98, 113-114, 136, 291-292 imputation, 221, 223, 225, 226-227, 228, 229-230, 306 MRPIS, 98, 113, 160-162, 179, 180, 293-294

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Improving Information for Social Policy Decisions: The Uses of Microsimulation Modeling, Volume I - Review and Recommendations Social Policy Simulation Database/Model, 108, 186, 187-188, 191 Treasury Individual Income Tax Simulation Model, 296-297 see also Department of the Treasury Internal Revenue Service Joint Committee on Taxation Statistics of Income Transfer Income Model 2 Tax Reform Act, 3, 23, 24, 66, 113, 176, 221, 225, 226, 229 Tune-series data and analysis, 2, 50, 52-53, 56, 60, 61, 83, 90, 147, 151 Transfer Income Model (TRIM), 110-111, 161, 184-185 Transfer Income Model 2 (TRIM2), 2, 24, 45, 46, 47, 36, 50, 98, 104 , 133, 136, 162, 187-188, 198, 222, 268, 297-298 aging of data, 49, 107, 163, 166-168, 169, 170, 233 baseline simulation, 107, 124, 256 behavioral response, 170, 171, 172 computing technology, 185, 186, 187-189, 191-192 history, 110-111, 112, 113, 114, 162, 182, 184-185, 221 Medicaid, 198, 204, 207 sensitivity analysis, 99, 233 validation, 3, 78, 99, 233-239, 246-247, 248-259 Treasury Individual Income Tax Simulation Model, 296-297 TRIM2, see Transfer Income Model 2 U Uncertainty, 3, 4, 5, 14, 15, 55, 85, 118-119, 215 behavioral parameters, 17-18, 75-76, 107, 173, 177, 180, 211, 282 confidence intervals, 79-80, 86, 87, 88, 92, 93-96, 244 information on, general, 81-84, 86-87, 86-88 macroeconomic models, 74-75 policy parameters, 74, 75, 107 quality profiles, 19, 51, 92, 140, 141, 263-264 social welfare policy models, 72-96 (passim) see also Errors and error analysis Unemployment, see Employment and unemployment United Kingdom, 193 UNIX, 188 Urban Institute, 110, 111, 112, 183, 184, 290, 297, 248, 259 User accessibility, 14, 16, 18, 158, 159, 279-280 computing innovations, 121, 148, 164, 183, 188, 189, 190, 193 confidentiality and, 66-69, 135, 207, 216, 227, 228 documentation, 266, 267, 269, 270 graphical interface, 121, 189, 190, 193 Utility issues, 2, 13, 25, 26, 54-55, 145, 279 V Validation and evaluation, 2, 3-5, 6, 10, 12, 14-15, 19, 25, 27, 51, 55, 77, 98, 119, 137, 139-152, 192, 209, 215, 229, 231-264 computing technology, 18, 192-193 costs, 259, 260, 261 design and, 16, 157, 158, 159-160 documentation and, 85, 157, 158, 266, 270, 271 external, 5, 12, 19, 77-78, 82, 87-88, 94-95, 99, 233, 235-236, 238 , 246, 262-263, 271, 279-280 historical perspectives, 241-259 information concerning, 81-84, 86-87, 86-88 internal, 78-80, 85 loss functions, 240-241 macroeconomic models, 74-75, 76 organizational factors, 260-263 quality profiles, 19, 51, 92, 140, 141, 263-264 research, 20, 241-263, 276, 282, 285-286 sensitivity analysis, 12, 18, 73, 78, 79-80, 82, 83, 91-92, 93, 94 , 99, 157, 158, 177, 192, 215, 218, 228, 229, 233, 235, 237-239, 242, 246, 248-261 social welfare policy models, 72-84, 86-88 "sunset" provision, 17, 160 surveys, general, 13, 140-141 TRIM2, 3, 78, 99, 233-234, 237-238, 246-247, 248-259 see also Data quality, Errors and error analysis Uncertainty Variance estimation Variance estimation, 18, 91, 92, 192, 239-240, 261-262 reuse techniques, 12, 91, 93, 157, 158, 239-240 W Weighting, 50, 124, 129, 136, 151, 308-309 sample, 138, 144, 147, 151 see also Data aging Wisconsin, 86 Workstations, 188, 189, 190, 191 Wohgeldmodell, 108 Z Zero-based reviews, see "Sunset" provision