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,
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
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
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.
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.
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
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)
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
cost factors, models, 175, 181
design for, 153, 154, 155, 156, 163, 164, 170-181
health care policy, 201-203, 209, 211
model descriptions, specific, 216, 290-298 (passim)
parameterization, 17-18, 75-76, 107, 163, 173-174, 177, 180, 211, 218, 229, 282
taxpayers, 170, 171-172, 176, 178, 179, 226, 228-229
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
Bootstrap techniques, 12, 91, 239-240
Brookings Institution, 109, 110, 204, 209, 220, 222, 294
Budget analysis, general, 1-24, 36-37
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
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
historical perspectives, 1. 24, 31, 34, 37, 162, 182-185, 186, 189
model descriptions, specific, 290-298 (passim)
personal computers, 34, 54, 108, 121, 186-187, 190, 191-192, 279
TRIM2, 185, 186, 187-189, 191-192
workstations, 188, 189, 190, 191
see also Matching;
Software
User accessibility
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
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
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
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
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
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
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
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
validation and, 16, 157, 158, 159-160
Disaggregation, 61, 132-133, 179
Distributional analysis, 221, 237, 240, 305
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
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
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
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
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
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
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
Frankfurt model, 108
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
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
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
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
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
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
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
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
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
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
Medicare, 61, 124, 178, 195, 199, 201, 202, 203, 207, 225
Medicare Catastrophic Coverage Act, 6, 206-207, 225
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
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
see also specific models
MOSES, 108
MRPIS, see Multi-Regional Policy Impact Simulation
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
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
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
Family Support Act, 20-24, 41-51, 187, 234, 261
model validation, 72-84, 86-88
vs policy research, 38, 281-283
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
elderly persons, 196, 201, 205
Macroeconomic-Demographic Model, 200, 213, 215
stochastic processes, 197
Poverty
CPS, 144
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
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
duplication of items, 13, 63-64, 277
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
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
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
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
see also Documentation and archiving
Models
specific models
SPSD/M, see Social Policy Simulation Database/Model
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
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
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
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
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
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
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
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
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
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
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
see also Data aging
Wisconsin, 86
Workstations, 188, 189, 190, 191
Wohgeldmodell, 108
Z
Zero-based reviews, see "Sunset" provision