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This book reviews the uses and abuses of microsimulation models--large, complex models that produce estimates of the effects on program costs and who would gain and who would lose from proposed changes in government policies ranging from health care to welfare to taxes. Volume 1 is designed to guide future investment in modeling and analysis capability on the part of government agencies that produce policy estimates. It will inform congressional and executive decision makers about the strengths and weaknesses of models and estimates and will interest social scientists in the potential of microsimulation techniques for basic and applied research as well as policy uses. The book concludes that a "second revolution" is needed to improve the quality of microsimulation and other policy analysis models and the estimates they produce, with a special emphasis on systematic validation of models and communication of validation results to decision makers.
This book reviews the uses and abuses of microsimulation modelsâ€"large, complex models that produce estimates of the effects on program costs and who would gain and who would lose from proposed changes in government policies ranging from health care to welfare to taxes. Volume 1 is designed to guide future investment in modeling and analysis capability on the part of government agencies that produce policy estimates. It will inform congressional and executive decision makers about the strengths and weaknesses of models and estimates and will interest social scientists in the potential of microsimulation techniques for basic and applied research as well as policy uses. The book concludes that a "second revolution" is needed to improve the quality of microsimulation and other policy analysis models and the estimates they produce, with a special emphasis on systematic validation of models and communication of validation results to decision makers.
This volume, second in the series, provides essential background material for policy analysts, researchers, statisticians, and others interested in the application of microsimulation techniques to develop estimates of the costs and population impacts of proposed changes in government policies ranging from welfare to retirement income to health care to taxes. The material spans data inputs to models, design and computer implementation of models, validation of model outputs, and model documentation.
This book gives an overview of the state of the art in five different approaches to social science simulation on the individual level. The volume contains microanalytical simulation models designed for policy implementation and evaluation, multilevel simulation methods designed for detecting emergent phenomena, dynamical game theory applications, the use of cellular automata to explain the emergence of structure in social systems, and multi-agent models using the experience from distributed artificial intelligence applied to special phenomena. The book collects the results of an international conference which brought together social scientists and computer scientists both engaged in a wide range of simulation approaches for the first time.
This book is a practical guide on how to design, create and validate a spatial microsimulation model. These models are becoming more popular as academics and policy makers recognise the value of place in research and policy making. Recent spatial microsimulation models have been used to analyse health and social disadvantage for small areas; and to look at the effect of policy change for small areas. This provides a powerful analysis tool for researchers and policy makers. This book covers preparing the data for spatial microsimulation; a number of methods for both static and dynamic spatial microsimulation models; validation of the models to ensure the outputs are reasonable; and the future of spatial microsimulation. The book will be an essential handbook for any researcher or policy maker looking to design and create a spatial microsimulation model. This book will also be useful to those policy makers who are commissioning a spatial microsimulation model, or looking to commission work using a spatial microsimulation model, as it provides information on the different methods in a non-technical way.
Generate and Analyze Multi-Level Data Spatial microsimulation involves the generation, analysis, and modeling of individual-level data allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical programming language. Get Insight into Complex Behaviors The book progresses from the principles underlying population synthesis toward more complex issues such as household allocation and using the results of spatial microsimulation for agent-based modeling. This equips you with the skills needed to apply the techniques to real-world situations. The book demonstrates methods for population synthesis by combining individual and geographically aggregated datasets using the recent R packages ipfp and mipfp. This approach represents the "best of both worlds" in terms of spatial resolution and person-level detail, overcoming issues of data confidentiality and reproducibility. Implement the Methods on Your Own Data Full of reproducible examples using code and data, the book is suitable for students and applied researchers in health, economics, transport, geography, and other fields that require individual-level data allocated to small geographic zones. By explaining how to use tools for modeling phenomena that vary over space, the book enhances your knowledge of complex systems and empowers you to provide evidence-based policy guidance.
The retirement income security of older Americans and the cost of providing that security are increasingly the subject of major debate. This volume assesses what we know and recommends what we need to know to estimate the short- and long-term effects of policy alternatives. It details gaps in data and research and evaluates possible models to estimate the impact of policy changes that could affect retirement income from Social Security, pensions, personal savings, and other sources.
The National Agricultural Statistics Service (NASS) is the primary statistical data collection agency within the U.S. Department of Agriculture (USDA). NASS conducts hundreds of surveys each year and prepares reports covering virtually every aspect of U.S. agriculture. Among the small-area estimates produced by NASS are county-level estimates for crops (planted acres, harvested acres, production, and yield by commodity) and for cash rental rates for irrigated cropland, nonirrigated cropland, and permanent pastureland. Key users of these county-level estimates include USDA's Farm Services Agency (FSA) and Risk Management Agency (RMA), which use the estimates as part of their processes for distributing farm subsidies and providing farm insurance, respectively. Improving Crop Estimates by Integrating Multiple Data Sources assesses county-level crop and cash rents estimates, and offers recommendations on methods for integrating data sources to provide more precise county-level estimates of acreage and yield for major crops and of cash rents by land use. This report considers technical issues involved in using the available data sources, such as methods for integrating the data, the assumptions underpinning the use of each source, the robustness of the resulting estimates, and the properties of desirable estimates of uncertainty.