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There is probably no concept other than saving for which U.S. official agencies issue annual estimates that differ by more than a third, as they have done for net household saving, or for which reputable scholars claim that the correct measure is close to ten times the officially published one. Yet despite agreement among economists and policymakers on the importance of this measure, huge inconsistencies persist. Contributors to this volume investigate ways to improve aggregate and sectoral saving and investment estimates and analyze microdata from recent household wealth surveys. They provide analyses of National Income and Product Account (NIPA) and Flow-of-Funds measures and of saving and survey-based wealth estimates. Conceptual and methodological questions are discussed regarding long-term trends in the U.S. wealth inequality, age-wealth profiles, pensions and wealth distribution, and biases in inferences about life-cycle changes in saving and wealth. Some new assessments are offered for investment in human and nonhuman capital, the government contribution to national wealth, NIPA personal and corporate saving, and banking imputation

Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at UrbanaChampaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo `a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Minist´erio da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the collaborating institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energ´eticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ci`encies de l'Espai, the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. The DES data management system is supported by the National Science Foundation under Grants No. AST-1138766 and No. AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under Grants No. AYA2015-71825, No. ESP2015-88861, No. FPA2015-68048, No. SEV-2012-0234, No. SEV2016-0597, and No. MDM-2015-0509, some of which include European Research Development Fund (ERDF) funds from the European Union. I. F. A. E. is partially funded by the Centres de Recerce de Catalunya (CERCA) program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including European Research Council Grants No. 240672, No. 291329, and No. 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics, through Project No. CE110001020.

Funding Information: The CMB-S4 collaboration ( https://cmb-s4.org/ ) is working to plan, construct, and operate a next-generation, multisite CMB experiment in the 2020s. The collaboration is led by an elected Governing Board, Spokespeople, Committee Chairs, and Executive Team. Funding for the CMB-S4 Integrated Project Office is provided by the Department of Energy's Office of Science (project level CD-0) and by the National Science Foundation through the Mid-Scale Research Infrastructure-R1 award OPP-1935892. This research used resources of Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science User Facility operated under Contract No. DE-AC02-06CH11357. This document was prepared by the CMB-S4 collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. Work at Lawrence Berkeley National Laboratory was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work at SLAC National Accelerator Laboratory is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. In the United States, work on CMB-S4 by individual investigators has been supported by the National Science Foundation (awards 1248097, 1255358, 1815887, 1835865, 1852617, 2009469), the Department of Energy (awards DE-SC0009919, DE-SC0009946, DE-SC0010129, DE-SC0011784), and the National Aeronautics and Space Administration (award ATP-80NSSC20K0518). In Australia, the Melbourne authors acknowledge support from an Australian Research Council Future Fellowship (FT150100074). In Canada, R.H. is supported by the Discovery Grants program from NSERC, and acknowledges funding from CIFAR, the Sloan Foundation, and the Dunlap family. In Italy, C.B. acknowledges support under the ASI COSMOS and INFN INDARK programs. In the Netherlands, D.M. acknowledges NWO VIDI award number 639.042.730. In Switzerland, J.C. is supported by an SNSF Eccellenza Professorial Fellowship (No. 186879). In the United Kingdom, A.L., G.F., and J.C. are supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC grant Agreement No. [616170]. A.L. also acknowledges STFC award ST/P000525/1. S.M. is supported by the research program Innovational Research Incentives Scheme (Vernieuwingsimpuls), which is financed by the Netherlands Organization for Scientific Research through the NWO VIDI grant No. 639.042.612-Nissanke and the Labex ILP (reference ANR-10-LABX-63) part of the Idex SUPER, received financial state aid managed by the Agence Nationale de la Recherche,as part of the program Investissements d'avenir under the reference ANR-11-IDEX-0004-02. Some computations in this paper were run on the Odyssey cluster, supported by the FAS Science Division Research Computing Group at Harvard University. Publisher Copyright: © 2022. The Author(s). Published by the American Astronomical Society. ; CMB-S4-the next-generation ground-based cosmic microwave background (CMB) experiment-is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the universe. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semianalytic projection tool, targeted explicitly toward optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2-3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments, given a desired scientific goal. To form a closed-loop process, we couple this semianalytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r > 0.003 at greater than 5 sigma, or in the absence of a detection, of reaching an upper limit of r < 0.001 at 95% CL. ; Peer reviewed