David S. Johnson – författare

Many federal agencies provide data and statistics on inequality and related aspects of household income, consumption, and wealth (ICW). However, because the information provided by these agencies is often produced using different concepts, underlying data, and methods, the resulting estimates of poverty, inequality, mean and median household income, consumption, and wealth, as well as other statistics, do not always tell a consistent or easily interpretable story. Measures also differ in their accuracy, timeliness, and relevance so that it is difficult to address such questions as the effects of the Great Recession on household finances or of the Covid-19 pandemic and the ensuing relief efforts on household income and consumption. The presence of multiple, sometimes conflicting statistics at best muddies the waters of policy debates and, at worst, enable advocates with different policy perspectives to cherry-pick their preferred set of estimates. Achieving an integrated system of relevant, high-quality, and transparent household ICW data and statistics should go far to reduce disagreement about who has how much, and from what sources. Further, such data are essential to advance research on economic wellbeing and to ensure that policies are well targeted to achieve societal goals.Creating an Integrated System of Data and Statistics on Household Income, Consumption, and Wealth reviews the major household ICW statistics currently produced by U.S. statistical agencies and provides guidance for modernizing the information to better inform policy and research, such as understanding trends in inequality and mobility. This report provides recommendations for developing an improved 21st century data system for measuring the extent to which economic prosperity is shared by households throughout the population and for understanding how the distribution of resources is affected by government policy and economic events.

Interest has grown recently in the application of computational and statistical tools to problems in the analysis of algorithms. In many algorithmic domains worst-case bounds are too pessimistic and tractable probabilistic models too unrealistic to provide meaningful predictions of practical algorithmic performance. Experimental approaches can provide knowledge where purely analytical methods fail and can provide insights to motivate and guide deeper analytical results. The DIMACS Implementation Challenge was organized to encourage experimental work in the area of network flows and matchings. Participants at sites in the U.S., Europe, and Japan undertook projects between November 1990 and August 1991 to test and evaluate algorithms for these problems. The Challenge culminated in a three-day workshop held in October 1991 at DIMACS.This volume contains the revised and refereed versions of twenty-two of the papers presented at the workshop, along with supplemental material about the Challenge and the Workshop.