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We study the dynamics of specialness for 1-day repo contracts on Italian government bonds over a 10-year sample period. As predicted by Duffie's (1996) model, our results show that collateral supply is a significant factor for specialness. However, we enrich that finding by also showing a clear impact from repo liquidity, collateral riskiness, information uncertainty and short-selling proxies, revealing the importance of speculative bond demand for specialness. During crisis periods, bond fire sales and European Central Bank interventions also have a large impact on repo specialness. We identify recurrent patterns for specialness around bond auctions. Specialness increases steadily from the auction announcement date until a few days before the auction settlement date, which is consistent with overbidding behavior and a short selling of treasuries (via reverse repos) from primary dealers ahead of auctions.

Knowledge, Social Institutions and the Division of Labour gives rise to a new and richer institutional analysis of the economy centred around the analysis of language, the division of labour and social knowledge. It is in this perspective that the economic analysis of institutions comes to be associated with the study of civil society, or with the broad framework of communication and coordination behind the interaction of individuals in economic and non-economic spheres. This fascinating book is divided into three parts beginning with the issue of the development of science as an aspect of the division of labour, starting from methodological problems on the communication of scientific knowledge. The volume goes on to explore issues on the moral bases of social interaction and, more particularly, of commercial society before ending with in depth analyses of questions on the division of labour, social institutions and the diffusion of knowledge in society

Social Security Administration disability programs are expensive, growing, and headed toward bankruptcy. People with psychiatric disabilities now constitute the largest and most rapidly expanding subgroup of program beneficiaries. Evidence-based supported employment is a well-defined, rigorously tested service model that helps people with psychiatric disabilities obtain and succeed in competitive employment. Providing evidence-based supported employment and mental health services to this population could reduce the growing rates of disability and enable those already disabled to contribute positively to the workforce and to their own welfare, at little or no cost (and, depending on assumptions, a possible savings) to the government.

The care of Americans with severe chronic illnesses is disorganized, unnecessarily costly, and undisciplined by sound clinical science. The federal government should invest in a crash program to improve the scientific basis of managing chronic illness, and the Centers for Medicare and Medicaid Services (CMS) should extend its pay-for-performance (P4P) agenda to ensure that within ten years all Americans with severe chronic illnesses have access to accountable health care organizations providing evidence-based prospective care. This paper recommends a strategy for achieving this goal.

Abstract
During the 2017 Spring Forecasting Experiment in NOAA's Hazardous Weather Testbed, 62 meteorologists completed a survey designed to test their understanding of forecast uncertainty. Survey questions were based on probabilistic forecast guidance provided by the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e). A mix of 20 multiple-choice and open-ended questions required participants to explain basic probability and percentile concepts, extract information using graphical representations of uncertainty, and determine what type of weather scenario the graphics depicted. Multiple-choice questions were analyzed using frequency counts, and open-ended questions were analyzed using thematic coding methods. Of the 18 questions that could be scored, 60%–96% of the participants' responses aligned with the researchers' intended response. Some of the most challenging questions proved to be those requiring qualitative explanations, such as to explain what the 70th-percentile value of accumulated rainfall represents in an ensemble-based probabilistic forecast. Additionally, participants providing answers not aligning with the intended response oftentimes appeared to consider the given information with a deterministic rather than probabilistic mindset. Applications of a deterministic mindset resulted in tendencies to focus on the worst-case scenario and to modify understanding of probabilistic concepts when presented with different variables. The findings from this survey support the need for improved basic and applied training for the development, interpretation, and use of probabilistic ensemble forecast guidance. Future work should collect data for a larger sample size to examine the knowledge gaps across specific user groups and to guide development of probabilistic forecast training tools.