Overconfidence in Labor Markets

This chapter reviews how worker overconfidence affects labor markets. Evidence from psychology and economics shows that in many situations, most people tend to overestimate their absolute skills, overplace themselves relative to others, and overestimate the precision of their knowledge. The chapter starts by reviewing evidence for overconfidence and for how overconfidence affects economic choices. Next, it reviews economic explanations for overconfidence. After that, it discusses research on the impact of worker overconfidence on labor markets where wages are determined by bargaining between workers and firms. Here, three key questions are addressed. First, how does worker overconfidence affect effort provision for a fixed compensation scheme? Second, how should firms design compensation schemes when workers are overconfident? In particular, will a compensation scheme offered to an overconfident worker have higher-or lower-powered incentives than that offered to a worker with accurate self-perception? Third, can worker overconfidence lead to a Pareto improvement? The chapter continues by reviewing research on the impact of worker overconfidence on labor markets where workers can move between firms and where neither firms nor workers have discretion over wage setting. The chapter concludes with a summary of its main findings and a discussion of avenues for future research

In silico constraint-based strain optimization methods: the quest for optimal cell factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.The work of P.M. was supported by the Portuguese Science Foundation (FCT) through Ph.D. grant SFRH/BD/61465/2009. We thank the FCT Strategic Project of UID/BIO/04469/2013 unit, the project RECI/BBB-EBI/0179/201 (FCOMP-01-0124-FEDER-027462) and the project “BioInd-Biotechnology and Bioengineering for Improved Industrial and Agro-Food Processes,” (NORTE-07-0124-FEDER-000028) cofunded by the Programa Operacional Regional do Norte (ON.2-O Novo Norte), QREN, FEDER, and the project “DeYeastLib-Designer Yeast Strain Library Optimized for Metabolic Engineering Applications” (ERA-IB-2/0003/2013) funded by national funds through FCT/MCTES

Population-based survival estimates for childhood cancer in Australia during the period 1997-2006

Background: This study provides the latest available relative survival data for Australian childhood cancer patients. Methods: Data from the population-based Australian Paediatric Cancer Registry were used to describe relative survival outcomes using the period method for 11 903 children diagnosed with cancer between 1983 and 2006 and prevalent at any time between 1997 and 2006. Results: The overall relative survival was 90.4% after 1 year, 79.5% after 5 years and 74.7% after 20 years. Where information onstage at diagnosis was available (lymphomas, neuroblastoma, renal tumours and rhabdomyosarcomas), survival was significantly poorer for more-advanced stage. Survival was lower among infants compared with other children for those diagnosed with leukaemia, tumours of the central nervous system and renal tumours but higher for neuroblastoma. Recent improvements in overall childhood cancer survival over time are mainly because of improvements among leukaemia patients. Conclusion: The high and improving survival prognosis for children diagnosed with cancer in Australia is consistent with various international estimates. However, a 5-year survival estimate of 79% still means that many children who are diagnosed with cancer will die within 5 years, whereas others have long-term health morbidities and complications associated with their treatments. It is hoped that continued developments in treatment protocols will result in further improvements in survival