Wer erbt Mehr? Erbschaften, Sozialstruktur und Alterssicherung

How do inheritances affect social structure, wealth formation, and provision for old age? Who benefits the most from inheritances? We analyse these questions based on the 2001 wave of the Socio-Economic Panel, which gives information on the deceased, and on the type, time, chance, and amount of the inheritance. Our findings confirm the theoretical hypotheses in general. Inheritance research is intergenerational research: most inheritances come from parents and benefit the recipients in the second half of their lives. However, the chances of receiving an inheritance and amounts inherited are distributed very unevenly. West Germans inherit significantly more frequently and larger amounts than East Germans due to the continuing effects of conditions in the former GDR. Foreigners have particularly low chances of receiving an inheritance. Thus, provision for old age through inheritance is limited mainly to specific groups of people. The most advantaged groups are the highly educated classes, which already receive higher (retirement) income.     

Transfer programs and aggregate household formations

The number of households in the United States increased by over fifty percent in the 1960s and 1970s, nearly double the rate of population growth. Part of the increase is explained by the movement of large cohort groups of the population into prime household-forming age categories, but higher headship rates also contribute. Age-specific headship rate increases result from non-demographic factors, and this paper focuses on the role of government transfer payment programs. Specifically considered are Social Security, Aid to Families with Dependent Children, and Food Stamps. These programs are found to have accounted for as many as 4 million net household formations between 1961 and 1984. The findings have implications for expected households formations in the 1990s.     

Introduction to Special Issue on Sustainable Corporate Governance

Papers in this special issue focus on sustainable corporate governance measures in the aftermath of the financial crisis, and a background environment of increased scepticism over executive pay and corporate behaviour more generally. The authors provide an overview of recent corporate governance reforms, including ‘say‐on‐pay’ and gender‐pay‐gap transparency, and explain how the research papers in the special issue contribute to understanding of corporate governance research and practice in the areas of boards of directors, corporate governance reform and corporate culture and risk‐taking.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems’ criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank‐order of food and agriculture system criticality to a large degree.     

F?rdern aktienkursbasierte Vergütungsinstrumente langfristig orientierte Unternehmensentscheidungen? Lehren aus der Kreditkrise

The question whether stock-based management incentives encourage long-term oriented management decisions is the topic of a controversial public debate. Also, the existing academic literature provides no clear picture, mainly due to endogeneity problems. In this paper, we reexamine the issue in the context of the recent credit crises, which allows us to solve the endogeneity problem. In the empirical analysis we find that firms, which have awarded stock-based incentives to their executives prior or during the financial crises, face substantially lower cuts in investment spending. These firms also show higher levels of R&D investments during the financial crises. Overall, our results suggest that stock based long-term incentives promote long-term oriented management decisions.     

Computer incompetents in the cross-hairs.

Human Resources managers are distressingly aware that more than half the middle and senior managers (40- and 50-year-olds) in their organizations are technologically incompetent. Having spent millions on technology, most corporate CEOs, many of whom can't use a computer either, are drawing a bead on middle managers who are passively resisting the computer revolution. Physician executives are no exception. Given the current shortage of physician managers with good track records and people skills, can middle managers skirt technology issues until retirement if they dance slowly enough? It's doubtful. Organizations are using new tactics to bar technically challenged managers from their ranks: They will only interview candidates with mastery of specific software and they're posting job opportunities on their web sites. Those who still believe they can ride out the computer revolution may want to rethink that strategy.