Correcting for the Missing Rich: An Application to Wealth Survey Data

It is a well‐known criticism that if the distribution of wealth is highly concentrated, survey data are hardly reliable when it comes to analyzing the richest parts of society. This paper addresses this criticism by providing a general rationale of the underlying methodological problem as well as by proposing a specific methodological approach tailored to correcting the arising bias. We illustrate the latter approach by using Austrian data from the Household Finance and Consumption Survey. Specifically, we identify suitable parameter combinations by using a series of maximum‐likelihood estimates and appropriate goodness‐of‐fit tests to avoid arbitrariness with respect to the fitting of the Pareto distribution. Our results suggest that the alleged non‐observation bias is considerable, accounting for about one quarter of total net wealth in the case of Austria. The method developed in this paper can easily be applied to other countries where survey data on wealth are available.     

Changes in Global Trade Patterns and Women's Employment in Manufacturing, 1995–2011

This study investigates the feminization and defeminization trends in manufacturing employment in thirty countries from 1995 to 2011. Utilizing two separate methods, structural decomposition analysis (SDA) and factor content analysis (FCA), the study identifies the major industries and trade partners behind the structural shifts in trade that have induced changes in employment and thus in the rates of women’s employment. The findings highlight that, as a general trend, defeminization in manufacturing has persisted in the Global North, led by a negative trade impact in low-technology industries. In the Global South, feminization and defeminization trends are not as straightforward. Despite positive changes in women’s share of employment in medium-high- and high-technology industries, negative gender bias effects of trade changes are found particularly in high-technology industries, where occupations are notably gendered.     

Risk identification in product innovation projects: new perspectives and lessons learned

The paper addresses the improvement of innovation project (IP) risk identification by applying the RBS (Risk Breakdown Structure) method, and represents a follow-up analysis of the subject (a case of IP before and after applying the method). The activities of risk identification are an initial phase in most of risk management processes and classifications. High-level risk-taking is one of the key characteristics of IPs, due to the fact that this type of projects is related to implementation of new ideas. An example illustrates the combination of RBS/WBS for evaluating the risk concentration, called Risk Breakdown Matrix. This research has proven that implementation of the presented approach in the context of the Serbian market can improve efficiency of all phases of the project, especially those related to planning phase.     

The more others care,the more you share? – Social contagion as a stardom trigger of social media superstars –

Emerging superstars on social media platforms reshape the media landscape. This research analyses social contagion as a stardom trigger of social media superstars (SMS). We argue that in addition to serving as a quality indicator, the number of observed consumers of SMS performances also indicates the suitability of discussing the SMS performances with others. We experimentally manipulated the number of previous views of a YouTube video and find that a high number of previous views significantly increases the perceived quality and the video’s discussion suitability even when holding all objective video characteristics constant. We discuss implications for aspiring SMS and (online) marketers.     

Forecasting the Adoption of GM Oilseed Rape: Evidence from a Discrete Choice Experiment in Germany

This paper explores farmers’ willingness to adopt genetically modified (GM) oilseed rape prior to its commercial release and estimates the ‘demand’ for the new technology. The analysis is based upon choice experiments with 202 German arable farmers. A multinomial probit estimation reveals that GM attributes such as gross margin, expected liability from cross pollination, or flexibility in returning to conventional oilseed rape significantly affect the likelihood of adoption. Neighbouring farmers’ attitudes towards GM cropping and a number of farmer and farm characteristics were also found to be significant determinants of prospective adoption. Demand simulations suggest that adoption rates are very sensitive to the profit difference between GM and non‐GM rape varieties. A monopolistic seed price would substantially reduce demand for the new technology. A monopolistic seed supplier would reap between 45% and 80% of the GM rent, and the deadweight loss of the monopoly would range between 15% and 30% of that rent. The remaining rent for farmers may be too small to outweigh possible producer price discounts resulting from the costs of segregating GM and non‐GM oilseed rape along the supply chain.     

Are we nice(r) to nice(r) people?—An experimental analysis

We experimentally investigate whether individuals can reliably detect cooperators (the nice(r) people) in an anonymous decision environment involving “connected games.” Participants can condition their choices in an asymmetric prisoners’ dilemma and a trust game on past individual (their partner’s donation share to a self-selected charity) and social (whether their partner belongs to a group with high or low average donations) information. Thus, the two measures of niceness are the individual donation share in the donation task, and the cooperativeness of one’s choice in the two games. We find that high donors achieve a higher-than-average expected payoff by cooperating predominantly with other high donors. Group affiliation proved to be irrelevant. Electronic Supplementary Material Supplementary material is available in the online version of this article at . JEL Classification C91, C72, D3     

Information Cascades in the Labor Market

A model of herding behavior in the labor market is presented where employers receive signals with limited precision about the workers types, and can observe previous employers decisions. Both the employer and the worker can influence the signal probabilities. In particular, the employer tries to increase the precision of the signal about the workers type whereas the worker wants to get a good signal, independent of her type. In a two-period model, we derive conditions for an equilibrium in which only down-cascades occur, i.e., the second employer does not hire a worker with a bad history even if he receives a favorable private signal about the workers type, but he follows his own signal if the workers history is good.     

Job Creation and Job Destruction in a Regulated Labor Market: The Case of Austria

We study Austrian job reallocation in the period of 1978 to 1998, using a large administrative dataset where we correct for spurious entries and exits of firms. We find that on average 9 out of 100 randomly selected jobs were created within the last year, and that about 9 out of randomly selected 100 jobs were destroyed within the next year. Hence, the magnitude of Austrian job flows seems to be comparable to other countries, similar to the well-known results of Davis et al. (1996) for the United States. Job reallocation appears to be driven primarily by idiosyncratic shocks. However, job creation increases significantly during cyclical upswings whereas job destruction rises in downturns. We also find substantial persistence of job creation and destruction. The pronounced pattern of job reallocation rates falling with firm size and age continues to hold when we use a set of controls. Finally, we show that – controlling for sector and firm size composition – Austrian job reallocation rates are only half the rates for the U.S. This result is not surprising given the impact of tighter regulation and labor law in Austria.     

Modelling of repairable systems with various degrees of repair

The availability of a stochastic repairable system depends on the failure behaviour and on repair strategies. In this paper, we deal with a general repair model for a system using auxiliary counting processes and corresponding intensities which include various degrees of repair (between minimal repair and perfect repair). For determining the model parameters we need estimators depending on failure times and repair times: maximum likelihood (ML) estimator and Bayes estimators are considered. Special results are obtained by the use of Weibull-type intensities and random observation times.