Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions

Using a high‐stakes field experiment conducted with a financial brokerage, we implement a novel design to separately identify two channels of social influence in financial decisions, both widely studied theoretically. When someone purchases an asset, his peers may also want to purchase it, both because they learn from his choice (“social learning”) and because his possession of the asset directly affects others' utility of owning the same asset (“social utility”). We randomize whether one member of a peer pair who chose to purchase an asset has that choice implemented, thus randomizing his ability to possess the asset. Then, we randomize whether the second member of the pair: (i) receives no information about the first member, or (ii) is informed of the first member's desire to purchase the asset and the result of the randomization that determined possession. This allows us to estimate the effects of learning plus possession, and learning alone, relative to a (no information) control group. We find that both social learning and social utility channels have statistically and economically significant effects on investment decisions. Evidence from a follow‐up survey reveals that social learning effects are greatest when the first (second) investor is financially sophisticated (financially unsophisticated); investors report updating their beliefs about asset quality after learning about their peer's revealed preference; and, they report motivations consistent with “keeping up with the Joneses” when learning about their peer's possession of the asset. These results can help shed light on the mechanisms underlying herding behavior in financial markets and peer effects in consumption and investment decisions.     

Financing Unemployment Benefits: Dismissal versus Employment Taxes

Abstract. This paper investigates the effects of using dismissal taxes to finance unemployment benefits. We compare dismissal and employment taxes in a model with search frictions. Employment taxes give rise to externalities because firms do not take into account the effects their dismissal decisions have on others. These externalities can be tackled by using dismissal taxes to finance unemployment insurance. Taking into account the budget for unemployment insurance, employment taxes can be reduced by more than is necessary to offset the adverse effect of dismissal taxes on the value of the firm. The introduction of dismissal taxes leads to higher job creation and lower unemployment.     

Hot times,hot places. Youths’ risk perceptions and risk management in Chicago and Rotterdam

Many young people in low-income neighbourhoods are considered to be at risk of poor social outcomes and of becoming victims of violence and crime. This exploratory study focuses on how young people perceive risk and navigate their environment in two low-income, multi-ethnic neighbourhoods, one in Rotterdam and one in Chicago. We conducted in-depth interviews and mental mapping exercises with young people aged 14–19 in both sites. We found that neighbourhoods matter because they expose young people to certain kinds of risks, but also that their responses were shaped by their perceptions and interpretations of these risks. Moreover, we illustrate the dynamic and interactive nature of these processes of risk perception and management by discussing the ways in which different groups occupy and use, or travel through, public space at different times of the day. We argue that risk in disadvantaged neighbourhoods is not a static dimension in young people’s lives but rather emerges from an ongoing and complex interaction between perception, interpretation and response that can be seen as a kind of boundary work.     

Circular literacy. A knowledge-based approach to the circular economy

ABSTRACT

This paper applies a knowledge-based approach to study the role of circular economy (CE) for sustainable development. An analysis of publications from practice-oriented CE advocates is deployed to study the types of knowledge actors foster when they pursue the transition towards a CE. With the proposed term ‘circular literacy’ (CL), an analytical framework is provided to differentiate between different types of knowledge relevant for a transition towards the CE: system, target, and transformation knowledge. The main emphasis is on identifying knowledge-related prerequisites that prevent or stimulate the transformative potential of CE thinking and practices for sustainable development. The analysis reveals how the CL in the sample is confined by a narrow economic vision of the CE, lacking a reflection of political and sociocultural aspects that are needed to be addressed in future research to gain a robust concept of a CE-driven systemic transition towards sustainability.     

Aufsichtsratsverflechtungen und ihr Einfluss

This article examines the development of executive compensation of the German DAX corporations from 2001 to 2006. A higher executive compensation requires an approval of the supervisory board. Accordingly, we investigate the social network of the board of directors of these companies and use several variables describing the structure of this network as covariates of the development of the executive compensation in a panel analysis. Fixed and time effects panel estimations imply that a rising number of executives in the supervisory board of other companies results in increasing compensation. By contrast, executive compensation decreases when the supervisory board of the respective company is controlled by a growing number of executives from foreign companies.     

A Delphi Method Expert Survey to Derive Standards for Flood Damage Data Collection

For the purpose of flood damage analyses reliable, comparable, comprehensive, consistent, and up-to-date data are an indispensable need. Like in many other countries a database with this kind of datasets does not exist in Germany. To establish it, standards have to be set for flood damage data collection. We approached this problem by questioning experts about their information needs for flood damage analysis. This survey is done by applying a Delphi survey approach. The aptitude of the Delphi approach to assess, structure, and standardize expert knowledge is evaluated in this article. In the survey a panel of 55 experts working in the field of flood damage analysis for insurances, engineering companies/consultancy, public water management, and universities and other scientific institutions helped to identify common information needs. The multi-step Delphi method proved to reduce the deviation of answers thereby enabling consensual results and also enhanced the quality by modifying group answers in the direction of experience based answers. There was also a high level of congruence in information needs between experts from different fields of employment that allowed the derivation of common standards.     

Modelling price paths in on-line auctions: smoothing sparse and unevenly sampled curves by using semiparametric mixed models

Summary.  On-line auctions pose many challenges for the empirical researcher, one of which is the effective and reliable modelling of price paths. We propose a novel way of modelling price paths in eBay's on-line auctions by using functional data analysis. One of the practical challenges is that the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data, which can be difficult if the data are irregularly distributed. We present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. As well as being able to handle sparse and unevenly distributed data, the model also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's on-line auctions. On-line auctions produce monotonic increasing price curves that are often correlated across auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also manages to capture the underlying monotonic trend in the data without imposing model constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an on-line auction, our approach also results in more accurate price predictions compared with standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants.     

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article, we apply the Normal-Gamma shrinkage prior to the VAR with stochastic volatility case and derive its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariance parameters of the VAR along with Gamma priors on a set of local and global prior scaling parameters. In a second step, we modify this prior setup by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. Two simulation exercises show that the proposed framework yields more precise estimates of model parameters and impulse response functions. In addition, a forecasting exercise applied to U.S. data shows that this prior performs well relative to other commonly used specifications in terms of point and density predictions. Finally, performing structural inference suggests that responses to monetary policy shocks appear to be reasonable.