The relationships between CEO characteristics and strategic risk-taking in family firms

Abstract

We examine the influences of chief executive officer (CEO) personal characteristics on family firms’ strategic risk-taking. Building on upper echelons theory, we investigate the influences of CEO family relationships, the CEO professional education, other career experiences, tenure, and career horizon have on the risk level a company takes. By analyzing a sample of 107 Italian family firms listed on the Milan Stock Exchange, we find that company’s risk-taking significantly and negatively relates to CEO family relationship and professional education, but positively to CEO career horizon. This provides support to the argument that such CEO personal characteristics are key factors in explaining differences in risk-taking among family firms. Further, our analysis of control variables shows that family firms’ risk-taking relates positively to board size and negatively to company size. These results suggest that company and board characteristics also significantly influence the risk levels taken by a company.     

The supply of education quality in a spatial model with asymmetric moving costs

The paper analyses the characteristics of the supply of higher education in different geographical macroareas using a strategic interaction framework. It focuses on universities operating in centralised funding system that autonomously set the quality of education showing that in equilibrium it is inversely related to students’ moving costs across areas. We show that in the presence of asymmetric information about workers’ ability and asymmetric costs of moving, the only PBE consistent with forward induction involves that only high ability workers acquire education and the quality of education is lower in macroareas where the moving costs are higher. Our model predicts that in economies with centralised university funding, educational policies must be regulated according to the specific socioeconomic characteristics of the area. Direct subsidies to universities may be ineffective in improving the quality of education in the less developed areas. When regional disparities are not too big, efficiency gains may be obtained by reducing moving costs.     

The intraday multivariate structure of the Eurofutures markets

We investigate the multivariate intraday structure in interest rates, focusing on implied forward rates from Eurofutures contracts. Since futures markets are the most liquid for interest rate instruments and they yield high-quality intraday data, it is somehow surprising that their intraday behavior has not been thoroughly studied in the literature.We find interesting similarities with the foreign exchange market: scaling law, intraday patterns, all of which point to the heterogeneity of market participants. Other properties like asymmetric causal information flow between fine and coarse volatilities for the same time series are present in our data. There are also lead–lag correlations across the term structure of implied forward rates, but they tend to disappear as markets mature.A principal component analysis of the short end of the yield curve allows us to determine the most important components and to reduce the number of time series needed to describe the term structure. We find the decomposition rather stable over time. The first component, which describes the curve level, shows an asymmetry in the information flow between volatilities of different time resolution, i.e., the coarse-grained volatility predicts the fine-grained volatility better than the other way around, as observed in the foreign exchange market. The remaining components do not show such an effect, having instead significant negative autocorrelations for the time series themselves. A heterogeneous autoregressive conditional heteroskedasticity (HARCH) model is estimated for the first component and the impact of different market agents is discussed.     

Knightian uncertainty and moral hazard

This paper presents a principal-agent model in which the agent has imprecise beliefs. We model this situation formally by assuming the agent?s preferences are incomplete as in Bewley (1986) [2]. In this setting, incentives must be robust to Knightian uncertainty. We study the implications of robustness for the form of the resulting optimal contracts. We give conditions under which there is a unique optimal contract, and show that it must have a simple flat payment plus bonus structure. That is, output levels are divided into two sets, and the optimal contract pays the same wage for all output levels in each set. We derive this result for the case in which the agent?s utility function is linear and then show it also holds if this utility function has some limited curvature.     

Eliciting Consumer Preferences for Certified Animal‐Friendly Foods: Can Elements of the Theory of Planned Behavior Improve Choice Experiment Analysis?

Models used in neoclassical economics assume human behavior to be purely rational. On the other hand, models adopted in social and behavioral psychology are founded on the “black box” of human cognition. In view of these observations, this paper aims at bridging this gap by introducing psychological constructs in the well‐established microeconomic framework of choice behavior based on random utility theory. In particular, it combines constructs developed employing Ajzen's theory of planned behavior with Lancaster's theory of consumer demand for product characteristics to explain stated preferences over certified animal‐friendly foods (AFF). To reach this objective, a Web survey was administered in the largest five EU‐25 countries: France, Germany, Italy, Spain, and the United Kingdom. Findings identify some salient cross‐cultural differences between northern and southern Europe and suggest that psychological constructs developed using the Ajzen model are useful in explaining heterogeneity of preferences. Implications for policymakers and marketers involved with certified AFF are discussed.     

Random Recursive Partitioning: a matching method for the estimation of the average treatment effect

In this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non‐empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between data sets. The RRP method is “honest” in that it does not match observations “at any cost”: if data sets are separated, the method clearly states it. The match obtained with RRP is invariant under monotonic transformation of the data. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used estimators. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable, in the presence of missing data or when the estimated propensity score is too sensitive to model specifications. Copyright © 2008 John Wiley & Sons, Ltd.     

A GIS-based tool for the management of industrial accidents triggered by volcanic ash fallouts

In this last decade, worldwide attention has been focused on the hazards derived from the interaction between extreme natural phenomena and critical infrastructures and/or chemical and process industry (natural–technological hazards or Na-Tech). Due to the recent occurrence of significant events, great attention has also been given to Na-Tech hazards triggered by volcanic eruptions; in particular, the eruption of the Icelandic volcano alarmed the European community due to the ash fallout over the continent, which caused significant problems for the population, road, rail and air traffic and production activities. This study aims at defining a procedure for the representation of the vulnerability of industrial facilities to potential volcanic ash fallouts. Its implementation on a Geographical Information System has also been executed and a semi-automatic procedure for the vulnerability mapping has been constructed.     

RETHINKING ENDOGENOUS MONEY: A CONSTRUCTIVE INTERPRETATION OF THE DEBATE BETWEEN HORIZONTALISTS AND STRUCTURALISTS

Beyond a widespread agreement on the idea that ‘loans create deposits’ and ‘deposits make reserves’, there is much controversy in the endogenous money literature over the workings of the reserve market, the credit market and the financial markets. In this paper a constructive interpretation of the debate between horizontalists and structuralists is suggested and their arguments are taken forward by showing that these controversial issues can be explained rigorously once a single‐period–continuation framework is adopted.     

WEIGHTED‐AVERAGE LEAST SQUARES (WALS): A SURVEY

Model averaging has become a popular method of estimation, following increasing evidence that model selection and estimation should be treated as one joint procedure. Weighted‐average least squares (WALS) is a recent model‐average approach, which takes an intermediate position between frequentist and Bayesian methods, allows a credible treatment of ignorance, and is extremely fast to compute. We review the theory of WALS and discuss extensions and applications.