Organizational career management practices and objective career success: A systematic review and framework

The theorization of the relationship between organizational investments in career development and individual success remains underdeveloped, and empirical tests of this relationship, which have been dispersed among several disciplinary areas, have produced inconsistent results. Addressing these issues, the purpose of this article is to propose a theoretical framework that illustrates why and how organizational career management practices translate into career success and under what circumstances the relationship is effective. Using a systematic review of empirical studies on career management practices and objective success, we identify three theoretical mechanisms - developmental, informational, and relational - and two groups of contingency factors that explain this relationship. Our framework advances the extant literature on organizational career management and provides suggestions to companies for designing effective career management systems.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Risk Arbitrage in Takeovers

This article studies the role of risk arbitrageurs in takeoversand the source of their advantage. We show how the presenceof arbitrageurs affects the value of the target shares, sincearbitrageurs are more likely to tender. Therefore an arbitrageurhas the informational advantage of knowing he bought shares.In equilibrium, the number of arbitrageurs buying shares andthe price they pay are determined endogenously. We also presentseveral empirical implications, including the relationship amongtrading volume, takeover premium, liquidity of the shares, andthe number of risk arbitrageurs investing in one particulardeal.     

Does Gibrat's Law hold among young,small firms?

According to Gibrat's Law of Proportionate Effect, the growth rate of a given firm is independent of its size at the beginning of the examined period. Aimed at extending this line of investigation, the present paper uses quantile regression techniques to test whether Gibrat's Law holds for new entrants in a given industry: that is for new small firms in the early stage of their life cycle. The main finding is that for some selected industries in Italian manufacturing Gibrat's Law fails to hold in the years immediately following start-up, when smaller firms have to rush in order to achieve a size large enough to enhance their likelihood of survival. Conversely, in subsequent years the patterns of growth of new smaller firms do not differ significantly from those of larger entrants, and the Law therefore cannot be rejected.JEL Classification: L11, L60Previous versions of this paper were presented at the 27th Annual EARIE Conference (Lausanne, 7-10 September 2000) and at seminars held between 2000 and 2003 at the Economics Department of Harvard University, the Catholic University of Milan, the University of Ferrara, the University of Bologna, the Bank of Italy, and Athens University of Economics & Business. We would like to thank Carlo Bianchi, Giuseppe Colangelo, Giovanni Dosi, Steven Klepper (Editor), Stephen Martin, Ariel Pakes, Aman Ullah and, in particular, Helen Louri and two anonymous referees for their valuable comments on earlier drafts. Financial support from MIUR (Year 2000; protocol #MM13038538_001; project leader: E. Santarelli) is gratefully acknowledged.Correspondence to: E. Santarelly     

Mean-variance hedging for interest rate models¶with stochastic volatility

The mean-variance hedging approach for pricing and hedging claims in incomplete markets was originally introduced for risky assets. The aim of this paper is to apply this approach to interest rate models in the presence of stochastic volatility, seen as a consequence of incomplete information. We fix a finite number of bonds such that the volatility matrix is invertible and provide an explicit formula for the density of the variance-optimal measure which is independent of the chosen times of maturity. Finally, we compute the mean-variance hedging strategy for a caplet and compare it with the optimal stategy according to the local risk minimizing approach. Received: 14 July 2000 / Accepted: 10 April 2001     

Setting consolidated reporting standards for local government

Under a corporatization trend, traditional financial reporting is generally considered unable to offer a complete view of the economic and financial activities of a group of public entities. Consequently, several reforms and standards have been introduced related to consolidated financial reports. Through interviews with key actors, the authors analysed the normativity process for issuing consolidated reporting standards for local governments in three European countries: Italy, Spain, and Sweden.     

The Effect of Aggregation Bias: An NTB‐modelling Analysis of Turkey's Agro‐food Trade with the EU

We explore how different data aggregation levels affect the gravity estimates of non‐tariff barriers (NTBs) in the agro‐food sector, and we examine their related impacts on policy simulations of an expansion to the European Union (EU) that would include Turkey. We calculate two sets of ad valorem equivalents (AVEs) of NTBs using the gravity approach to disaggregated and aggregated Central Product Classification data for 15 Global Trade Analysis Project (GTAP) agro‐food sectors. We find that the AVEs of NTBs vary substantially across products and that using aggregated data primarily results in greater effects of NTBs. In a second step, we incorporate the AVEs of NTBs into the GTAP model to evaluate Turkey's EU membership and conclude that aggregation bias has considerable effects on both the estimation of NTBs and the general equilibrium simulation results. Utilising different data aggregation levels leads to a great variability of trade costs of NTBs and, hence, to misleading trade and welfare effects.     

Peer effects on risk behaviour: the importance of group identity

This paper investigates whether and to what extent group identity plays a role in peer effects on risk behaviour. We run a laboratory experiment in which different levels of group identity are induced through different matching protocols (random or based on individual painting preferences) and the possibility to interact with group members via an online chat in a group task. Risk behaviour is measured by using the Bomb Risk Elicitation Task and peer influence is introduced by giving subjects feedback regarding group members’ previous decisions. We find that subjects are affected by their peers when taking decisions and that group identity influences the magnitude of peer effects: painting preferences matching significantly reduces the heterogeneity in risk behaviour compared with random matching. On the other hand, introducing a group task has no significant effect on behaviour, possibly because interaction does not always contribute to enhancing group identity. Finally, relative riskiness within the group matters and individuals whose peers are riskier than they are take on average riskier decisions, even when controlling for regression to the mean.