The HR-Firm Performance Relationship: Can it be in the Mind of the Beholder?

This study examined whether respondents’ implicit theories of performance could impact their responses to surveys regarding HR practices and effectiveness. Senior Human Resource and Line Executives and MBA, graduate Engineering, and graduate HR students read scenarios of high and low performing firms and were asked to report on the prevalence of various HR practices and effectiveness of the HR function in each firm. Results indicated that all four groups of respondents held implicit theories that high performing firms were characterized by extensive HR practices and had highly effective HR functions relative to low performing firms. Subjects with substantial work experience reported greater differences between and high and low performing firms than did subjects with relatively little work experience. The implications of these results for research on the HR Practices – Firm Performance relationship are discussed

Learning about demand abroad from wholesalers: a B2B analysis. NBB Working Paper No 377, November 2019

This paper uses Business to Business (B2B) transaction level data. It shows that manufacturing firms that initially export via a wholesaler are much more likely to become direct exporters to the same destination in subsequent periods. Theoretically, we rationalise this finding by demonstrating how a connection to a wholesaler reduces uncertainty about the foreign demand. In the data we isolate the channel for demand learning from productivity spillovers. Non-exporting manufacturing firms, previously serving a foreign destination through an exporting wholesaler, have a much higher probability of becoming direct exporters to the same export market in subsequent periods. A connection to an exporting wholesaler results in a probability of exporting to the same destination that is six times higher than a comparable firm without any exposure to the foreign destination

A Bayesian nonparametric approach to modeling market share dynamics

We propose a flexible stochastic framework for modeling the market share dynamics over time in a multiple markets setting, where firms interact within and between markets. Firms undergo stochastic idiosyncratic shocks, which contract their shares, and compete to consolidate their position by acquiring new ones in both the market where they operate and in new markets. The model parameters can meaningfully account for phenomena such as barriers to entry and exit, fixed and sunk costs, costs of expanding to new sectors with different technologies and competitive advantage among firms. The construction is obtained in a Bayesian framework by means of a collection of nonparametric hierarchical mixtures, which induce the dependence between markets and provide a generalization of the Blackwell-MacQueen P\'{o}lya urn scheme, which in turn is used to generate a partially exchangeable dynamical particle system. A Markov Chain Monte Carlo algorithm is provided for simulating trajectories of the system, by means of which we perform a simulation study for transitions to different economic regimes. Moreover, it is shown that the infinite-dimensional properties of the system, when appropriately transformed and rescaled, are those of a collection of interacting Fleming-Viot diffusions.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ392 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Firm and Industry Level Profit Efficiency Analysis Under Incomplete Price Data: A Nonparametric Approach based on Absolute and Uniform Shadow Prices

We discuss the nonparametric approach to profit efficiency analysis at the firm and industry levels in the absence of complete price information, and propose two new insights. First, choosing one commodity (whose price is known) as a numeraire good enables us to measure profit inefficiency in absolute monetary terms. Second, imposing a ‘Law of One Price’ (LoOP) constraint that all firms should be evaluated in terms of the same input-output prices allows us to aggregate firm-level profit inefficiencies to the overall industry inefficiency. Moreover, the LoOP restrictions increase the discriminatory power of the method by better capturing firm-level allocative inefficiencies. Besides the measurement of profit losses, the presented approach enables one to recover absolute price information from quantity data. We conduct a series of Monte Carlo simulations to study the performance of the proposed approach in controlled production environments.Profit Efficiency, Industry Inefficiency, Data Envelopment Analysis, Absolute Prices, Law of One Price, Weight Restrictions, Simulation

Derivatives and Credit Contagion in Interconnected Networks

The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radically altered the dynamics of contagion for more than a decade, models quantifying their impact on systemic risk are still missing. Here, we examine contagion through credit default swaps in a stylized economic network of corporates and financial institutions. We analyse such a system using a stochastic setting, which allows us to exploit limit theorems to exactly solve the contagion dynamics for the entire system. Our analysis shows that, by creating additional contagion channels, CDS can actually lead to greater instability of the entire network in times of economic stress. This is particularly pronounced when CDS are used by banks to expand their loan books (arguing that CDS would offload the additional risks from their balance sheets). Thus, even with complete hedging through CDS, a significant loan book expansion can lead to considerably enhanced probabilities for the occurrence of very large losses and very high default rates in the system. Our approach adds a new dimension to research on credit contagion, and could feed into a rational underpinning of an improved regulatory framework for credit derivatives.Comment: 26 pages, 7 multi-part figure

Dynamic asset (and liability) management under market and credit risk

We introduce a modelling paradigm which integrates credit risk and market risk in describing the random dynamical behaviour of the underlying fixed income assets. We then consider an asset and liability management (ALM) problem and develop a mul- tistage stochastic programming model which focuses on optimum risk decisions. These models exploit the dynamical multiperiod structure of credit risk and provide insight into the corrective recourse decisions whereby issues such as the timing risk of default is appropriately taken into consideration. We also present a index tracking model in which risk is measured (and optimised) by the CVaR of the tracking portfolio in relation to the index. Both in- and out-of-sample (backtesting) experiments are undertaken to validate our approach. In this way we are able to demonstrate the feasibility and flexibility of the chosen framework