Institutional distance among country influences and environmental performance standardization in multinational enterprises

This research compares and contrasts the findings in Aguilera-Caracuel et al. (2013) with the outcomes of applying fuzzy-set qualitative comparative analysis (fsQCA) — a methodological strategy that gathers quantitative and qualitative information to explain complexity at the case level and generality across cases. Using the same sample of 128 multinational enterprises (MNEs) with headquarters and subsidiaries based in the USA, Canada, France, and Spain, we identify a set of relevant configurations of causes and conditions to explain environmental performance standardization. By avoiding separate treatments for each variable, which is typical in multiple regression analysis (MRA), we overcome prior limitations and propose a new way of understanding this phenomenon. In summary, our results significantly reinforce and complement the previous results.     

The Gain‐Loss Spread: A New and Intuitive Measure of Risk

The standard deviation, arguably the most widely‐used measure of risk, suffers from at least two limitations. First, the measure has little intuitive appeal (defined as it is by the square root of the average quadratic deviation from the arithmetic mean return). Second, investors tend to associate risk more with bad outcomes than with volatility per se. To overcome these limitations, this article introduces a new measure of risk, the gain‐loss spread (GLS), which takes into account the probability of a loss, the average size of the loss, and the average gain—all variables that investors consider relevant when assessing risk. The author presents evidence that the GLS is both highly correlated with the standard deviation—thus providing basically the same information about risk—and more correlated with mean returns than both the standard deviation and beta, thereby offering a tighter link between risk and return.     

Technical change and irreversible investment under risk

A stochastic dynamic model was constructed to analyze investment decisions of an individual farme under risk in the presence of legibilities, embedded technical change and indivisible capital. An analytical solution was obtained and its local behavior studied by numerical methods. Optimal investment is obtained by regulating the difference between the desired and actual capital stocks between two barriers that define an inaction interval. While the desired capital drifts between the barriers, no action is taken. If the desired capital touches the upper barrier, the farmer invests pushing the average efficiency of the actual capital stock up. This in turn raises the desired capital even higher and contracts the inaction interval. If these effects are strong enough, the farmer will invest again until the potential gains of the technological package are exhausted. If the desired capital falls enough, the farmer disinvests, pushing down the average productivity and expanding the inaction interval. Disinvestment continues until it slops either because the inaction interval becomes so wide that it is no longer optimal to disinvest or because the actual capital stock is so small that it is no longer profitable to produce.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

On the Role of Weight Restrictions in Data Envelopment Analysis

This paper examines the role that weight restrictions play in Data Envelopment Analysis (DEA). It is argued that the decision to include a factor (input or output) in a DEA model represents an implicit judgement that the factor has a non-trivial weight. It therefore seems perverse to allow DEA to assign a trivial weight to that factor in assessing the efficiency of a unit. There is therefore a strong case for imposing restrictions on factor weights. However, many existing methods of weight restriction are in practice unwieldy. This paper proposes an alternative approach we term contingent weight restriction which is both practical and intellectually consistent with the DEA philosophy. The paper explores the implications of alternative methods of weight restriction using simulated data from a well known production process.     

Foresight analysis of tilapia supply chains (Sistema Producto) in four states in Mexico: Scenarios and strategies for 2018

In order to increase competitiveness among individual producers and to encourage their integration, the federal government of Mexico has pursued the establishment, at both the state and national levels, of supply chains, or Sistema Producto (SP). For fisheries and aquaculture, 33 SPs exist at the state level and 7 at the national level, 1 of them for tilapia. The objective of this study is to carry out a foresight analysis (FA) of 4 tilapia SPs in Mexico—for the States of Colima, Sonora, Tabasco and Yucatán—analyzing their 2018 vision for the development of the tilapia chain. FA provides an integral vision of both the internal and external environments, identifying the key factors for each SP and helping to develop the best strategies in order to compete successfully. Sonora, Tabasco and Yucatán identify marketing aspects as key elements for their 10-year future: adding value to the product by means of new processing technologies, developing their own brand, and identifying and differentiating their product based on sanitary issues. Colima focuses its industry future success on efficiency improvements. Finally, the main technological disruptions identified by the 4 states are the incremental use of information technology, product and process certification, new hatcheries with genetically-improved fry, and new processing methodologies developed and/or applied.