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Various authors have brought forth the idea that the increase in context turbulence and the relentless change in today's economic and competitive environments have rendered it essential for an effective firm strategy to combine both value appropriation and value creation (Porter, 1996; Moran & Ghoshal, 1999; Venkataraman & Sarasvathy, 2001; Hitt, Ireland, Camp, & Sexton, 2001b). Nonetheless, the methodological bases and the assumptions that characterize contributions concerning value appropriation and value creation are notably different and in many respects opposite to one another. These profound methodological differences hinder the possibility of a combined consideration of value appropriation and value creation issues within a coherent interpretative framework. By reinterpreting more conventional strategy studies in the light of the Austrian process view, this article builds a process framework which is able to consider and render mutually compatible both value appropriation and value creation within the unitary process of firm development. In addition, the use of the Austrian approach as an interpretative lens enables an evolution and extension of the resource-based theory that consents it, not only to grasp the mechanisms behind value appropriation, but also to suggest new ways of viewing post-industrial firm behavior that help to interpret its dynamic and proactive role in the value creation process.

Over the past decade, biofuels production in the European Union and the United States has boomed – much of this due to government mandates and subsidies. The United States has now surpassed Brazil as the world's leading producer of ethanol. The economic and environmental impact of these biofuel programs has become an important question of public policy. Due to the complex intersectoral linkages between biofuels and crops, livestock as well as energy activities, CGE modeling has become an important tool for their analysis. This chapter reviews recent developments in this area of economic analysis and suggests directions for future research.

South Asia is one of the world's poorest regions. In this chapter we use a representative household model of South Asia to explore the potential impacts of regional trade liberalization under the auspices of SAFTA on both the distribution of economic gains across the countries of South Asia, and across various groups within South Asia. We also discuss the underlying theory of a potential extension to our approach.

This paper deals with estimation of risk and the risk preference function when producers face uncertainties in production (usually labeled as production risk) and output price. These uncertainties are modeled in the context of production theory where the objective of the producers is to maximize expected utility of normalized anticipated profit. Models are proposed to estimate risk preference of individual producers under (i) only production risk, (ii) only price risk, (iii) both production and price risks, (iv) production risk with technical inefficiency, (v) price risk with technical inefficiency, and (vi) both production and price risks with technical inefficiency. We discuss estimation of the production function, the output risk function, and the risk preference functions in some of these cases. Norwegian salmon farming data is used for an empirical application of some of the proposed models. We find that salmon farmers are, in general, risk averse. Labor is found to be risk decreasing while capital and feed are found to be risk increasing.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

This chapter examines changes in US monetary policy uncertainty (ΔMPU) and fiscal policy uncertainty (ΔFPU) on stock returns while controlling for downside risk, lagged dividend yield, and time series patterns. Testing G7 markets consistently shows that both ΔMPU and ΔFPU have significant negative impacts on stock returns. Evidence shows that any downside risk, ΔMPU or ΔFPU in US market will soon be transmitted to G6 industrial markets and the impacts are extended to two months. These risk and uncertainty premiums should be priced in the stocks of the major industrial markets.