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Global Mindset (GM) is a multifaceted construct that has received broad interest among practitioners and academics. It is a fragmented construct at this point in time, due to definitional overlap with other constructs such as global leadership and cultural intelligence. This overlap has created complexity for research that attempts to understand GM in isolation. Lack of clear boundaries in defining and conceptualizing this construct challenges researchers who are attempting to capture fully what constitutes GM. Our work seeks to better understand and explain what underlines the individual GM construct and how does this impact the development of global competencies in individual managers.

We systematically review and analyze the individual GM literature thematically to provide an overview of the extant research from a broad array of scholarly sources dating from 1994 to 2017. Our work offers a thematic analysis that provides a visual guide to GM by tracking the corpus of individual-level GM studies. We categorize the research according to its theoretical groundings and basic concepts and proceed review how GM has been operationalized at the individual level and measured. Next, we integrate major dimensions in the GM research and propose a framework to enhance understanding of the phenomenon. Finally, we discuss the implications of our review for the development of GM for practitioners, coaches and trainers.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

Purpose – The chapter examines the international welfare effects of biotech crop adoption, based on a transversal literature review and a case study of the introduction of genetically modified (GM) food crops in Bangladesh, India, Indonesia, and the Philippines.

Methodology/approach – The analysis is based on (a) a review of lessons from the applied economic literature and (b) simulations using an improved multimarket, multicountry, computable general equilibrium (CGE) model, calibrated with productivity hypotheses formulated with local scientists in the four Asian countries.

Findings – Results from the analysis show that, in the absence of trade-related regulations, GM crop adoption generates economic gains for adopting countries and importing non-adopters, that domestic regulations at adopters and especially non-adopters can reduce these gains, and that import regulations in other countries can also affect gains for exporting adopters. The case study illustrates these conclusions, but it also shows that net importers will mostly benefit from adoption in their terms of trade, and that segregation of non-GM crops for export markets can be beneficial if it is not too costly.

Research limitations/implications – The use of a CGE model allows for accounting for cross-sectoral effects, and for regulations affecting bilateral trade flows, but it also has a number of limitations. The model used here, like the ones used in the other papers in the literature, is static, based on an aggregated representation of the global economy (GTAP database), and assumes perfect competition. This means that the absolute results of each scenario may not perfectly represent the actual welfare effects engendered by the adoption of biotech crops. Still, what matters here is the comparison of the relative welfare effects across countries and scenarios. The simulations are also done ex-ante, so, even if the model here was calibrated with country-based data, the results do depend on hypothetical assumptions about the performance of the selected technologies.

Originality/value of the paper – The chapter aims to illustrate the welfare effects generated by GM crops for adopters, non-adopters, in a segmented and regulated international market. Unlike other papers, the review section provides key transversal lessons from the literature, accounting for results from both partial equilibrium and CGE model studies. The empirical application focuses on four populous Asian countries that have been largely left out of the literature. The model used in the simulation presents a number of improvement from the CGE literature on GM crops, including partial adoption, factor-biased productivity shock in each adopting country, GM labeling regulations modeled as trade filters, and the inclusion of costly non-GM segregation as observed in the international market.

Our paper discusses simulation-based Bayesian inference using information from previous draws to build the proposals. The aim is to produce samplers that are easy to implement, that explore the target distribution effectively, and that are computationally efficient and mix well.

Purpose – The chapter provides a comprehensive review of trade-related regulations of genetically modified (GM) food, identifies their main effects, and analyzes the main motivations behind their support.

Methodology/approach – The analysis is substantiated by (a) results from the literature on GM food regulations and (b) comparative statics results from a simplified three-country partial equilibrium welfare and political economic model.

Findings – The analysis shows that in a non-GM producing country, trade-related regulations will benefit producers, but not necessarily consumers. Producers' support is found to be instrumental to push for a ban, for information requirements on shipments, or for mandatory labeling of GM food products. Outside pressure groups will play the role of swing voters in cases where consumers and producers do not agree.

Research limitations/implications – The analytical model is based on simplifying assumptions on the groups and market effects of each regulation. Future research is needed to empirically validate some of the main results.

Originality/value of the chapter – The goal of the chapter is to inform economic and policy researchers on the effects of GM food trade-related regulations. The chapter provides an updated comprehensive overview of the key trade regulations of GM food. It uses a unique model to derive the main welfare effects of GM food regulations. By comparing the effects of GM food regulations in different types of countries for different pressure groups, the findings provide new insights in this area.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

We analyze the sizes of standard cointegration tests applied to data subject to linear interpolation, discovering evidence of substantial size distortions induced by the interpolation. We propose modifications to these tests to effectively eliminate size distortions from such tests conducted on data interpolated from end-of-period sampled low-frequency series. Our results generally do not support linear interpolation when alternatives such as aggregation or mixed-frequency-modified tests are possible.