Liberalization, FDI, and Growth in Developing Countries: A Panel Cointegration Approach

Using a panel cointegration framework, the article explores the two-way link between FDI and growth for a panel of 23 developing countries. In addition, it investigates the impact of liberalization on the dynamics of the FDI and GDP relationship. A long-run cointegrating relationship is found between FDI and GDP after allowing for heterogeneous country effects. The cointegrating vectors reveal a bidirectional causality between GDP and FDI for more open economies. For relatively closed economies, long-run causality appears unidirectional and runs from GDP to FDI, implying that growth and FDI are not mutually reinforcing under restrictive trade and investment regimes.     

Towards Enhancement of the Economy of a Thermal Power Generating System through Prediction of Plant Efficiency

The plant ‘Heat Rate’ (HR) is a measure of overall efficiency of a thermal power generating system. It depends on a large number of factors, some of which are non-measurable, while data relating to others are seldom available and recorded. However, coal quality (expressed in terms of ‘effective heat value’ (EHV) as kcal/kg) transpires to be one of the important factors that influences HR values and data on EHV are available in any thermal power generating system. In the present work, we propose a prediction interval of the HR values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR. The underlying theory borrows the idea of providing simultaneous confidence interval (SCI) to the coefficients of a p-th p(≥1) order autoregressive model (AR(p)). The theory has been substantiated with the help of real life data from a power utility (after suitable base and scale transformation of the data to maintain the confidentiality of the classified document). Scope for formulating strategies to enhance the economy of a thermal power generating system has also been explored.     

Construction of bivariate and multivariate weighted distributions via conditioning

Weighted distributions (univariate and bivariate) have received widespread attention over the last two decades because of their flexibility for analyzing skewed data. In this article, we propose an alternative method to construct a new family of bivariate and multivariate weighted distributions. For illustrative purposes, some examples of the proposed method are presented. Several structural properties of the bivariate weighted distributions including marginal distributions together with distributions of the minimum and maximum, evaluation of the reliability parameter, and verification of total positivity of order two are also presented. In addition, we provide some multivariate extensions of the proposed models. A real-life data set is used to show the applicability of these bivariate weighted distributions.     

Some alternative bivariate Kumaraswamy models

In this article we discuss various strategies for constructing bivariate Kumaraswamy distributions. As alternatives to the Nadarajah et al. (2011) bivariate model, four different models are introduced utilizing a conditional specification approach, a conditional survival function approach, and an Arnold–Ng bivariate beta distribution construction approach. Distributional properties for such bivariate distributions are investigated. Parameter estimation strategies for the models are discussed, as are the consequences of fitting two of the models to a particular data set involving the proportion of foggy days at two different airports in Colombia.     

Path to “Stardom” in Globally Distributed Hybrid Teams: An Examination of a Knowledge‐Centered Perspective using Social Network Analysis

Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups.     

Missing the wealthy in the HFCS: micro problems with macro implications

Wealth aggregates implied by the Household Finance and Consumption Survey (HFCS) usually yield much lower amounts than macroeconomic statistics reported in the National Accounts. An important source of this gap may be the under-representation of the wealthiest households in the HFCS. This article therefore combines a semi-parametric Pareto model estimated from top survey data and observations from rich lists with a non-parametric stratification approach to quantify the impact of the missing wealthy households on component-specific micro-macro gaps. We find that unadjusted micro data substantially underestimates wealth inequality. The largest effects are documented for equity. For other components, the missing wealthy explain less than ten percentage points of the micro-macro gap. We find that differences in oversampling strategies limit the cross-country comparability of unadjusted survey-implied wealth distributions and that our top tail adjustment leads to measures that are internationally better comparable.

     

Acute Exposure to Extremely Hazardous Substances: An Analysis of Environmental Equity

Although environmental equity research has focused primarily on chronic pollution sources, recent advances in environmental modeling and geographic information systems (GIS) provide a foundation for developing measures that can be used to evaluate differential exposure to acute pollution events. This article describes a methodology that uses facility-specific information to develop a risk surface representing the spatial distribution of accidental exposure to hazardous substances in a study area. Environmental pollution models recommended by the U.S. Environmental Protection Agency were used in conjunction with GIS software to achieve this objective. The methodology was implemented in a large metropolitan region (Hillsborough County, Florida) to examine disproportionate exposure to worst-case releases of extremely hazardous substances. The environmental inequity hypothesis was investigated by directly comparing the distribution of potential exposures within each racial (non-White versus White) and income (below poverty versus above poverty) subgroup. The results indicate that a significantly large proportion of both non-White and impoverished individuals resided in areas potentially exposed to multiple accidental releases.     

On intertemporal poverty measures: the role of affluence and want

This paper proposes classes of intertemporal poverty measures which take into account both the debilitating impact of prolonged spells in poverty and the mitigating effect of periods of affluence on subsequent poverty. The weight assigned to the level of poverty in each time period depends on the length of the preceding spell of poverty or of non-poverty. The proposed classes of intertemporal poverty measures are quite general and allow for a range of possible judgements as to the overall impact on a poor period of preceding spells of poverty or affluence. We discuss the properties of the proposed classes of measures and axiomatically characterize these measures.     

Hardness and algorithms for rainbow connection

An edge-colored graph G is rainbow connected if any two vertices are connected by a path whose edges have distinct colors. The rainbow connection of a connected graph G, denoted rc(G), is the smallest number of colors that are needed in order to make G rainbow connected. In the first result of this paper we prove that computing rc(G) is NP-Hard solving an open problem from Caro et al. (Electron. J. Comb. 15, 2008, Paper R57). In fact, we prove that it is already NP-Complete to decide if rc(G)=2, and also that it is NP-Complete to decide whether a given edge-colored (with an unbounded number of colors) graph is rainbow connected. On the positive side, we prove that for every ε>0, a connected graph with minimum degree at least ε n has bounded rainbow connection, where the bound depends only on ε, and a corresponding coloring can be constructed in polynomial time. Additional non-trivial upper bounds, as well as open problems and conjectures are also presented.