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.     

Generalized correlation and kernel causality with applications in development economics

New generalized correlation measures of 2012, GMC(Y|X), use Kernel regressions to overcome the linearity of Pearson's correlation coefficients. A new matrix of generalized correlation coefficients is such that when |r*_ij| > |r*_ji|, it is more likely that the column variable X_j is what Granger called the “instantaneous cause” or what we call “kernel cause” of the row variable X_i. New partial correlations ameliorate confounding. Various examples and simulations support robustness of new causality. We include bootstrap inference, robustness checks based on the dependence between regressor and error, and on the out-of-sample forecasts. Data for 198 countries on nine development variables support growth policy over redistribution and Deaton's criticism of foreign aid. Potential applications include Big Data, since our R code is available in the online supplementary material.     

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.     

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.     

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering an appropriate transformation on the Lindley distribution, we propose the unit-Lindley distribution and investigate some of its statistical properties. An important fact associated with this new distribution is that it is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify their influences on the average of the response variable. Finally, a practical application is presented to show that our model fits much better than the Beta regression.     

A three-state continuous time Markov chain model for HIV disease burden

Plasma HIV viral load (VL) is the clinical indicator used to evaluate disease burden for HIV-infected patients. We developed a covariate-adjusted, three-state, homogenous continuous time Markov chain model for HIV/AIDS disease burden among subgroups. We defined Detectable and Undetectable HIV VL levels as two transient states and Death as the third absorbing state. We implemented the exact maximum likelihood method to estimate the parameters with related asymptotic distribution to conduct hypothesis testing. We evaluated the proposed model using HIV-infected individuals from South Carolina (SC) HIV surveillance data. Using the developed model, we estimated and compared the transition hazards, transition probabilities, and the state-specific duration for HIV-infected individuals. We examined gender, race/ethnicity, age, CD4 count, place of residence, and antiretroviral treatment regimen prescribed at the beginning of the study period. We found that patients with a higher CD4 count, increased age, heterosexual orientation, white, and single tablet regimen users were associated with reduced risk of transitioning to a Detectable VL from an Undetectable VL, whereas shorter time since diagnosis, being male, and injection drug use increased the risk of the same transition.     

A modified Conway–Maxwell–Poisson type binomial distribution and its applications

This article proposes a generalized binomial distribution, which is derived from the finite capacity queueing system with state-dependent service and arrival rates. This distribution is also generated from the conditional Conway–Maxwell–Poisson (CMP) distribution given a sum of two CMP variables. In this article, we consider the properties of the probability mass function, indices of dispersion, skewness and kurtosis, and give applications of the proposed distribution. The estimation method and simulation study are also considered.