The Transnational Organization of Production and Environmental Degradation: A Cross‐National Study of the Effects of Foreign Capital Penetration on Water Pollution Intensity, 1980–1995*

Objective. This study attempts to increase our understanding of the environmental impacts of the transnational organization of production in the context of foreign capital penetration. Methods. With a sample of 53 countries, panel analyses using OLS and robust regression are conducted to assess the effects of foreign direct investment in the manufacturing sector on growth in organic water pollution intensity, 1980–1995. Slope‐dummy interaction variables are used to compare the effects in developed countries and less‐developed countries. The tested models include a variety of relevant controls, such as domestic investment, state environmentalism, size of the manufacturing sector, and level of economic development. Results. Findings for the analyses indicate that foreign direct investment in manufacturing positively affects growth in organic water pollution intensity in less‐developed countries, while the effect in developed countries is nonsignificant. Conclusions. This research illustrates the need for social scientists to investigate the environmental impacts of both the transnational organization of production and the overall scale of production in different sectors.     

On the predictive efficiency of the core solution in side-payment games

This paper reports the first cross-study competitive test of thecore solution in side-payment games where the core is nonempty and nonunique (i.e., larger than a single point). The core was tested against five alternative theories including the Shapley value, the disruption nucleolus, the nucleolus, the 2-center, and the equality solution. A generalized Euclidean distance metric which indexes the average distance between an observed payoff vector and the entire set of predicted payoff vectors (Bonacich, 1979) was used as the measure of goodness-of-fit. Analysis of data assembled from six previously reported studies (encompassing a total of 1,464 observations over 56 3-person and 4-person side-payment games) showed the core to predict less accurately than the Shapley value, disruption nucleolus, and nucleolus solutions (p < 0.01). These findings are consistent with previous empirical results that show the core to have a low level of predictive accuracy in side-payment games.This research was supported by grants SOC-7726932 and SES-8015528 from the National Science Foundation. Data analysis was performed at the Madison Academic Computing Center.     

Union organizing effort and success in the U.S., 1948–2004

While union density in the public sector has increased in recent decades, private sector union density in the U.S. has declined steadily since the mid-1950s. Scholars have evoked a variety of explanations to account for the decline in union membership, but substantially less attention has been devoted to understanding the contribution of the union organizing process as governed by the National Labor Relations Board (NLRB). In this paper, we focus on two aspects of this process: union organizing effort (the effort to establish union representation elections to organize non-unionized workers) and union organizing success (success in winning union representation elections). Using annual time series data for the U.S. from 1948 to 2004, we show that there has been a long-term decline in both union organizing effort and union organizing success, which likely contributes to declining union density. We focus on three explanations for these changes: the political–legal environment for unions, deindustrialization and globalization, and employer opposition to unionization efforts. We find that each of these factors contributes to organizing effort and success and conclude with a discussion of the implications of this research for future mobilization efforts.     

A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.     

Modeling time series data with semi-reflective boundaries

Time series data are increasingly common in many areas of the health sciences, and in some instances, may have natural boundaries serving as performance guidelines or as thresholds associated with adverse outcomes. Such boundaries may be labeled as semi-reflective, in that the time series values have an increased chance of returning towards middle levels as the boundaries are approached, but boundaries can still be breached. In this paper we review a model that was previously proposed for such data and we investigate its statistical properties. Specifically, this model consists of a third-order auto-regressive projection component, parameterized as a constrained linear combination of linear, flat, and quadratic trends, and an error term that uses a logistic regression model for its sign. We describe and compare a previously-proposed estimation method with a modified version thereof, using computer simulations, as well as data examples from heart monitoring and from a driving simulator. We find that the two methods tend to give different results, with the modified technique having lower bias and more accurate confidence intervals than the previously-proposed method.     

Discreteness Causes Bias in Percentage-Based Comparisons: A Case Study From Educational Testing

Discretizing continuous distributions can lead to bias in parameter estimates. We present a case study from educational testing that illustrates dramatic consequences of discreteness when discretizing partitions differ across distributions. The percentage of test takers who score above a certain cutoff score (percent above cutoff, or “PAC”) often describes overall performance on a test. Year-over-year changes in PAC, or ΔPAC, have gained prominence under recent U.S. education policies, with public schools facing sanctions if they fail to meet PAC targets. In this article, we describe how test score distributions act as continuous distributions that are discretized inconsistently over time. We show that this can propagate considerable bias to PAC trends, where positive ΔPACs appear negative, and vice versa, for a substantial number of actual tests. A simple model shows that this bias applies to any comparison of PAC statistics in which values for one distribution are discretized differently from values for the other.