All Politics is Not Local: National Forces in State Abortion Initiatives

Objective. Although research suggests that national forces can play a role in local and state elections, most of this work has only recently begun to examine the potential role of national forces in state or local ballot initiative or referenda elections. Methods. Our research addresses this gap in the literature by exploring the influence of national forces, such as the timing of elections, Supreme Court rulings, the activities of interest groups, and public opinion, on state direct legislation elections. We incorporate national forces into the morality politics framework and derive specific hypotheses. We then test these hypotheses by conducting a multivariate analysis of county–level voting patterns across 16 abortion–related direct legislation elections. Results. Our results confirm most of the hypotheses derived from the morality politics framework, including those concerning the role of national forces. Conclusions. Voting patterns on abortion tend to be influenced by the presence of presidential elections, Supreme Court rulings, interest–group activity, public opinion, partisanship, college education, and conservative religious forces. We discuss the implications of our findings for research on elections, abortion policy, and morality politics.     

The Effect of Single Rater Bias in Multi‐Stakeholder Research: A Methodological Evaluation of Buyer‐Supplier Relationships

As the global competitive landscape intensifies, firms have looked to their supply chain organizations to improve cost, visibility, and cycle time performance across functions, products, and markets. As a result, the scope of supply chain related operations have increasingly cut across organizational boundaries. To understand and capture such cross‐organizational activities, researchers have broadened the focus of their studies and included multiple stakeholders in their analysis (e.g., integration, sustainability, and buyer‐supplier relationships). However, multi‐stakeholder research has also increased the complexity and effort required to conduct studies across organizational boundaries. Unfortunately, many studies that use multi‐stakeholder constructs fail to fully address their multi‐sided nature during both construct conceptualization and data collection. Several studies suggest that neglecting the multi‐sided nature of certain constructs can affect the research validity and reliability and may invalidate research inferences and results, although such concerns have not been empirically demonstrated. The current study addresses this gap by performing a series of tests using data from 105 matched pairs of buyers and their suppliers to illustrate key methodological considerations for conducting multi‐stakeholder research. This study also offers practical guidance regarding assumptions routinely made in single rater research and proposes when single rater data may be appropriate for multi‐stakeholder research.     

Bayesian spectral analysis models for quantile regression with Dirichlet process mixtures

This paper presents a Bayesian analysis of partially linear additive models for quantile regression. We develop a semiparametric Bayesian approach to quantile regression models using a spectral representation of the nonparametric regression functions and the Dirichlet process (DP) mixture for error distribution. We also consider Bayesian variable selection procedures for both parametric and nonparametric components in a partially linear additive model structure based on the Bayesian shrinkage priors via a stochastic search algorithm. Based on the proposed Bayesian semiparametric additive quantile regression model referred to as BSAQ, the Bayesian inference is considered for estimation and model selection. For the posterior computation, we design a simple and efficient Gibbs sampler based on a location-scale mixture of exponential and normal distributions for an asymmetric Laplace distribution, which facilitates the commonly used collapsed Gibbs sampling algorithms for the DP mixture models. Additionally, we discuss the asymptotic property of the sempiparametric quantile regression model in terms of consistency of posterior distribution. Simulation studies and real data application examples illustrate the proposed method and compare it with Bayesian quantile regression methods in the literature.     

Taxonomy of ERP integrations and performance outcomes: an exploratory study of manufacturing firms

Building and deploying network capabilities of firms are crucial for sustaining competitive advantage. Enterprise resource planning (ERP) systems are useful to enhance such network capabilities thorough effective information flows. Their intended goals are measured in terms of production costs, operational flexibility and supply chain performance outcomes. However, the impact of ERP system implementation on firm performance has been reported as somewhat inconclusive. This study contends that a missing link in the story is the scope and extent of ERP system implementation after investigating how the extent of ERP integration is associated with the performance outcomes of manufacturing firms. The study also posits that restructuring in the organisation and supply chains are positively associated with manufacturing performance. Since ERP often entails restructuring in an organisation and supply chains, it is anticipated that restructuring plays an important role in inducing positive impact of ERP implementation to the firm. Using a global sample of 641 manufacturers, this research identifies four distinct ERP systems integration patterns, epitomised by different extents and directions of integration, and finds a significant association among the broadest degree of ERP systems integration, restructuring and plant performance improvement. The empirical results also show that restructuring takes place most actively in a firm that implement ERP with widest scope and scale.     

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy-making in Cali,Colombia

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the non-constant pattern of a log baseline rate is modeled with a non-parametric step function, the resulting semi-parametric model involves a model component of varying dimensions and thus requires a sophisticated varying-dimensional inference to obtain the correct estimates of model parameters of a fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art Markov chain Monte Carlo-type algorithm based on partial collapse. The proposed model and methods are used to investigate the association between the daily homicide rates in Cali, Colombia, and the policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.     

Analysis of Poisson varying-coefficient models with autoregression

In the regression analysis of time series of event counts, it is of interest to account for serial dependence that is likely to be present among such data as well as a nonlinear interaction between the expected event counts and predictors as a function of some underlying variables. We thus develop a Poisson autoregressive varying-coefficient model, which introduces autocorrelation through a latent process and allows regression coefficients to nonparametrically vary as a function of the underlying variables. The nonparametric functions for varying regression coefficients are estimated with data-driven basis selection, thereby avoiding overfitting and adapting to curvature variation. An efficient posterior sampling scheme is devised to analyse the proposed model. The proposed methodology is illustrated using simulated data and daily homicide data in Cali, Colombia.     

Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection

In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates the sample selection scheme into a stochastic monotone trend constraint, variable selection, and robustness against departures from the normality assumption. We present the basic theoretical properties of the proposed model that include its stochastic representation, sample selection bias quantification, and hierarchical model specification to deal with the stochastic monotone trend constraint in the nonparametric component, simple bias corrected estimation, and variable selection for the linear components. We then develop computationally feasible Markov chain Monte Carlo methods for semiparametric Bernstein polynomial functions with stochastically constrained parameter estimation and variable selection procedures. We demonstrate the finite-sample performance of the proposed model compared to existing methods using simulation studies and illustrate its use based on two real data applications.     

Living Arrangement and Life Satisfaction of the Elderly in South Korea

With a rapid aging population in South Korea, the elderly living alone has received particular attention from researchers and stakeholders. Although previous research has found that living alone negatively affects life satisfaction of the elderly, much remains unclear about how life satisfaction of the elderly changes over time as their living arrangement change. To advance knowledge on life satisfaction of the elderly, using the Korean Longitudinal Study of Aging we tested the association between life satisfaction (overall life, health, and financial) of the elderly and their living arrangement. Our longitudinal research found that the elderly living with family had significantly higher life satisfaction compared to those living alone between 2006 and 2016. In addition, individuals who had a superior economic profile and maintained physical activity and social relations showed higher life satisfaction than their counterparts. Our findings suggest that to improve the life satisfaction of the elderly living alone, both material and psychological support programs are needed. Future research is needed to account for the multi-faceted nature of life satisfaction.