Welfare State Development in Latin America and the Caribbean (1970s–2000s): Multidimensional Welfare Index,Its Methodology and Results

There is an ongoing debate between focalization and universalization on welfare policies as the best way to develop the welfare state in Latin America and the Caribbean. However, there is a need to develop a measure that exhibits the multidimensional nature of the welfare state, instead of focusing on the social spending dimension. Segura-Ubiergo (The political economy of the welfare state in Latin America: globalization, democracy and development. Cambridge University Press, New York, 2007) constructed a welfare effort index (WEI) to facilitate understand the relative degrees of welfare state development among Latin American countries. The WEI focuses mainly on social spending and ignores the other dimensions of welfare. Based on a comparative analysis of 17 Latin American countries and following the methodology of Segura-Ubiergo, a new index that aims at enriching the WEI was constructed. The new index is multidimensional in that it has eight indicators relating to three dimensions of welfare: social spending, coverage of welfare programs and outcome of welfare institutions. Principal component analysis was used for reducing the indicators into three indexes that represent three proposed dimensions of welfare. The combination of these three indexes gives the multidimensional welfare index. The results of the index account for more than 75 % of the data variance.     

A Study of the Role Played by Manufacturing Strategic Objectives and Capabilities in Understanding the Relationship between Porter's Generic Strategies and Business Performance

An appropriate alignment between business strategy, manufacturing strategic objectives and manufacturing capabilities reportedly influences business performance positively. However, few papers empirically analyse this proposition for the case of Porter's generic strategies of cost leadership and differentiation, and none jointly considers all four elements. This paper integrates strategies, capabilities and performance in a single model and proposes that both manufacturing competitive priorities and capabilities, articulated in terms of cost and flexibility, are essential for explaining the link between generic business strategies and business commercial and financial performance. Within this analytic framework, we test whether companies that emphasize one business strategy rather than another achieve a better alignment and superior performance. The analyses conducted with a sample of 148 Spanish manufacturers provide general support for these propositions and contribute to a deeper understanding of the role played by functional strategies in understanding the outcomes of business strategy.     

On-line bin packing with restricted repacking

Semi-on-line algorithms for the bin-packing problem allow, in contrast to pure on-line algorithms, the use of certain types of additional operations for each step. Examples include repacking, reordering or lookahead before packing the items. Here we define and analyze a semi-on-line algorithm where for each step at most k items can be repacked, for some positive integer k. We prove that the upper bound for the asymptotic competitive ratio of the algorithm is a decreasing function of k, which tends to 3/2 as k goes to infinity. We also establish lower bounds for this ratio and show that the gap between upper and lower bounds is relatively small.     

A team level investigation of the relationship between Leader–Member Exchange (LMX) differentiation,and commitment and performance

Although the differential treatment of team members by their leader is at the heart of Leader–Member Exchange (LMX) theory, empirical studies exploring the role of within-team LMX differentiation in relation to team outcomes are still relatively scarce. This study among 269 Dutch secondary school teachers from 33 different teams tested the hypotheses that the relationship between LMX differentiation and team commitment, and team performance is moderated by LMX-quality median. Moreover, we hypothesized that team members' perceived dissimilarity regarding work values and orientations would be positively related to within-team LMX differentiation. Teachers completed questionnaires on LMX-quality, dissimilarity, and team commitment, whereas team performance was rated by school principals. Results indeed showed that LMX differentiation is positively related to both outcome variables in teams with a low LMX-quality median only. As expected, more perceived dissimilarity between team members was related to more within-team variability in LMX-scores. These results contribute to knowledge on hypothetical antecedents and consequences of LMX differentiation at the team level.     

Multivariate log-Birnbaum–Saunders regression models

In this paper, we present a multivariate version of the skewed log-Birnbaum–Saunders regression model. This new family of distributions holds good properties such as marginal variables following univariate skewed log-Birnbaum–Saunders distributions, besides presenting the usual log-Birnbaum–Saunders distribution as a particular case. Furthermore, the model parameters are estimated through maximum-likelihood methods, a closed-form expression for the Fisher’s information matrix is presented, and testing hypothesis for model parameters is performed. Two real datasets are analyzed and results are discussed.     

Archival Work as Qualitative Sociology

Qualitative Sociology -     

Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes

The hyper‐Poisson distribution can handle both over‐ and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation‐specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper‐Poisson distribution in analyzing motor vehicle crash count data. The hyper‐Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway‐highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness‐of‐fit measures indicated that the hyper‐Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper‐Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway‐Maxwell‐Poisson model previously developed for the same data set. The advantages of the hyper‐Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper‐Poisson model can handle both over‐ and underdispersed crash data. Although not a major issue for the Conway‐Maxwell‐Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.