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.     

Ensuring Parliamentary Gender Equality Through a New Zipper Method: An Application to Finland

Gender equality constitutes a crucial objective for a successful societal development. Although research has found that the differences are shrinking, parliamentary composition is not gender balanced in most areas of the world. Henceforth, recent literature has undertaken important efforts aimed at developing different initiatives to promote parity in democratic parliaments, especially through the use of quota systems. Prior initiatives, although leading to an improvement of gender parity, do not manage to ensure an optimal gender-egalitarian parliamentary composition. Thus, this paper presents a method to organize closed and blocked lists of candidates that guarantees the achievement of gender-balanced representation in parliamentary elections. Specifically, parity is sought globally, in each party and also in each electoral constituency. Furthermore, the method is applied to the elections held over the last two decades in Finland. Results reveal that parity in gender representation increases at global, party, and constituency levels throughout all the parliamentary elections in Finland, even approaching optimal numerical parity (i.e. 50 % for each gender) in most of the cases.     

Materialism in Pedagogy Students in Chile

Many studies have used Richins and Dawson’s (J Consum Res 19: 303–316, 1992) Material Values Scale (MVS), applying it to different types of populations that exhibit a particular psychometric behavior, and showing little stability in their factorial structure. In the present study, 1,070 pedagogy students from the northern, central and southern regions of Chile answered the MVS. This sample was randomly divided in two. Using the first sub-sample (N = 539), an exploratory factorial analysis was carried out, from which a structure of nine items was grouped into two factors called “Social Success” and “Personal Happiness”, which presented adequate reliability. Later, with the second sub-sample (N = 531), the factorial structure indicated above was put to the test through a confirmatory factorial analysis. The data from the model show that the scale contains 8 items in total, grouped into two dimensions. The factorial loads are significant at the level of 1 %, which indicates that the 2-factor structure can be confirmed. Finally—using the proposed structure—the presence of the students’ material values was evaluated.     

On the asymptotic distribution of Cook's distance in logistic regression models

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.     

Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams

In the past few years, the field of dam safety has approached risk informed methodologies throughout the world and several methodologies and programs are appearing to aid in the systematization of the calculations. The most common way of implementing these calculations is through the use of event trees, computing event probabilities, and incremental consequences. This methodology is flexible enough for several situations, but its generalization to the case of systems of several dams is complex and its implementation in a completely general calculation methodology presents some problems. Retaining the event tree framework, a new methodology is proposed to calculate incremental risks. The main advantage of this proposed methodology is the ease with which it can be applied to systems of several dams: with a single risk model that describes the complete system and with a single calculation the incremental risks of the system can be obtained, being able to allocate the risk of each dam and of each failure mode. The article shows how both methodologies are equivalent and also applies them to a case study.     

International job search: Mexicans in and out of the US

It is argued that migration from Mexico to the US and return migration are determined by international wage differentials and preferences for origin. We use a model of job search, savings and migration to show that job turnover is a crucial determinant of the migration process. We estimate this model by Simulated Method of Moments (SMM) and find that migration practically disappears, it goes down from 19 to 0.5%, had Mexico US arrival rates while employed. A lower decrease in migration, to 7%, occurs by an increase in Mexican wage offers. On the other hand, doubling migration costs reduces migration rates from 19 to 5%, while subsidizing return migration in $300 reduces migration rates only to 14%.     

Influence analysis in multivariate linear general models

In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance.