The Death Penalty and Historical Change in Spain

This article studies the long duration of the death penalty in Spain until its abolition in the Constitution of 1978. After analysing the plurality of theoretical approaches and possibilities offered by archival sources and specialised historiography (particularly those produced by specialists in the history of law and social history), I synthesise the Spanish answers to the major questions posed in the international historiographical debate on this issue. I then review the formality and the religious and juridical content of these “ceremonies of torment” in order to understand the scope of the practice of the death penalty in processes of social change: what did political power transmit from the scaffolds and what type of social impact was generated by public executions? How did the institutions that exercised the death penalty evolve? Why, during the transition from the Old Regime to the liberal state, was the death penalty used more regularly than before? How many prisoners were in fact executed, for what crimes, and using what procedures and techniques? Finally, after confirming that in the decades straddling the nineteenth and twentieth centuries the death penalty was on the decline, being counteracted by abolitionist discourse in the field of penal sentencing, I examine the functions played by political executions in the repressive dynamics of the Civil War and Francoism.     

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real‐world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities.     

Polarization: Concepts,Measurement, Estimation

We develop the measurement theory of polarization for the case in which income distributions can be described using density functions. The main theorem uniquely characterizes a class of polarization measures that fits into what we call the “identity‐alienation” framework, and simultanously satisfies a set of axioms. Second, we provide sample estimators of population polarization indices that can be used to compare polarization across time or entities. Distribution‐free statistical inference results are also used in order to ensure that the orderings of polarization across entities are not simply due to sampling noise. An illustration of the use of these tools using data from 21 countries shows that polarization and inequality orderings can often differ in practice.     

Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

This paper proposes a new nested algorithm (NPL) for the estimation of a class of discrete Markov decision models and studies its statistical and computational properties. Our method is based on a representation of the solution of the dynamic programming problem in the space of conditional choice probabilities. When the NPL algorithm is initialized with consistent nonparametric estimates of conditional choice probabilities, successive iterations return a sequence of estimators of the structural parameters which we call K–stage policy iteration estimators. We show that the sequence includes as extreme cases a Hotz–Miller estimator (for K=1) and Rust's nested fixed point estimator (in the limit when K→∞). Furthermore, the asymptotic distribution of all the estimators in the sequence is the same and equal to that of the maximum likelihood estimator. We illustrate the performance of our method with several examples based on Rust's bus replacement model. Monte Carlo experiments reveal a trade–off between finite sample precision and computational cost in the sequence of policy iteration estimators.     

Methods to Distinguish Between Polynomial and Exponential Tails

Two methods to distinguish between polynomial and exponential tails are introduced. The methods are based on the properties of the residual coefficient of variation for the exponential and non‐exponential distributions. A graphical method, called a CV‐plot, shows departures from exponentiality in the tails. The plot is applied to the daily log‐returns of exchange rates of US dollar and Japanese yen. New statistics are introduced for testing the exponentiality of tails using multiple thresholds. They give better control of the significance level than previous tests. The powers of the new tests are compared with those of some others for various sample sizes.     

Small area estimation of mean price of habitation transaction using time-series and cross-sectional area-level models

In this paper, a new small domain estimator for area-level data is proposed. The proposed estimator is driven by a real problem of estimating the mean price of habitation transaction at a regional level in a European country, using data collected from a longitudinal survey conducted by a national statistical office. At the desired level of inference, it is not possible to provide accurate direct estimates because the sample sizes in these domains are very small. An area-level model with a heterogeneous covariance structure of random effects assists the proposed combined estimator. This model is an extension of a model due to Fay and Herriot [5], but it integrates information across domains and over several periods of time. In addition, a modified method of estimation of variance components for time-series and cross-sectional area-level models is proposed by including the design weights. A Monte Carlo simulation, based on real data, is conducted to investigate the performance of the proposed estimators in comparison with other estimators frequently used in small area estimation problems. In particular, we compare the performance of these estimators with the estimator based on the Rao–Yu model [23]. The simulation study also accesses the performance of the modified variance component estimators in comparison with the traditional ANOVA method. Simulation results show that the estimators proposed perform better than the other estimators in terms of both precision and bias.     

A grounded theory research approach to building and testing TQM theory in operations management

Total quality management (TQM) has continued to develop as a strategic business improvement approach in organisations and within the Operations Management literature. Strategic TQM is a dynamic phenomenon, reflecting the complexity and technology development in the business environment. Therefore, this conception of TQM has led to significant challenges with regards to developing suitable models and research methodology where traditional, and normative research data, includes survey responses associated with deductive theory and testing. Such data, and its use, is often premised on cause and effect rationality and fails to supply deep rich data to address meanings, phenomena and complex socio-political events, which is a feature of strategic TQM.     

Profiles identification on hierarchical tree structure data sets

In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis–Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.     

The Effect of Infancy Childcare Center Use and Maternal Employment on Toddlers’ Language Development in South Korea

Using first to fourth wave data of the Panel Study of Korean Children (PSKC), this study explores the effects of childcare center use, maternal employment, and other child and familial characteristics on the language development of toddlers in Korea. Among the 2078 families with children in the PSKC, those who completed the Receptive and Expressive Vocabulary Test at the fourth wave were selected, and a small number of disabled or prematurely born children were excluded. In addition, to clarify the effects of maternal employment, families with mothers who were either employed or unemployed for three consecutive years during the child's infancy were selected. Regression analysis showed that neither childcare center use nor maternal employment during infancy had significant effects on toddlers’ expressive and receptive vocabulary at the fourth wave. Family characteristics, such as household poverty and maternal education level, as well as child characteristics, such as gender and birth order, had significant effects on expressed vocabulary. However, only children's gender significantly affected the receptive vocabulary level of toddlers.