Unemployment and labour force participation in Spain

This article examines the relationship between the unemployment rate and the labour force participation rate in Spain. Cointegration analysis is performed for aggregate, male and female time-series. Results suggest that there is no a long-run relationship between the two variables for the aggregate and male cases. However, the findings support a long-run relationship between the two variables for the female time-series. Thus, the unemployment invariance hypothesis is supported in the two former cases but not in the latter.     

The poor outside the lamplight: on the prevalence of poverty among population groups not included in household surveys

Poverty rate calculations are often based on household surveys that exclude some of the groups most vulnerable to poverty such as the homeless. This paper documents the nature of the excluded groups and the quantitative importance of their omission for the Czech Republic. Our study combines European Union Statistics on Income and Living Conditions data for 2011 with information from the 2011 Population Census. Our preferred estimate indicates an increase in the poverty rate by almost one percentage point. The paper contributes to accurately identifying the most vulnerable members of the society, which is essential for setting effective social policies.     

An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components

Portuguese Economic Journal - A set of RLS-type models with ARMA and ARFIMA dynamics is estimated and compared in a forecasting exercise with ARFIMA, GARCH and FIGARCH models. It is an extension of...     

Content analysis research method with Nvivo-6 software in a PhD thesis: an approach to the long-term psychological effects on Chilean ex-prisoners survivors of experiences of torture and imprisonment

The aim of this paper is to review the usefulness of the software CAQDAS QSR—Nvivo-6 in a PhD. Thesis, which consisted of finding the long-term psychological effects of experiences of imprisonment and torture for political reasons in their Chilean survivors. For this research, it was created and administered an in-depth interview to 60 survivors, from which outcome was 9900 paragraphs, coded into 784 categories. The software QSR Nvivo-6 threw a report of frequencies, which were compared quantitatively using Pearson’s χ ² square test and cluster analysis. We conclude that the software facilitates the development of qualitative thematic content analysis, simplifying the coding, analysis and display of data. In Addition, it is very useful when working with large quantities of information, promoting the reliability and validity of studies. However, other aspects are also required to ensure the rigor of a qualitative study.     

An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective

This article presents an application of a bibliometric and visual study of the research carried out on a social science subfield, concretely the consumer behaviour research (CBR), from a longitudinal perspective (period 1966–2008). The study combines performance analysis and science mapping for detecting and visualizing conceptual subdomains. Quantitative and qualitative measures are used in order to identify the most prominent themes. Quantitative data are used to put together very related concepts (themes or clusters of topics), while qualitative indicators (as those based on citations) are used to measure the quality and/or impact of the detected themes. The study also uses bibliometric maps to show in a visual way the associations between the main concepts treated by the CBR community. The maps provide insight into the structure of the CBR, visualize the division of the field into several subfields, and indicate the relationships between these subfields. Co-word analysis is the bibliometric technique used to identify the main themes. All this allows us to quantify and visualize the thematic evolution of the CBR. It also helps to both experts and novices to understand the current state of the art of the CBR and to predict where future research could lead.     

Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products

Energy supply and demand, and as a consequence energy prices, are likely to represent one of the biggest challenges of the 21st century. Commodity markets exhibit increased volatility when there is little or no underutilized supply capability to meet natural fluctuations in demand. In the case of energy markets, the large capital requirements and significant lead times associated with energy production and delivery make them more susceptible to the imbalances in supply capability and demand. Energy price volatility has destructive impact on market agents, and this impact is intensified when the prices exhibit asymmetric volatility. This article pursues two aspects of the issue. First we consider general aspects, especially the asymmetric pattern of volatility of daily returns of different types of energy products. Then, we analyze the behaviour of daily returns by using traditional models of volatility that include AGARCH, TGARCH, EGARCH, and ARSV strategies, as well as a threshold asymmetric autoregressive stochastic volatility (TA-ARSV) model that we propose. The energy products considered in this analysis are probably the most relevant energy products for the economic activity of the nations and the economic relations between countries: Crude Oil (OPEC reference basket and London Brent index), Gasoline, Natural Gas, Butane, and Propane. We use spot prices and the time reference ranges from 1986–1993 to 2009 depending on the product.     

Wage and hour violations in urban labour markets: a comparison of Los Angeles,New York and Chicago

This article compares violations of minimum wage laws and other labour standards in New York City, Los Angeles and Chicago. Los Angeles has the highest violation rates, due to such factors as its industrial composition and disproportionately large number of small establishments, as well as its vast unauthorised immigrant population. In addition, Los Angeles’ higher rates reflect the stricter legal standards in California. We conclude that, although stronger workplace laws and regulations are crucial, in the absence of effective enforcement, they may fail to prevent workplace violations.     

Heterogeneity in bank pricing policies: The Czech evidence

We estimate the pass-through from market interest rates to bank interest rates using heterogeneous panel cointegration techniques to address heterogeneity at the bank level in the Czech Republic. The results indicate heterogeneity in bank pricing in the short, but not in the long term. Mortgage rates and firm rates typically adjust to money market changes, but often less than fully in the long run. Large corporate loans have a smaller mark-up than small loans. Consumer rates have a high mark-up and do not exhibit a cointegration relationship with money market rates even in the long run. Next, we examine how bank characteristics determine the nature of interest rate pass-through in a cross-section of Czech banks. We find evidence for relationship lending, as banks with a stable pool of deposits smooth interest rates and require a higher spread as compensation. Large banks are not found to price their products less competitively. Greater credit risk increases vulnerability to money market shocks.     

Using templates and linguistic patterns to define process performance indicators

Process performance management (PPM) aims at measuring, monitoring and analysing the performance of business processes (BPs), in order to check the achievement of strategic and operational goals and to support decision-making for their optimisation. PPM is based on process performance indicators (PPIs), so having an appropriate definition of them is crucial. One of the main problems of PPIs definition is to express them in an unambiguous, complete, understandable, traceable and verifiable manner. In practice, PPIs are defined informally – usually in ad hoc, natural language, with its well-known problems – or they are defined from an implementation perspective, hardly understandable to non-technical people. In order to solve this problem, in this article we propose a novel approach to improve the definition of PPIs using templates and linguistic patterns. This approach promotes reuse, reduces both ambiguities and missing information, is understandable to all stakeholders and maintains traceability with the process model. Furthermore, it enables the automated processing of PPI definitions by its straightforward translation into the PPINOT metamodel, allowing the gathering of the required information for their computation as well as the analysis of the relationships between them and with BP elements.     

Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot be implemented uncritically for spatial data. Generally, most of the studies in spatial data analysis can be divided into two branches: the model-driven approach and the data-driven approach. The main aim of this paper is the comparison of both approaches. To carry out such a task, crime rate data in Columbus (Ohio), coming from a well-known database, have been used. The main aim of this paper is to illustrate how spatial effects can be viewed as spatial econometric models, which assess the limitations of standard techniques in a spatial context, suggesting alternative methods to deal with this problem. An application to the crime rate in Columbus (Ohio) has been carried out.