Productivity, quality, and export intensities

We study how firm and foreign market characteristics affect the geographic distribution of exporter' sales. To this purpose, we use export intensities (the ratio of exports to sales) across destinations as our key measures of firms'relative involvement in heterogeneous foreign markets. In a representative sample of Italian manufacturing firms, we find a robust negative correlation between revenue-TFP and export intensity to low-income destinations and, more generally, that the correlations between export intensities and TFP are increasing in per capita income of the foreign destinations. We argue that these (and other) empirical regularities can arise from the interplay between (endogenous) cross-firm heterogeneity in product quality and cross-country heterogeneity in quality consumption. To test this conjecture, we propose a new strategy to proxy for product quality that allows to exploit some unique features of our dataset. Our results strongly suggest that firms producing higher-quality products tend to concentrate their sales in the domestic and other high-income markets.

Ethnicity and conflict : an empirical study

This paper examines the impact of ethnic divisions on conflict. The analysis relies on a theoretical model of conflict (Esteban and Ray, 2010) in which equilibrium conflict is shown to be accurately described by a linear function of just three distributional indices of ethnic diversity: the Gini coefficient, the Hirschman-Herfindahl fractionalization index, and a measure of polarization. Based on a dataset constructed by James Fearon and data from Ethnologue on ethno-linguistic groups and the "linguistic distances" between them, we compute the three distribution indices. Our results show that ethnic polarization is a highly significant correlate of conflict. Fractionalization is also significant in some of the statistical exercises, but the Gini coefficient never is. In particular, inter-group distances computed from language and embodied in polarization measures turn out to be extremely important correlates of ethnic conflict.

A possible correlation between the host genetic background in the epidemiology of Hepatitis B virus in the Amazon region of Brazil

The Amazon region of Brazil is an area of great interest because of the large distribution of hepatitis B virus in specific Western areas. Seven urban communities and 24 Indian groups were visited in a total of 4,244 persons. Each individual was interviewed in order to obtain demographic and familial information. Whole blood was collected for serology and genetic determinations. Eleven genetic markers and three HBV markers were tested. Among the most relevant results it was possible to show that (i) there was a large variation of previous exposure to HBV in both urban and non-urban groups ranging from 0 to 59.2%; (ii) there was a different pattern of epidemiological distribution of HBV that was present even among a same linguistic Indian group, with mixed patterns of correlation between HBsAg and anti-HBs and (iii) the prevalence of HBV markers (HBsAg and anti-HBs) were significantly higher (P=0.0001) among the Indian population (18.8%) than the urban groups (12.5%). Its possible that the host genetic background could influence and modulate the replication of the virus in order to generate HB carrier state.

Correlation between plasma and urine phenylalanine concentrations.

In this pilot study, we show that plasma phenylalanine concentration can be predicted from urine concentration if the age of the patient is taken into consideration. This observation could open the way to a new monitoring of phenylketonuric patients in which painful frequent blood sampling, mandatory to adapt the low phenylalanine diet, could be mostly replaced by urinalysis. Compliance to treatment would be improved and hence also the ultimate mental development. Since this study was based on a small number of patients, validation of the model in a large multicentric survey is needed before it can be recommended.

Spatial distribution of economic activities: an empirical approach usingself-organizing maps

The aim of this paper is to analyse the colocation patterns of industries and firms. We study the spatial distribution of firms from different industries at a microgeographic level and from this identify the main reasons for this locational behaviour. The empirical application uses data from Mercantile Registers of Spanish firms (manufacturers and services). Inter-sectorial linkages are shown using self-organizing maps. Key words: clusters, microgeographic data, self-organizing maps, firm location JEL classification: R10, R12, R34

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

An empirical analysis of a merger between a network and low-cost airlines

We use a difference-in-difference estimator to examine the effects of a merger involving three airlines. The novelty lies in the examination of this operation in two distinct scenarios: (1) on routes where two low-cost carriers and (2) on routes where a network and one of the low-cost airlines had previously been competing. We report a reduction in frequencies but no substantial effect on prices in the first scenario, while in the second we report an increase in prices but no substantial effect on frequencies. These results may be attributed to the differences in passenger types flying on these routes.

The correlation between inbreeding and fitness: does allele size matter?

The availability of highly polymorphic genetic markers, in particular microsatellites, has made it possible to test the effect of inbreeding on fitness in the field and in the absence of pedigree information. It has been suggested that the squared difference in allele size at a locus (d(2)) might be a better indicator of the level of inbreeding than is heterozygosity. Using an elegant new analytical model, Tsitrone et al. now put this idea to the test, and to rest.

Smooth Transition Patterns in the Realized Stock- Bond Correlation

Abstract: We analyze the realized stock-bond correlation. Gradual transitions between negative and positive stock-bond correlation is accommodated by the smooth transition regression (STR) model. The changes in regime are de…ned by economic and …financial transition variables. Both in sample and out-of- sample results document that STR models with multiple transition variables outperform STR models with a single transition variable. The most important transition variables are the short rate, the yield spread, and the VIX volatility index. Keywords: realized correlation; smooth transition regressions; stock-bond correlation; VIX index JEL Classifi…cations: C22; G11; G12; G17

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation

This paper analyses the impact of using different correlation assumptions between lines of business when estimating the risk-based capital reserve, the Solvency Capital Requirement (SCR), under Solvency II regulations. A case study is presented and the SCR is calculated according to the Standard Model approach. Alternatively, the requirement is then calculated using an Internal Model based on a Monte Carlo simulation of the net underwriting result at a one-year horizon, with copulas being used to model the dependence between lines of business. To address the impact of these model assumptions on the SCR we conduct a sensitivity analysis. We examine changes in the correlation matrix between lines of business and address the choice of copulas. Drawing on aggregate historical data from the Spanish non-life insurance market between 2000 and 2009, we conclude that modifications of the correlation and dependence assumptions have a significant impact on SCR estimation.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.