786 research outputs found

    Going Clubbing in the Eighties: Convergence in Manufacturing Sectors at a Glance

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    I adopt the distribution dynamics framework to study labor productivity convergence, in the period 1980-1995, among 28 developed and developing countries, in different manufacturing sub-sectors, identified, as according their technological content into Resource Based, Low Technology, Medium Technology and High Technology. I find that, exception made for High Technology and Manufacturing as a whole, all subcompartments are predicted to converge within small groups, validating the so-called club-convergence hypothesis. Thus, as high tech sectors are the ones opening the best growth-equity prospects, developing countries should target these kind of productions.Italian Regions; Neoclassical and Technological Convergence; Distribution Dynamics.

    Socio-economic contribution of African breadfruit (Treculia africana Decne) toward sustainable livelihood in eastern, Nigeria

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    Home gardens provide perspective for conservation of plant genetic resources while contributing to improving livelihoods. The Non-Timber Forest Products (NTFPs) the conserve are gathered for household consumption and commercial uses; they have formed an inherent part of rural economy for millennia and equally serve as safety net during periods of adverse environmental changes such as famine due to crop failure. Accordingly, the study was carried out to examine the level of income generation, processing, distribution of sales as well as the importance of Treculia africana to food security in Southeastern, Nigeria in 2015. The study was conducted in Okigwe agricultural zone, Imo State, Nigeria. A multi-stage sampling technique was employed for this study. Data collected was analysed using descriptive statistic. The results revealed that majority of the respondents were female (53.70%) and were married (84.40%). The source of the product was mainly from the home gardens (76.70%) and the reason for harvesting was for income generation and subsistence use (household consumption) (77.78%).The product was best harvested during rainy season (68.00%) when fruits are most abundant (48.90%). Majority of respondents (57.80%) sold Treculia africana kernel in cigarette cup ranging from ₩110−₩160 while the weekly income was between ₩2500−₩4500. The major problem affecting price rate of Traculia fruits and kernels was labour (25.20%), transportation (16.90%) and local tax (22.20%). It is therefore, recommended that appropriate conservation measures be put in place using sustainable policy framework that would enhance its in situ and ex situ conservation and equally ensure it protracted use in order to increase its abundance and availability.Keywords: Income, dietary, culinary values, African breadfruit, ukwa, sustainable Livelihoo

    Subgroup discovery for structured target concepts

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    The main object of study in this thesis is subgroup discovery, a theoretical framework for finding subgroups in data—i.e., named sub-populations— whose behaviour with respect to a specified target concept is exceptional when compared to the rest of the dataset. This is a powerful tool that conveys crucial information to a human audience, but despite past advances has been limited to simple target concepts. In this work we propose algorithms that bring this framework to novel application domains. We introduce the concept of representative subgroups, which we use not only to ensure the fairness of a sub-population with regard to a sensitive trait, such as race or gender, but also to go beyond known trends in the data. For entities with additional relational information that can be encoded as a graph, we introduce a novel measure of robust connectedness which improves on established alternative measures of density; we then provide a method that uses this measure to discover which named sub-populations are more well-connected. Our contributions within subgroup discovery crescent with the introduction of kernelised subgroup discovery: a novel framework that enables the discovery of subgroups on i.i.d. target concepts with virtually any kind of structure. Importantly, our framework additionally provides a concrete and efficient tool that works out-of-the-box without any modification, apart from specifying the Gramian of a positive definite kernel. To use within kernelised subgroup discovery, but also on any other kind of kernel method, we additionally introduce a novel random walk graph kernel. Our kernel allows the fine tuning of the alignment between the vertices of the two compared graphs, during the count of the random walks, while we also propose meaningful structure-aware vertex labels to utilise this new capability. With these contributions we thoroughly extend the applicability of subgroup discovery and ultimately re-define it as a kernel method.Der Hauptgegenstand dieser Arbeit ist die Subgruppenentdeckung (Subgroup Discovery), ein theoretischer Rahmen fĂŒr das Auffinden von Subgruppen in Daten—d. h. benannte Teilpopulationen—deren Verhalten in Bezug auf ein bestimmtes Targetkonzept im Vergleich zum Rest des Datensatzes außergewöhnlich ist. Es handelt sich hierbei um ein leistungsfĂ€higes Instrument, das einem menschlichen Publikum wichtige Informationen vermittelt. Allerdings ist es trotz bisherigen Fortschritte auf einfache Targetkonzepte beschrĂ€nkt. In dieser Arbeit schlagen wir Algorithmen vor, die diesen Rahmen auf neuartige Anwendungsbereiche ĂŒbertragen. Wir fĂŒhren das Konzept der reprĂ€sentativen Untergruppen ein, mit dem wir nicht nur die Fairness einer Teilpopulation in Bezug auf ein sensibles Merkmal wie Rasse oder Geschlecht sicherstellen, sondern auch ĂŒber bekannte Trends in den Daten hinausgehen können. FĂŒr EntitĂ€ten mit zusĂ€tzlicher relationalen Information, die als Graph kodiert werden kann, fĂŒhren wir ein neuartiges Maß fĂŒr robuste Verbundenheit ein, das die etablierten alternativen Dichtemaße verbessert; anschließend stellen wir eine Methode bereit, die dieses Maß verwendet, um herauszufinden, welche benannte Teilpopulationen besser verbunden sind. Unsere BeitrĂ€ge in diesem Rahmen gipfeln in der EinfĂŒhrung der kernelisierten Subgruppenentdeckung: ein neuartiger Rahmen, der die Entdeckung von Subgruppen fĂŒr u.i.v. Targetkonzepten mit praktisch jeder Art von Struktur ermöglicht. Wichtigerweise, unser Rahmen bereitstellt zusĂ€tzlich ein konkretes und effizientes Werkzeug, das ohne jegliche Modifikation funktioniert, abgesehen von der Angabe des Gramian eines positiv definitiven Kernels. FĂŒr den Einsatz innerhalb der kernelisierten Subgruppentdeckung, aber auch fĂŒr jede andere Art von Kernel-Methode, fĂŒhren wir zusĂ€tzlich einen neuartigen Random-Walk-Graph-Kernel ein. Unser Kernel ermöglicht die Feinabstimmung der Ausrichtung zwischen den Eckpunkten der beiden unter-Vergleich-gestelltenen Graphen wĂ€hrend der ZĂ€hlung der Random Walks, wĂ€hrend wir auch sinnvolle strukturbewusste Vertex-Labels vorschlagen, um diese neue FĂ€higkeit zu nutzen. Mit diesen BeitrĂ€gen erweitern wir die Anwendbarkeit der Subgruppentdeckung grĂŒndlich und definieren wir sie im Endeffekt als Kernel-Methode neu

    The characteristics of the consumption of medicinal herbs in Hungary

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    Our research we aimed to examine a topic that has so far not been explored. In our study we analysed consumer habits related to herbs in Hungary. The national representative questionnaire involved 1050 individuals. Several multivariable statistical techniques were applied for the analysis of the data: Principal Component Analysis (PCA), cluster analysis. In our analysis we found that one third of the respondents are regular consumers. Typically, they turn to herbs for colds, flu or stomach upsets. The most popular are chamomile, rosehip, and lime flowers (consumed in the form of dried or essential oils). As a new element in our research, we studied health attitudes to the use of medicinal herbs. Based on health attitudes, we identified a total of 5 influencing factors: health awareness, sport, healthy nutrition, medical check-ups and the presence of some illness. A total of 5 clusters were defined for these factors: comprehensive regenerators, seeking objective control, comprehensive health consciousness, regenerating with food, non-users. The most important element of our conclusions is that strengthening preventive healthcare can be one of the key factors in the growth of medicinal herb consumption. Medical feedback about the state of health makes people more willing to purchase herbs. The expert recommendation as the key communication channel is an important element too

    Extensive pollen flow but few pollen donors and high reproductive variance in an extremely fragmented landscape

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    Analysing pollen movement is a key to understanding the reproductive system of plant species and how it is influenced by the spatial distribution of potential mating partners in fragmented populations. Here we infer parameters related to levels of pollen movement and diversity of the effective pollen cloud for the wind-pollinated shrub Pistacia lentiscus across a highly disturbed landscape using microsatellite loci. Paternity analysis and the indirect KinDist and Mixed Effect Mating models were used to assess mating patterns, the pollen dispersal kernel, the effective number of males (N(ep)) and their relative individual fertility, as well as the existence of fine-scale spatial genetic structure in adult plants. All methods showed extensive pollen movement, with high rates of pollen flow from outside the study site (up to 73-93%), fat-tailed dispersal kernels and large average pollination distances (ή = 229-412 m). However, they also agreed in detecting very few pollen donors (N(ep) = 4.3-10.2) and a large variance in their reproductive success: 70% of males did not sire any offspring among the studied female plants and 5.5% of males were responsible for 50% of pollinations. Although we did not find reduced levels of genetic diversity, the adult population showed high levels of biparental inbreeding (14%) and strong spatial genetic structure (S(p) = 0.012), probably due to restricted seed dispersal and scarce safe sites for recruitment. Overall, limited seed dispersal and the scarcity of successful pollen donors can be contributing to generate local pedigrees and to increase inbreeding, the prelude of genetic impoverishment

    Assessing European competitiveness: The new CompNet micro-based database. National Bank of Belgium Working Paper No. 279

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    Drawing from confidential firm-level balance sheets for 17 European countries (13 Euro-Area), the paper documents the newly expanded database of cross-country comparable competitivenessrelated indicators built by the Competitiveness Research Network (CompNet). The new database provides information on the distribution of labour productivity, TFP, ULC or size of firms in detailed 2-digit industries but also within broad macro-sectors or considering the full economy. Most importantly, the expanded database includes detailed information on critical determinants of competitiveness such as the financial position of the firm, its exporting intensity, employment creation or price-cost margins. Both the distribution of all those variables, within each industry, but also their joint analysis with the productivity of the firm provides critical insights to both policymakers and researchers regarding aggregate trends dynamics. The current database comprises 17 EU countries, with information for 56 industries, including both manufacturing and services, over the period 1995-2012. The paper aims at analysing the structure and characteristics of this novel database, pointing out a number of results that are relevant to study productivity developments and its drivers. For instance, by using covariances between productivity and employment the paper shows that the drop in employment which occurred during the recent crisis appears to have had “cleansing effects” on EU economies, as it seems to have accelerated resource reallocation towards the most productive firms, particularly in economies under stress. Lastly, this paper will be complemented by four forthcoming papers, each providing an in-depth description and methodological overview of each of the main groups of CompNet indicators (financial, trade-related, product and labour market)

    Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

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    This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way

    Diagnostic Expectations and Stock Returns

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    We revisit La Porta’s (1996) finding that returns on stocks with the most optimistic analyst long term earnings growth forecasts are substantially lower than those for stocks with the most pessimistic forecasts. We document that this finding still holds, and present several further facts about the joint dynamics of fundamentals, expectations, and returns for these portfolios. We explain these facts using a new model of belief formation based on a portable formalization of the representativeness heuristic. In this model, analysts forecast future fundamentals from the history of earnings growth, but they over-react to news by exaggerating the probability of states that have become objectively more likely. Intuitively, fast earnings growth predicts future Googles but not as many as analysts believe. We test predictions that distinguish this mechanism from both Bayesian learning and adaptive expectations, and find supportive evidence. A calibration of the model offers a satisfactory account of the key patterns in fundamentals, expectations, and returns
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