1,721,022 research outputs found
Market Integration and Regional Divide: What role for trade and ICT?
No full textGlobalisation of markets and acceleration of technological innovation have changed the way economic systems evolve all over the world. We examine the existing empirical evidence on the way these economic transformations are impacting on growth. First, we survey the modern debate on convergence, focusing on the empirical evidence on cross-country differences in growth rates and in particular on the issue of global and local convergence (growth clubs). Secondly, we examine the conditions under which the two major pillars of globalization, trade liberalization and technology diffusion, may induce perverse effects on economic growth inducing “underdevelopment traps”, “polarization” and inequality. In the last section we highlight the reasons why a deficit of strong national and international institutions, a lack of investment in human and social capital and of cooperation among regulatory authorities, are to be considered as the main problems to be solved to fight against perverse effects of globalization
Comparison among different FT-MIR spectra treatments for the prediction of coagulation properties of individual milk of Brown Swiss cows
No full textQUALITY MATTERS. ITALY’S INTRA-INDUSTRYTRADE WITH EASTERN EUROPEOVER THE YEARS 1988-’95
No full textThis paper studies the main changes observed over the period 1988-1995 in the Italian trade with several Central and Eastern European (CEE) economies. The analysis is carried out using a number of methods. The first method consists of calculating the weight and the evolution of
high and low quality intra-industry trade. The second method consists of looking at the features
assumed by the trade reorientation using a Constant Market Share Analysis, able to break down
the growth in CEE exports to Italy into a “demand” and a “competitiveness” share. Once verified
the dominance of the latter, the paper tests by means of regression analysis to what extent
the increased competitiveness has been determined by increased price competitiveness (term of
trade effect) as opposed to quality upgrading. No evidence is found of the latter. A test of the employment effects associated with different sources of trade with the CEECs confirms the importance of monitoring the evolution of vertical and horizontal intra-industry trade and its impact on the adjustment process
“EU Integration with Mediterranean Partner Countries vis-à-vis CEE-10. A Gravity Study”
No full text
"Mind the Gap: Unemployment in the New EU Regions"
No full textThe paper surveys the theoretical and empirical literature on regional unemployment during transition in Central and Eastern Europe. The focus is on optimal speed of transition (OST) models and on comparison of them with the neo-classical tradition. In the typical neo-classical models, spatial differences essentially arise as a consequence of supply side constraints and institutional rigidities. Slow-growth, high-unemployment regions are those with backward economic structures and constraints on factors mobility contribute to making differences persistent. However, such explanations leave the question unanswered of how unemployment differences arise in the first place. Economic transition provides an excellent testing ground to answer this question. Pre-figuring an empirical law, the OST literature finds that the high degree of labour turnover of high unemployment regions is associated with a high rate of industrial restructuring and, consequently, that low unemployment may be achieved by implementing transition more gradually. Moreover, international trade, foreign direct investment and various agglomeration factors help explain the success of capital cities compared to peripheral towns and rural areas in achieving low unemployment. The evidence of the empirical literature on supply side factors suggests that wage flexibility in Central and Eastern Europe is not lower than in other EU countries, while labour mobility seems to reinforce rather than change the spatial pattern of unemployment
Factor Endowment and Market Size in EU-CEE Trade. Would Human Capital Change the Actual Quality Trade Patterns?
Full text linkThis paper aims to test several hypotheses on the determinants of the quality of trade in
cross-country regressions, taking a sample of trade competitors in EU markets. The
hypotheses are those underlying two models of VIIT: the so-called neo-H-O model based on
factor endowment and an ?economic geography? model based on market size and economic
integration. As the explanatory variables used (proxies for human capital, physical capital,
market size and a dummy for market integration) significantly affect the dependent variable
(unit-value differences), it seems plausible to conclude that these variables give rise to
specialisation in different segments of the quality spectrum. Much information is drawn from
the analysis with respect to CEE specialisation in low-quality exports to EU markets. In
particular, the estimates suggest the existence of a process of ?crowding out? of the existing
human capital due to the process of economic transition. Moreover, the smaller market size
of the EU accession countries could contribute to strengthen the disadvantage in high quality
segments of production. In fact, the significant coefficient of the variable used to measure
market size suggests that liberalisation might be accompanied by increased concentration of
high-quality productions in large markets. However, the geographic proximity to the core of
Europe could counterbalance this force. The integration process itself could accelerate the
process of catching up in terms of quality of products and of per capita income, providing
Eastern producers with a larger market and potential for economies to scale
Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI)
No full text: Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants
Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy
No full textCheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerà ̧d, Denmark) over the spectral range, from 5,000 to 900 wavenumberÃcm-1. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow
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