Trade Liberalization in Latin America and Eastern Europe: The Cases of Ecuador and Slovenia

This paper analyzes the potential effects of two ongoing trade liberalization experiences: Ecuador signing a Free Trade Agreement with the United States and Slovenia joining the European Union as a full member. We construct a static Applied General Equilibrium Model and perform a numerical experiment that consists on eliminating all import tariffs that Ecuador and Slovenia impose on the United States and European Union, respectively. To calibrate our models, we work with Input-Output tables and construct a Social Accounting Matrix for each country. We perform additional numerical experiments, such as sensitivity analysis on the import and export elasticities of substitution, a partial liberalization scenario, the fiscal impact of eliminating the tariff revenues and how this loss can be compensated with other taxes, and an alternative trade liberalization framework for Slovenia. We find that both countries benefit from these trade liberalization reforms, with prices falling in the import sector and production rising in the export sector. However, different forms of trade liberalization (free trade agreement vs. customs union) have different implications on the patterns of trade and welfare.Trade Liberalization; Free Trade Agreement; Customs Union; Fiscal Policy; Social Accounting Matrix; Ecuador; Slovenia

Welfare Impact of Trade Liberalization

This paper constructs a static Applied General Equilibrium Model and analyzes the distributional impact of trade reforms. To calibrate our model, we work with the Household Expenditure Survey to disaggregate household groups by income, age, and skill intensity, and the Input-Output table to construct a Social Accounting Matrix. Our benchmark simulation looks at Slovenia joining the European Union. We then compare with two alternative scenarios: a free trade agreement between Slovenia and the EU, and an alternative fiscal arrangement of distributing tariff revenues under the EU. While trade reforms lead to falling prices in the import sector, rising production in the export sector, and improvement in aggregate welfare, the distributional impacts across household groups vary in its degree.Trade Liberalization; Free Trade Agreement; Customs Union; Social Accounting Matrix; Household welfare

New Goods Trade in the Baltics

We analyze the role of the new goods margin—those goods that initially account for very small volumes of trade—in the Baltic states’ trade growth during the 1995-2008 period. We find that, on average, the basket of goods that in 1995 accounted for 10% of total Baltic exports and imports to their main trade partners, represented nearly 50% and 25% of total exports and imports in 2008, respectively. Moreover, we find that the share of Baltic new-goods exports outpaced that of other transition economies of Central and Eastern Europe. As the International Trade literature has recently shown, these increases in newly-traded goods could in turn have significant implications in terms of welfare and productivity gains within the Baltic economies

Atomiser, Source, Inductively Coupled Plasmas in Atomic Fluoresence Spectrometry (ASIA): A Study of Chemical and Ionisation Interference Effects

The effects of phosphate, aluminium, sodium and potassium on the atomic fluorescence of calcium at 422.7 nm and the ionic fluorescence at I:393.4-396.8 nm have been studied. When the operating conditions are optimised for maximum fluorescence signal from a solution containing no interferents, interference effects are observed which may be interpreted in terms of stable compound formation, ionisation suppression and fluorescence quenching. These effects may be removed by optimising the operating parameters for minimum interference

Continuing Developments in Atomizer, Source, Inductively Coupled Plasmas in Atomic Fluorescence Spectrometry

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Comparison of Alternating Variable Search and Simple Methods of Optimisation for Inductively Coupled Plasma Optical Emission and Atomic Fluorescence Spectrometry

The performance of several cyclic alternating variable search (AVS) optimisation methods are compared with two simplex methods with respect to the number of changes of variable required to search a model two-factor response space. The roles of the initial step size and of the variable step size are discussed, and the information produced concerning the shape of the factor space is evaluated. An AVS method which starts with a fixed step size and then changes to a variable step size on second and subsequent cycles is compared with a variable step size simplex for the optimisation of an inductively coupled plasma optical emission spectrometer and of the atomiser, source inductively coupled plasmas in atomic fluorescence spectrometry (the ASIA system). The order in which the variables are taken in the AVS method does not affect the value of the optimum eventually found. Both methods perform satisfactorily for the optical emission work, although the AVS method provides information about the shape of the factor space which is easier to interpret than in the simplex method. However, the simplex method was not always able to satisfy the conditions for termination in the case of the atomic fluorescence studies and was much slower to implement than the AVS method as the latter used direct visual feedback from the output of the lock-in amplifier as a measure of the figure of merit (total fluorescence signal)

A Comparison of Boosted-Discharge Hollow Cathode Lamps and an Inductively Coupled Plasma (ICP) as Excitation Sources in ICP Atomic Fluorescence Spectrometry

Copper, nickel and lead boosted-discharge hollow cathode lamps, run at recommended currents, have been compared with a high-powered inductively coupled plasma (ICP) as excitation sources in atomic fluorescence spectrometry (AFS). A similar comparison was made with a copper lamp run at higher currents. It was found that for lead and nickel, the fluorescence spectra differed in the relative intensities of the transitions observed with the two sources. No evidence was found for a difference in radiances between the two sources when the lamp was overrun. Although the lamps gave rise to lower blank standard deviation values, detection limits were worse because of poorer sensitivity due to the inability of the circular source to illuminate the required atom cell volume in the atomiser. It was concluded that the ICP was the better source, when the criterion is detection limits, but the lamps may be more convenient in some circumstances

Integration of metabolomics, lipidomics and clinical data using a machine learning method.

BACKGROUND: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, -γ, and -δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. RESULTS: In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. CONCLUSIONS: We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism