Three Essays on Taxation, Growth and Consumption

The purpose of this dissertation is to examine three distributional issues in macroeconomics. First I explore the effects fiscal federalism on economic growth across regions in China. Using the comprehensive official data set of China for 31 regions from 1952 until 1999, I investigate a number of indicators used by the literature to measure federalism and find robust support for only one such measure: the ratio of local total revenue to local tax revenue. Using a difference-in-difference approach and exploiting the two-year gap in the implementation of a tax reform across different regions of China, I also identify a positive relationship between fiscal federalism and regional economic growth. The second paper hypothesizes that an inequitable distribution of income negatively affects the rule of law in resource-rich economies and provides robust evidence in support of this hypothesis. By investigating a data set that contains 193 countries and using econometric methodologies such as the fixed effects estimator and the generalized method of moments estimator, I find that resource-abundance improves the quality of institutions, as long as income and wealth disparity remains below a certain threshold. When inequality moves beyond this threshold, the positive effects of the resource-abundance level on institutions diminish quickly and turn negative eventually. This paper, thus, provides robust evidence about the endogeneity of institutions and the role income and wealth inequality plays in the determination of long-run growth rates. The third paper sets up a dynamic general equilibrium model with heterogeneous agents to investigate the causal channels which run from a concern for international status to long-run economic growth. The simulation results show that the initial distribution of income and wealth play an important role in whether agents gain or lose from globalization.

Crisis, educación y precariedad-afluencia. El rol de la educación en las condiciones de vida de la población española

El objeto de este artículo es estudiar la influencia del nivel educativo (capital cultural) en los procesos de precariedad-afluencia de la población española entre los años posteriores a la crisis de inicio de la década de 1990 y los años más duros de la crisis de 2007. A partir de los datos de las encuestas PHOGUE y ECV del Instituto Nacional de Estadística (INE) se han construido cuatro indicadores para medir la precariedad laboral, de ingresos, de salud y de vivienda y su distribución según distintas variables demográficas. Se pretende contrastar la hipótesis de que más educación significa más protección frente a la precariedad, estudiando diferentes condiciones de las condiciones de vida y existencia en momentos tanto de crecimiento como de crisis económica. Mediante un análisis multivariable se intenta determinar el nivel de impacto del capital cultural, alcance, evolución y, sobre todo, si sus efectos positivos o negativos están en proceso de expansión o desaceleración. El resultado tiene una doble aportación: de un lado, metodológica, consistente en la construcción de los indicadores; de otro lado, los resultados, con los que se puede reevaluar algunas generalizaciones sobre la pérdida de importancia del rol de la educación en las sociedades contemporáneas.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.

Women of an uncertain age: quantifying human capital accumulation in rural Ireland in the nineteenth century

Geary and Stark find that Ireland’s post-Famine per capita GDP converged with British levels, and that this convergence was largely due to total factor productivity growth rather than mass emigration. In this article, new long-run measurements of human capital accumulation in Ireland are devised in order to facilitate a better assessment of sources of this productivity growth, including the relative contribution of men and women. This is done by exploiting the frequency at which age data heap at round ages, widely interpreted as an indicator of a population’s basic numeracy skills. Because Földvári, van Leeuwen, and van Leeuwen-Li find that gender-specific trends in this measure derived from census returns are biased by who is reporting and recording the age information, any computed numeracy trends are corrected using data from prison and workhouse registers, sources in which women ostensibly self-reported their age. The findings show that rural Irish women born early in the nineteenth century had substantially lower levels of human capital than uncorrected census data would otherwise suggest. These results are large in magnitude and thus economically significant. The speed at which women converged is consistent with Geary and Stark’s interpretation of Irish economic history; Ireland probably graduated to Europe’s club of advanced economies thanks in part to rapid advances in female human capital.

Application of GPCR Structures for Modelling of Free Fatty Acid Receptors

Five G protein-coupled receptors (GPCRs) have been identified to be activated by free fatty acids (FFA). Among them, FFA1 (GPR40) and FFA4 (GPR120) bind long-chain fatty acids, FFA2 (GPR43) and FFA3 (GPR41) bind short-chain fatty acids and GPR84 binds medium-chain fatty acids. Free fatty acid receptors have now emerged as potential targets for the treatment of diabetes, obesity and immune diseases. The recent progress in crystallography of GPCRs has now enabled the elucidation of the structure of FFA1 and provided reliable templates for homology modelling of other FFA receptors. Analysis of the crystal structure and improved homology models, along with mutagenesis data and structure activity, highlighted an unusual arginine charge pairing interaction in FFA1-3 for receptor modulation, distinct structural features for ligand binding to FFA1 and FFA4 and an arginine of the second extracellular loop as a possible anchoring point for FFA at GPR84. Structural data will be helpful for searching novel small molecule modulators at the FFA receptors.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.

Joint Leverage and Maturity Choices in Real Estate Firms: The Role of the REIT Status

We explore the interdependence of leverage and debt maturity choices in Real Estate Investment Trusts (REITs) and unregulated listed real estate investment companies in the U.S. for the period 1973-2011. We find that the leverage and maturity choices of all listed real estate firms are interdependent, but in contrast to industrial firms, they are not made simultaneously. Across the different types of real estate firms considered, we find substantial differences in the nature of the relationship between leverage and maturity. Leverage determines maturity in non-REITs, whereas maturity is a determinant of leverage in REITs. We suggest that the observed differences reflect the effects of the REIT regulation, rather than solely being a function of real estate as the underlying asset class. We also present novel evidence that the relationship between leverage and maturity in both firm types can be used to moderate the effects of other exogenous financing policies.

Rethinking the Eclectic Paradigm: Explaining MNE E-Commerce Corporations' Activity in the Chinese Digital Sector

We use the eclectic paradigm as an analytical framework to explain the MNE e-commerce company’s activities in China. Grounded in the rich data, we argue that the dynamic interplay between the ownership advantage and local institutional context that have emerged—particularly in the information age—plays a significant role in explaining the trajectory of MNE e-commerce companies in China. We propose On, Ln and In by embedding network-based advantages within the OLI paradigm. With the acceleration of technological change and non-ergodic uncertainty, such a network-embedded eclectic paradigm will lead to MNE e-commerce companies’ sustainable development in the emerging economy.

Determinantes da liquidez das PME da indústria transformadora em Portugal

Este artigo visa identificar os determinantes da liquidez das pequenas e médias empresas (PME) portuguesas, assim como analisar até que ponto estes se alteram quando analisamos períodos de estabilidade ou de recessão financeira. Para tal, recorremos a uma amostra de dados em painel, considerando 4.355 PME, e analisando o período compreendido entre 2002 e 2011. De um modo geral, os resultados confirmam a existência de uma relação significativa entre algumas das variáveis independentes e a liquidez das empresas. Mais especificamente, os resultados evidenciam uma relação positiva entre a dimensão, a rendibilidade e a probabilidade de existência de problemas financeiros, e a liquidez das empresas, bem como uma relação negativa entre o grau de endividamento e a maturidade da dívida, e a liquidez das PME. Os resultados mostram ainda que a liquidez das empresas é afetada em períodos de crise financeira, verificando-se, nomeadamente, uma redução da dívida de curto prazo e um aumento da duração do ciclo de conversão de caixa.

A Corrupção burocrática inibe o empreendedorismo? uma análise empírica dos estados brasileiros

This work aims to investigate the relationship between the entrepreneurship and the incidence of bureaucratic corruption in the states of Brazil and Federal District. The main hypothesis of this study is that the opening of a business in Brazilian states is negatively affected by the incidence of corruption. The theoretical reference is divided into Entrepreneurship and bureaucratic corruption, with an emphasis on materialistic perspective (objectivist) of entrepreneurship and the effects of bureaucratic corruption on entrepreneurial activity. By the regression method with panel data, we estimated the models with pooled data and fixed and random effects. To measure corruption, I used the General Index of Corruption for the Brazilian states (BOLL, 2010), and to represent entrepreneurship, firm entry per capita by state. Tests (Chow, Hausman and Breusch-Pagan) indicate that the random effects model is more appropriate, and the preliminary results indicate a positive impact of bureaucratic corruption on entrepreneurial activity, contradicting the hypothesis expected and found in previous articles to Brazil, and corroborating the proposition of Dreher and Gassebner (2011) that, in countries with high regulation, bureaucratic corruption can be grease in the wheels of entrepreneurship

X-Ray crystal structure of [Cu2OH(O2CCF 3)3(quinoline)2]2, a tetranuclear basic copper(II) carboxylate adduct with significant metal-metal interaction

The basic copper(II) carboxylate adduct, [Cu2-OH(O 2CCF3)3(quinoline)2]2, has been shown by an X-ray structural analysis to have a novel tetranuclear structure; magnetic susceptibility data show that substantial Cu-Cu interaction is present in this compound.

Os determinantes da liquidez: evidência nas empresas portuguesas cotadas na Euronet Lisbon

Neste estudo são analisados, através de técnicas adequadas para dados em painel, os determinantes da liquidez das empresas portuguesas cotadas na Euronext Lisbon. Para a concretização do mesmo foi utilizada uma amostra de 40 empresas, para o período de 2000 a 2014, sendo este período dividido em dois subperíodos, o antes e o depois da crise. Os resultados evidenciam a existência de uma relação entre algumas das variáveis independentes e a variável dependente. De facto, antes da crise, verifica-se que as oportunidades de crescimento e a probabilidade de dificuldades financeiras são significativas para o nível da liquidez, sendo que, depois da crise, as variáveis explicativas do nível de liquidez são a volatilidade dos fluxos de caixa, o ciclo de conversão de caixa e a probabilidade de dificuldades financeiras.