Computational intelligence for measuring macro-knowledge competitiveness

The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators

Knowledge-Based Economy in Developing Countries: Measurements and Impacts

The traditional factors of production, such as land, labour, and capital, have typically determined a nation’s comparative advantage. However, in the context of a global Knowledge-Based Economy (KBE), a nation’s prosperity is now determined by its knowledge assets. This transition to a KBE offers endless advantages and is desirable for all countries. However, developing countries face significant challenges in adopting this new development paradigm, where knowledge is the key driver of economic growth. Yet, to effectively measure the extent to which a country is considered knowledge-based on the international level, a robust framework is needed. Although the burgeoning literature, existing KBE measurement frameworks have limitations and may not accurately reflect the progress and efficiency of the transition to a KBE, especially in developing countries. Consequently, relying on these frameworks can lead to misleading policy directions that hinder the necessary rapid transition in developing countries. This thesis aims to fill the gap in understanding the KBE within developing countries through an extensive analysis. To achieve this, the thesis begins by reviewing the conceptual and theoretical literature on the KBE. It then critically examines existing measurement frameworks and empirical studies related to the KBE, specifically evaluating their suitability for developing countries. In response to the limitations found, a new and more effective measurement framework is proposed. This framework focuses on input-output indicators across four dimensions of the KBE: acquisition, distribution/dissemination, production, and utilization. Notably, it utilizes a non-parametric approach known as Data Envelopment Analysis (DEA), which differs from conventional econometric analysis. The DEA empirical results are then compared with those obtained from other existing KBE measurement frameworks, allowing for a comprehensive assessment of the advantages offered by DEA. Based on the DEA empirical findings, knowledge production is identified as the weakest aspect, despite its utmost importance among the four KBE dimensions. As a result, this thesis places special emphasis on enhancing innovation development in selected developing countries through effective innovation policies tailored to their specific circumstances and utilizing country-specific innovation policy instruments

The distribution of debt across Euro Area countries : the role of individual characteristics, institutions and credit conditions

Incluye referencias bibliográficasEste trabajo tiene un doble objetivo. En primer lugar, se presenta un análisis actualizado de las diferencias entre los países de la zona del euro en las distribuciones de varias medidas de deuda condicionadas a las características de los hogares. Se consideran tres magnitudes diferentes: la probabilidad de mantener deuda, el importe de la deuda mantenida y, en el caso de la deuda garantizada, el tipo de interés de la hipoteca principal. En segundo lugar, se examina el papel que desempeñan las instituciones jurídicas y económicas en dichas diferencias. Para lograr los objetivos mencionados se utilizan datos de la primera ola de una nueva encuesta sobre la situación financiera de las familias, la Encuesta sobre la Situación Financiera y el Consumo de los Hogares (HFCS), y se constata que los patrones de los resultados de la deuda garantizada y sin garantizar varían notablemente de un país a otro. Entre todas las instituciones consideradas, la duración de los procesos de ejecución hipotecaria es la variable que mejor explica las características de la distribución de la deuda garantizada. En los países con períodos de embargo más prolongados, la proporción de prestatarios es menor, el grupo de familias más jóvenes se endeuda por importes más reducidos (condicionado a endeudarse) y los hogares con rentas bajas pagan tipos de interés hipotecarios más elevados. Las ratios préstamo-valor regulatorias, la tributación de las hipotecas y la prevalencia de las hipotecas con amortización al vencimiento o a tipo de interés fijo dan lugar a resultados menos robustosThe aim of this paper is twofold. First, we present an up-to-date assessment of the differences across euro area countries in the distributions of various measures of debt conditional on household characteristics. We consider three different outcomes: the probability of holding debt, the amount of debt held and, in the case of secured debt, the interest rate paid on the main mortgage. Second, we examine the role of legal and economic institutions in accounting for these differences. We use data from the first wave of a new survey of household finances, the Household Finance and Consumption Survey, to achieve these aims. We find that the patterns of secured and unsecured debt outcomes vary markedly across countries. Among all the institutions considered, it is the length of asset repossession periods that best accounts for the features of the distribution of secured debt. In countries with longer repossession periods, the fraction of people who borrow is smaller, the youngest group of households borrow lower amounts (conditional on borrowing), and the mortgage interest rates paid by low-income households are higher. Regulatory loan-to-value ratios, the taxation of mortgages and the prevalence of interest-only or fi xed-rate mortgages deliver less robust result

Financial distress identification among energy sector companies listed in bursa Malaysia

The main objective of this study is to identify financial distress of Energy Companies listed in Bursa Malaysia. The study indicates a strong significance of the variable in determining financial distress using three different models which is Altman Z-Score Model, Grover Model and Zmijewski Model. The study adopts quantitative research method of secondary source was used to collect data from annual financial report of all the energy companies listed in Bursa Malaysia. The period of study is prior Covid 19 crisis which is from 2016 to 2020. The descriptive and comparative analysis method has been used for the study and this model is developed to compare the independent variables. The descriptive analysis in this study shows that Altman Z-Score model is the most significant model to predict financial failure of the firms. While the comparative analysis shows there is no significant difference between Altman and Grover, Altman and Zmijewski as well as Grover and Zmijewski