Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

This is a research on Political Economy, Macroeconomics and Applied Economy, based on the Austrian Economics´ approach over the economic cycles and applied to the Spanish case (2002-2014). Attention is focused on the endemic problem of economic cycles, such as successive and distorting periods of boom and recession that modern economies suffer, such as the Spanish economy. This is an inquire to find empirical evidence in favor of the Austrian Business Cycle Theory, for the specific Spanish case between 2002 and 2014, through the application of econometric methods. Consequently, after reviewing the scarce previous econometric literature on the Austrian cycle, this research has as its main objective to explain and illustrate the boom with the effective introduction of the euro in 2002, the recession during 2008-11 and the recovery in 2014, using the Austrian theory of the endogenous and non-neutral monetary cycle.

Industry 4.0 was born amid a technological breakthrough and development which set information out to become a valuable element in strengthening competitive advantages and innovation processes to improve organizations productivity performance.  For this, the scientific and business community is debating the negative and positive effects of these technologies and their relationship with business management, in the search for sustainable solutions for organizations. In this sense, the main purpose of this article is to identify the perceptions of SME managers in Colombia on the importance of implementing information technologies to promote competitive advantages that match the pace of industry 4.0. The research method used in the study was analysed by sampling using the questionnaire as a data collection tool. 75 managers provided complete and useful answers to this research. The collected data were analysed using different regression models were carried out; showing like principal results that a greater use of these technologies generates a positive impact on the competitive ad-vantage related to price/cost, quality, delivery reliability, product innovation and time to market. Statistical Package for the Social Sciences (SPSS) was used to the analysis.

The current study aims to examine and analyze the effect of soft skills on working readiness. Respondents of the current study were active students from several universities in Indonesia. The sampling technique used is non-probability sampling, namely convenient sampling. Data analysis were performed using multiple linear regression. The results show that learning throughout life skills, creativity, teamwork skills, and leadership skills significantly influence on working readiness. Meanwhile, problem-solving skills, communication skills, and ethics have no significant effect on working readiness. The results of the current study provide a theoretical contribution regarding the relationship between soft skills and working readiness. Future research is urgent to conduct to provide a clearer picture of the relationship between soft skills and working readiness. The current research gives students awareness to improve their soft skills. The study also provides a managerial contribution for universities to formulate the concept of developing soft skills for their students.

We analyze volatility contagion between the U.S. and Chinese stock markets and international capital markets. The volatility is modeled using: GARCH, TARCH, EGARCH, APARCH, IGARCH, FIGARCH, ACGARCH and GAS models under Gaussian, GED and t-Student distributions. 21,000 intraday observations of thirteen markets from January/1st to June/25th 2020 are employed. Once volatility is modeled, the incidence of Chinese and American markets on the rest of the bourses is tested employing Vector Autoregressive Markov Switching Models. Evidence confirms incidence of the Chinese and American capital markets volatility in other markets volatility; common breakpoints and Intermarket incidence in high volatility periods stand out.

In this paper, we study the relationship between reconciliations in the Colombian electricity market and the bid prices by firms on the spot market. In this work, we propose a model of behavior of the firm to elaborate theoretical predictions about the relationship between the reconciliations and the bid prices. Subsequently, we developed an empirical strategy to test the predictions of our model. We find that when firms expect their units to have negative reconciliations the next day, they tend to reduce the bid prices for their units. To positive reconciliations, there is no evidence of any relationship.

This paper aims to estimate the probability of growth aspirations of Colombian entrepreneurs according to the generation in which they were born. There is a large literature on the determinants of the growth aspiration, but there is not much empirical evidence on the possible effects of belonging to a specific generation. We used data from the Global Entrepreneurship Monitor for the period between 2006 and 2017. Specifically, the data were taken from the Adult Population Survey, which measures certain relevant characteristics of entrepreneurs around the world. Based on a sample of 6,670 individuals, three ordinal logistic regression models were estimated, one for each generation: Baby Boomers, Generation X and Generation Y-Millennials. The results show that the youngest entrepreneurs, the Millennials, have a very different conception of life than their predecessors in relation to their view of entrepreneurship as a life choice. In fact, structural factors such as gender, education and motive for entrepreneurship are significant for all generations. However, context-related factors such as their skills, fear of failure, recognition, and their view of entrepreneurship as a desirable career have different effects on each generation. These results are relevant for the development of public policies and business strategies associated with entrepreneurship in developing regions.

The objective of this work is the construction of a probability credit scoring model in order to minimize the risk of default on payment of the customer portfolio, for which dependent (customer good and bad) and independent (characteristic of customer) variables were used to provide a correct analysis to determine whether or not the company grants a loan. The descriptive methodology and quantitative and qualitative approaches were applied taking as primary sources the data of the customer portfolio of the company SEGUMAR S.A. The database consists of the information of 100 people applying for a loan and is included in the measurement of 7 variables for each person. Each applicant is classified into one of two possible categories, "good customer" (70 cases) or "bad customer" (30 cases).  A credit scoring rule was developed to determine whether a new applicant is a "Good" or "Bad" customer, based on the values of one or more explanatory variables resulting from the final model. This study evaluated the characteristics that customers have at the time of requesting a loan and that according to the characteristics of each customer it is possible to make predictions, classify them as a good customer or a bad customer. In the results obtained from the Logit model it can be concluded that the selected variables that were applied in the model gave us a 76% success rate that allows us to classify each of our customers as a good customer or bad customer in our model.

This article consisted of the application of the CAPM model on companies listed on the stock exchange of and related to the construction sector in Colombia during the period from January 1, 2015 to December 31, 2020. This research has a quantitative approach with a type of descriptive and longitudinal research. Its methodology consisted in the application of ordinary least squares to the daily volatilities of the asset based on the estimation of the betas, which mostly complied with the probabilistic values and the intercepts were not different from 0, which is consistent with the model hypothesis. As for the variables that accompany the model, the risk-free rate TFIT16240724 was chosen and the ICOLCAP index was chosen as the variable that measures the market risk. Finally obtained the results, these were evaluated, concluding that the beta coefficient is an acceptable indicator in the risk-return assessment of the asset during the period in question, however, as an estimator it is not effective, which reflects the ineffectiveness of the model.

An Industrial Cluster constitutes a strategic unit to develop industrial policies to improve sustainable competitiveness, especially for small and medium-sized enterprises (SMEs). The objective of the research was to carry out the diagnosis of the supply chain in the tannery cluster, leather, and footwear the province of Trujillo (Peru), considering its strategy around 14 themes for analysis. The method used was the “Case Study”, with a sample of ten SMEs from the most representative manufacturing sector. At the same time, the data processing was analyzed through Pearson's correlations and Kendall's correlation coefficient. The result of the analysis shows that the organizational variables with the greatest impact on the profitability of the Cluster is the capacity for innovation and an entrepreneurial culture. Likewise, considering the fundamental factors that favor the development of the leather-footwear Cluster, they are quality and trust for the exchange of information and the viability of constituting the Cluster, according to the affirmation of the respondents, who are Managers of 8 producing companies, 1 design producer and only one company as a supplier of inputs. In third place, the results of the multivariate statistical analysis show that, in regard to the development of the business Cluster model in leather-footwear, it is necessary to encourage the cooperation between the Institutions and finances, the confidence of the workers in the production processes and the technological resources with the administrative processes and of operations.

The use of quantitative techniques for the classification of population segments is a critical phase to evaluate their conditions. This information will serve as input for planning strategies to alleviate poverty. In this article, we present a model of vector support machines. Consequently, a sample of families residing in Cartagena de Indias is segmented, based on certain economic and sociodemographic variables. Analytical results confirm that most important factors are employment status, accessibility to public services and familiar income. In addition, it is corroborated that neighborhood conditions and monetary transfers have a low discriminatory power.