This research focuses on the factors of survival and growth of new enterprises in Tunisia. Based on previous research, we hypothesize that three factors influence the survival and growth of these firms: factors related to the entrepreneur, factors related to organizational characteristics and characteristics of the environment from start-up. We test these assumptions on a sample of 60 companies. The results show that human capital and the experience of the entrepreneur have a relatively small impact on the survival of newly created firms. Similarly, the intensity of preparation for creation by accompanying structures is not generally a key factor for survival. On the other hand, organizational characteristics (the amount of capital invested at start-up or customer structure) are strongly linked to the survival of the latter.
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C1, C11.This paper deals with the issue of predicting customers' default payment. The Bayesian network credit model is applied for the prediction and classification of personal loans with regard to credit worthiness. Referring to credit experts and using K2 algorithm for learning structure, we set up the dependency conditional relations between variables that explain default payments. Then, the parametric learning is adopted to detect conditional probabilities of customers' default payment. The parameters are estimated on the basis of real personal loan data obtained from a Tunisian commercial bank. The Bayesian network analysis has revealed that customers' age, gender, type of credit, professional status, and monthly repayment burden and credit duration have an important predictive power for the detection of customers' default payment. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers through the use of a Bayesian Network model.
The main purpose of this study is to investigate the causal relationship among renewable energy, nuclear energy consumption, economic growth, and CO2 emissions for selected OECD countries over the period 1980 to 2013. All variables are found to be cointgrated. Results of Granger causality show long-run relationship from GDP, renewable energy consumption and nuclear energy consumption to CO2 emissions, from CO2 emissions, GDP, to renewable energy consumption, from emissions, GDP to renewable energy, and from CO2 emissions GDP and nuclear energy consumption. In short run, results show that there exists bidirectional causality between GDP and CO2 emissions, and unidirectional causality running from renewable energy consumption to GDP. Also unidirectional causality running from renewable energy consumption to CO2 emissions without feedback but no causality running from nuclear energy consumption to CO2 emissions was found. This evidence suggests that renewable energy can help to mitigate CO2 emissions, but so far, nuclear energy consumption has not reached a level where it can CO2 emissions.
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