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This article tries to uncover the drivers of soccer players’ market value in the five major European soccer leagues taking into account model uncertainty (variable selection) in a framework with 35 billion potential models. For this purpose, we use a hedonic regression framework and implement Bayesian model averaging (BMA) through Markov chain Monte Carlo model composition (MC3). To deal with endogeneity issues, instrumental variable Bayesian model averaging (IVBMA) is implemented as well. We find very strong, and robust evidence, that the most important value drivers are player’s performance, participation in the national team (senior and under-21), age, and age squared.

This paper uses Bayesian model averaging to uncover the true determinants of export diversification among 36 potential factors, and thus 236 potential models. Using data from 2001 to 2010, our results reveal two strong predictors: Primary school enrollment (99.7% posterior inclusion probability in the true model) raises export diversification, whereas the share of natural resources in gross domestic product (98.6%) lowers diversification levels. The importance of basic education coverage offers policymakers an opening toward diversifying exports, at least in the long run. This result is robust to accounting for the endogeneity of income levels by applying an instrumental variable BMA method. (JEL C11, F1, F34, O1, O2, O11)

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Countries with diversified export baskets take advantage of various benefits, which are said to foster and stabilize economic growth directly and through indirect channels (e.g. reduced income volatility, positive externalities, spillover effects). This is especially important in the context of developing economies. However, identifying the true determinants of export diversification is difficult as there exists no comprehensive theoretical or empirical framework to capture all potential factors in their entirety. This paper uses Bayesian Model Averaging to uncover the true long-term roots of export diversification among 43 potential determinants, and thus 2 43 potential models. Our results suggest that only four factors are important in predicting export diversification levels over the long run: natural resource rents as a percentage of GDP (100 % posterior inclusion probability), primary school enrollment rates (96 %), population size (25 %), and foreign direct investment levels (17 %). Many prominent candidates turn out to be insignificant in determining diversification levels. Neither policy-related variables (e.g. tariffs, freedom from trade regulations or democracy) nor macroeconomic factors (such as trade openness, terms of trade or domestic investment levels) nor geographical remoteness (whether the country is an island or landlocked) play a role. Various robustness checks confirm our results.JEL Classification: C11, F1, O11

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El objetivo de este artículo es identificar determinantes que llevan a los individuos a la toma de la decisión de ser empresarios en Medellín Área Metropolitana en 2009. Mediante modelos logit y probit multinomial, y logit binario secuencial, se estima la probabilidad de que una persona tome la decisión ocupacional de ser empresario, independiente formal, empleado o independiente informal, siendo el primer estado aquel que genera mayor efecto en el desarrollo de la región. Los resultados permiten observar que la educación, el uso de tecnologías de información y telecomunicaciones, y el capital financiero son las variables que más contribuyen en la probabilidad de ser empresario. Estos resultados sugieren nuevos elementos a la discusión de política pública relacionada con el emprendimiento y fortalecimiento empresariaThe main objective of this paper is to identify key factors that determine the decision of an individual to become an entrepreneur in the Medellin Metropolitan Area in 2009. Using models such as Multinomial Logit and Probit, as well as Sequential Binary Logit, we assess the probability that a person makes an occupational decision to be an entrepreneur, independent formal, informal or employee, the first state being the one that has the greatest effect on regional development. The results show us that education, use of information technologies and telecommunications, and financial capital are the variables that contribute the most to the probability of being an entrepreneur. These results may add new elements to the public policy discussion related to entrepreneurship and company growth

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