Revisiting the Determinants of Entrepreneurship: A Bayesian Approach

Entrepreneurship has long been seen as an important instrument in stimulating and generating economic growth. The amount of research trying to identify key factors that drive entrepreneurship is considerable; yet, little consensus has been achieved. We argue that this lack of consensus could be on account of model uncertainty as empirical studies often tend to be selective on what variables are included in the final model. Drawing on recent literature, we demonstrate the benefits of Bayesian model averaging (BMA) in reducing the impact of model uncertainty on empirical research in entrepreneurship. Additionally, BMA provides measures of variable importance and can be seen as a complementary approach to dominance/relative importance analysis. We show that when model uncertainty is corrected for, gross domestic product per capita, unemployment, the marginal tax rate, and the volatility of inflation are the only macro variables significantly and universally associated with aggregate entrepreneurship. Furthermore, the emphasis on inflation and taxation suggests that governments have the power to influence the quantity and distribution of entrepreneurial activity by setting incentives that are not entrepreneurship specific but overlap significantly with general and fundamental principles of economic stability.