Personality traits, subjective learning, and entrepreneurial decision making

We present a dynamic occupational choice model with a learning algorithm simultaneously capable to explain entrepreneurial entry, exit, and survival. According to our model, those individuals decide to become entrepreneurs who expect their productivity to be highest when managed by themselves. As we further assume that individuals have incomplete information about their own non-cognitive skills, which are relevant for entrepreneurial processes, entrepreneurial entry in our model is driven by overconfidence in the own skills---in line with earlier empirical findings. After entry, entrepreneurs receive noisy feedback from the market. Depending on a set of traits different from those driving the entry process into the market, entrepreneurs decide to either stay or leave the market. Our learning-based model generates survival rates decreasing at decreasing rates and captures findings on the earnings puzzle according to which median entrepreneurs do not earn more than median wage workers

Career choice under uncertainty

Contemporary theoretical literature on occupational choice consists mostly of models that treat choice outcomes as either deterministic or risky. This paper proposes taking a slightly more realistic perspective by constructing a general occupational choice model on the basis of the assumption that outcomes are partially uncertain such that some reward distributions are unknown. The change in perspective yields some major advantages: Learning and career trajectories, which in general cannot be generated by models with deterministic or risky rewards, become a natural feature of the dynamic solution of sequential occupational choice problems. Furthermore, earnings-puzzle-like observations can be explained by sufficiently high uncertainty aversion, as uncertainty aversion has a significant impact on learning. In addition, central model predictions are consistent with data on relative choice frequencies and enterprise death rates