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We analyze whether start-up rates in different industries systematically change with business cycle variables. Using a unique data set at the industry level, we mostly find correlations that are consistent with counter-cyclical influences of the business cycle on entries in both innovative and non-innovative industries. Entries into the large-scale industries, including the innovative part of manufacturing, are only influenced by changes in the cyclical component of unemployment, while entries into small-scale industries, like knowledge intensive services, are mostly influenced by changes in the cyclical component of GDP. Thus, our analysis suggests that favorable conditions in terms of high GDP might not be germane for start-ups. Given that both innovative and non-innovative businesses react counter-cyclically in ‘regular’ recessions, business formation may have a stabilizing effect on the economy.
The human personality predicts a wide range of activities and occupational choices-from musical sophistication to entrepreneurial careers. However, which method should be applied if information on personality traits is used for prediction and advice? In psychological research, group profiles are widely employed. In this contribution, we examine the performance of profiles using the example of career prediction and advice, involving a comparison of average trait scores of successful entrepreneurs with the traits of potential entrepreneurs. Based on a simple theoretical model estimated with GSOEP data and analyzed with Monte Carlo methods, we show, for the first time, that the choice of the comparison method matters substantially. We reveal that under certain conditions the performance of average profiles is inferior to the tossing of a coin. Alternative methods, such as directly estimating success probabilities, deliver better performance and are more robust.