Sociologists have examined gender inequalities across a wide array of social contexts. Yet, questions remain regarding how inequalities arise among autonomous groups pursuing economic goals. In this article, we investigate mixed-sex entrepreneurial teams to unpack the mechanisms by which gender inequality in leadership emerges, despite strong pressures toward merit-based organizing principles. We theorize the potentially competing relationships between merit and gender and explore the contingencies moderating their effects. Drawing on a unique, nationally representative dataset of entrepreneurial teams sampled from the U.S. population in 2005, we use conditional logistic regression to test our hypotheses. We demonstrate that merit’s effect becomes much larger when multiple merit-based criteria provide consistent predictions for which team member is superior to others, and when entrepreneurial founders adopt bureaucratic templates to construct new ventures. However, gender stereotypes of leaders pervasively constrain women’s access to power positions, and gender’s effect intensifies when spousal relationships are involved. Women have reduced chances to be in charge if they co-found new businesses with their husbands, and some family conditions further modify women’s chances, such as husbands’ employment and the presence of children.

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Drawing on role congruity theory, we examine whether and when women-led ventures are more likely to fail than men-led ventures. We investigate the relationship between the gender of the leading entrepreneur and business failure and three important moderators of this relationship: whether the leadership assignment is consistent with merit, whether the venture operates in a female-dominated industry, and whether the venture is operated by a spousal team. Drawing on a unique, nationally representative data set of entrepreneurial firms sampled from the U.S. population in 2005 and followed yearly until 2011, we demonstrate that female entrepreneurs’ businesses are more likely to fail than those of their male counterparts. Regarding the moderating effects, our results show that female entrepreneurs’ businesses are more likely to fail when their merit-based competence is inferior to that of their cofounders. However, in the same scenario, male entrepreneurs are still able to lead their businesses successfully. We also find that women entrepreneurs’ disadvantages in leading new businesses are amplified in contexts that many have expected to be supportive of women, including in industries dominated by women and within spousal teams. Together, our results suggest that women’s disadvantages in leading their businesses may be perpetuated by gender beliefs that discount women’s leadership. Based on our findings, we discuss our contributions to theory and practice, and we offer implications for future research.

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Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. To evaluate the new regression model, we train and test neural networks with different depths and widths on simulated data, and we find the optimal parameters. We perform multiple numerical tests of the optimal regression model on multiple simulated data, and the results show that the new regression model behaves well on simulated data. Comparisons are also made between the optimal residual regression and other linear as well as nonlinear approximation techniques, such as lasso regression, decision tree, and support vector machine. The optimal residual regression model has better approximation capacity compared to the other models. Finally, the residual regression is applied into the prediction of a relative humidity series in the real world. Our study indicates that the residual regression model is stable and applicable in practice.