Knowledge that Transforms

Using multiple administrative data sources from Norway, we examine how firm performance changes after entrepreneurs become parents.Female-owned businesses experience a substantial decline in profits, steadily decreasing to 30% below baseline ten years post-childbirth.In contrast, male-owned businesses show no decline, often growing in revenues and costs after childbirth.The profit decline for female-owned firms is most pronounced among highly capable entrepreneurs, women who are majority owners, and those with working spouses.Entrepreneurial effort is key to performance, and our findings suggest that time demands from childbirth and childcare are a significant determinant of the decline in firm profits.

Firms are starting to replace humans with algorithms in important screening decisions, but there are potential spillovers of human biases contained in datasets to subsequent algorithmic predictions. When these biases are motivated by human prejudices, there are risks of algorithms perpetuating discrimination. I prove that when datasets are generated by a sufficiently discriminatory human, firms are more profitable when training discriminatory algorithms. If instead enough affirmative action is instituted in favor of a disadvantaged group, firms are more profitable when training algorithms that inflate scores for this group, but this effect diminishes with excess affirmative action.

This paper analyzes the effects of firms posting multiple but varying numbers of vacancies, hence differing in their market power, in professional labor markets. I find that strategic wage posting does, in general, not result in an efficient assignment of workers to firms. This is because firms with a larger number of vacancies pay on average lower wages than their competitors due to a lack of within-firm rivalry. If highly productive firms hire more, the resulting welfare loss due to mismatch may be substantial. Moreover, I provide a potential explanation why firms post uniform wages, missing out on more skilled workers.