This article views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning-based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.

We show that subjective assessments of employee “potential” contribute to gender gaps in promotion and pay. Using data on 29,809 management-track employees from a large retail chain, we find that women receive substantially lower potential ratings despite receiving higher performance ratings. Differences in potential ratings account for approximately half of the gender promotion gap. Women’s lower potential ratings do not reflect accurate forecasts of future performance: Women subsequently outperform male colleagues, both on average and on the margin of promotion. We highlight two mechanisms driving the gender potential gap: strategic retention and stereotyping. (JEL J16, J31, J71, L81, M12, M51)

We provide evidence that risk aversion leads pharmaceutical firms to underinvest in radical innovation. We introduce a new measure of drug novelty based on chemical similarity and show that firms face a risk-reward trade-off: novel drug candidates are less likely to obtain FDA approval but are based on more valuable patents. Consistent with a simple model of costly external finance, we show that a positive shock to firms’ net worth leads firms to develop more novel drugs. This suggests that even large firms may behave as though they are risk averse, reducing their willingness to investment in potentially valuable radical innovation.

The best worker is not always the best candidate for manager. In these cases, do firms promote the best potential manager or the best worker in their current job? Using microdata on the performance of sales workers at 131 firms, we find evidence consistent with the Peter Principle, which proposes that firms prioritize current job performance in promotion decisions at the expense of other observable characteristics that better predict managerial performance. We estimate that the costs of promoting workers with lower managerial potential are high, suggesting either that firms are making inefficient promotion decisions or that the benefits of promotion-based incentives are great enough to justify the costs of managerial mismatch. We find that firms manage the costs of the Peter Principle by placing less weight on sales performance in promotion decisions when managerial roles entail greater responsibility and when frontline workers are incentivized by strong pay for performance.

Please do not cite or circulate without permission This paper examines whether and how firms should adopt job testing technologies. If hiring managers have other sources of information about a worker’s quality (e.g. interviews), then firms may want to allow managers to overrule test recommendations for candidates they believe show promise. Yet if firms are concerned that managers are biased or have misaligned objectives, it may be optimal to impose hiring rules even if this means ignoring potentially valuable soft information. We evaluate the staggered introduction of a job test across 130 locations of 15 firms employing service sector workers. We show that testing improves the match-quality of hired workers, as measured by their completed tenure, by about 14%. These gains largely come from managers who follow test recommendations, as opposed to those who frequently make exceptions to test recommendations. That is, when faced with similar applicant pools, managers who make more exceptions systematically end up with workers with lower tenure. In this setting, our results suggest that firms can improve productivity by taking advantage of the verifiability of test scores to limit managerial discretion. 1 1

We quantify the impact of scientific grant funding at the National Institutes of Health (NIH) on patenting by pharmaceutical and biotechnology firms. Our paper makes two contributions. First, we use newly constructed bibliometric data to develop a method for flexibly linking specific grant expenditures to private-sector innovations. Second, we take advantage of idiosyncratic rigidities in the rules governing NIH peer review to generate exogenous variation in funding across research areas. Our results show that NIH funding spurs the development of private-sector patents: a $10 million boost in NIH funding leads to a net increase of 2.3 patents. Though valuing patents is difficult, we report a range of estimates for the private value of these patents using different approaches.