Abstract: Innovation extends beyond patents and R&D. I propose a holistic measure of innovation using text data from startup websites and public company 10-K filings. I designate successful young startups as the innovation benchmark and measure how much public companies' business descriptions evolve toward the benchmark over time. My measure captures firms' adoption of emerging technologies, pivots into new markets, and imitation of successful entrants. It correlates with R&D and patent-based innovation proxies and is available for firms that do not engage in patenting or R&D. Applying this measure, I find that younger firms, those in less concentrated markets, struggling firms, and more liquid firms are more likely to innovate. I also find that innovation improves future financial performance: a one standard deviation increase in innovation raises average excess returns by 2 percentage points and earnings normalized by sales by 0.4 percentage points over the next five years.
Abstract: We model a production network economy with sectoral and occupational unemployment by incorporating matching between job seekers across various occupations and employers in different production sectors. We derive expressions that unpack how the impact of microeconomic shocks on output and unemployment depends on the interaction between the network linkages, search costs, and changes in labor market tightness. When labor markets are slack, our model predicts larger output and employment responses because the network-adjusted labor productivity gain outweighs search costs. Calibrating our model to the U.S. economy, we demonstrate that our model significantly amplifies the response of aggregate output and unemployment to productivity shocks in any sector and changes the relative importance of sectors to aggregate output and unemployment. Our model nearly doubles the output response compared to an efficient production network and triples the unemployment response compared to a multi-sector search model following a productivity shock to durable manufacturing.
Abstract: We assess whether men and women are treated differently when presenting their research in economics seminars. We collected data on every interaction between presenters and audience members across thousands of seminars, job market talks and conference presentations, leveraging both human judgment and audio processing algorithms to measure the number, tenor, tone and type of interruptions. Within a seminar series, women are interrupted more than men, and this finding holds when controlling for characteristics of the presenter and their paper topic and for audience size. Interruptions that may not be favorable to the presenter, such as those that are negative in tenor or tone, or cutoff the presenter mid-sentence, are common occurrences in economics seminars, and increase for women presenters. We also find greater engagement with female presenters in the form of larger, more diverse audiences, suggesting a potential role model effect.
Abstract: This paper measures seminar dynamics using a replicable, scalable, machine‑learning approach and finds a gender‑tone gap in economics presentations. We train a deep convolutional neural network to impute labels for gender and tone‑of‑voice. We apply this to recorded presentations from the 2022 NBER Summer Institute to measure tone at a high frequency, which allows us to provide novel results on how economists interact with each other in talks. We find that female economists are less likely to be spoken to in a positive tone and more likely to be addressed with a serious and stern tone. Female economists are also more likely to speak in a positive tone. Overall, we show that gender differences in economics presentations exist across fields and presentation formats.