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. In combination, these two realistic features of any modern economy lead to large and pervasive unemployment responses across sectors and occupations. In addition, our model predicts larger output responses relative to an efficient production network. We demonstrate the empirical significance of our novel propagation mechanism by calibrating our model to the U.S. economy. A 1% productivity shock to the durable manufacturing sector results in a 0.41% increase in real GDP, and a 0.22pp decrease in unemployment. In contrast, in an efficient production network model, the same shock results in a 0.26% increase in GDP and no change in unemployment.
Abstract: This paper studies gender and tone in economics presentations using a replicable, scalable, machine-learning approach. We train a deep convolutional neural network to impute labels for gender, age, and tone-of-voice. We apply this to recorded presentations from the 2022 NBER Summer Institute to measure tone at a high-frequency level. We find that female economists are more likely to speak in a positive tone and less likely to be spoken to in a positive tone, even by other women. We find that male economists are significantly more likely to sound angry or stern compared to female economists.