Abstract: We develop a framework for studying unemployment in a production network by incorporating sector-specific search and matching frictions. We derive aggregation formulae for how output and unemployment respond to sector-specific productivity and labor supply shocks. Specifically, output aggregation can be decomposed into two channels, the input-output channel, which can be expressed in terms of sales shares, and the network-adjusted search-and-matching channel, which depends on the interaction between labor market frictions and an occupational-labor-share weighted Leontief inverse. We show that the foundational theorem of Hulten (1978) is a special case of the network we study, when wages respond to exactly offset changes in the network-adjusted marginal productivity of labor. Additionally, the wage assumption is essential for determining whether matching frictions amplify or dampen the impact of shocks. For example, when wages rise by less than the network-adjusted marginal product of labor in response to a positive technology shock, firms increase their hiring, amplifying the positive effect on output, and vice versa. We calibrate our model to the US data and show that search and matching is both quantitatively and qualitatively important for the propagation of technology and labor force shocks.
Abstract: This paper develops a replicable and scalable method for analyzing tone in economics seminars to study the relationship between speaker gender, age, and tone in both static and dynamic settings. We train a deep convolutional neural network on public audio data from the computer science literature to impute labels for gender, age, and multiple tones, like happy, neutral, angry, and fearful. We apply our trained algorithm to a topically representative sample of presentations from the 2022 NBER Summer Institute. Overall, our results highlight systematic differences in presentation dynamics by gender, field, and format. We find that female economists are more likely to speak in a positive tone and are 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. Despite finding that female and male presenters receive a similar number of interruptions and questions, we find slightly longer interruptions for female presenters. Our trained algorithm can be applied to other economics presentation recordings for continued analysis of seminar dynamics.
Abstract: Ethical certification labels are increasingly popular as people become more conscious of global trade inequality. However, verifying the truthfulness of these certification labels is difficult in a deep global supply chain, and false certification exists. I propose a two-seller, one-buyer duopoly signaling game to study how prosocial preferences and information barriers contribute to false certification in the market for ethically sourced and labelled products. My results show that buyer-side prosocial preferences, such as warm glow and altruism, make room for false certification. When the buyer is driven by warm glow, false certification occurs regardless of information availability. When the buyer has no information on the seller’s prosocial type, sellers who certify truthfully are at a competitive disadvantage and the market is flooded with falsely certified products. Also, when buyers are more likely to believe in products that exhibit high certification effort, such as fancier packaging and more elaborate websites, sellers try to out-certify each other in order to out-compete each other, and certification becomes even more costly.