Abstract: In this paper, I develop a tractable model of corporate bankruptcy law to examine its ef fects on firm default rates, borrowing costs, production, and to determine how to design it optimally. The model highlights the heterogeneous impacts of increasing creditor protec tion during the liquidation of insolvent firms and the efficiency-equity trade-off involved in bankruptcy design. I estimate the model using firm-level data from the Brazilian economy in order to evaluate the impact of the 2005 bankruptcy reform and to compare its outcomes with those of an optimal policy benchmark. I find that the reform increased the lender’s share of the liquidation value of insolvent companies from 1.5% to 63%, while reducing the fraction of assets lost during the proceedings from 80% to 54%- ultimately contributing to lower lending spreads, which are historically high in Brazil. However, the reform is far from optimal. I solve for an optimal lender share of 12%, with a consumption gain of 0.87%, compared to the post-reform economy. Finally, I provide two guidelines to implement the optimal outcome, including a variation of the current Brazilian law .
"Labor, Despite AI", joint with Kyle Herkenhoff, Dimitris Papanikolaou, Jonathan Rothbaum, Lawrence Schmidt, Bryan Seegmiller (draft available upon request).
Abstract: The hypothesis of this paper is that AI models must be trained on data generated by workers (both in- and out-of-house), and that this complementarity between labor and data fundamentally alters AI's effects on the labor market. We develop this hypothesis in three steps. We link the Annual Business Survey (ABS) and Business Trends and Outlook Survey (BTOS) to individual tax records and document (1) the importance of in-house R&D for AI adoption, (2) the importance of data and talent for AI adoption, and (3) the heterogeneous effects of AI adoption on wage outcomes. We find that exposed workers gain relative to non-exposed workers on average, particularly so in occupations where firms conduct significant in-house R&D on AI (i.e., where AI adoption is not ``out-of-the-box''). Second, we build a theory of AI adoption as an experience good (i.e., AI combines external and internal data on worker tasks) and we integrate this learning process into a directed search model with skill-weights. We allow firms to adapt their skill-weights, endogenously giving rise to new work, and we allow workers to retrain. We then estimate the model and compute labor transition paths as AI improves over the next 20 years. A 90% reduction in adoption costs, a 9-fold increase in exposure, and a 90% reduction in data obsolescence fail to generate widespread displacement. Workers retrain and remain complementary with data, bounding unemployment below 6.5% in the long-run. Lastly, an AI insurance fund for fully automated workers facilitates retraining and yields significant welfare gains.