Trade Credit in a Developing Country: The Role of Large Suppliers in the Production Network - with Pierluca Pannella and Leonardo Alencar
Trade credit can be a substitute for bank credit when firms have limited access to financial institutions. This is particularly relevant in developing countries where bank interest rates are highly dispersed and smaller firms face a prohibitive cost of bank credit. This paper builds a production network model where each pair of sellers and buyers choose intermediate inputs’ quantities, prices, and levels of trade credit in a decentralized fashion, given heterogeneous bank interest rates. The dispersion in interest rates is explained by both heterogeneous risk of firms’ default and additional heterogeneous costs, labeled as ‘frictions.’ In equilibrium, suppliers paying low bank interest rates are net providers of trade credit to clients paying high interest rates when this spread is due to frictions. We calibrate the model using balance sheet data, firm-to-firm transaction data, and bank-to-firm credit data for the Brazilian economy. We decompose the observed interest rates between the risk and the frictional components. Trade credit attenuates shocks to financial frictions hitting downstream firms, while it amplifies these shocks when they hit upstream companies. Trade credit also amplifies interest rate shocks due to a higher risk of default. We also use our model to evaluate the importance of trade credit for aggregate output, given the evolution of firm-level bank interest rates in the 2019-2023 period. Trade credit reduced the output loss due to frictions by 44% in 2019. However, the importance of trade credit diminished between 2019 and 2023, together with the reduction in the dispersion of interest rates.
Exposure to Artificial Intelligence and Occupational Mobility: a Cross-Country Analysis - with Carlo Pizzinelli, Marina Mendes Tavares and Emma Rockall (IMF Working Paper 2024/116)
Labor Market Exposure to AI: Cross-country Differences and Distributional Implications - with Augustus Panton, Carlo Pizzinelli, Marina Mendes Tavares, and Longji Li (earlier version as IMF Working Paper 2023/216)
Gen-AI: Artificial Intelligence and the Future of Work - with Florence Jaumotte, Longji Li, Giovanni Melina, Augustus Panton, Carlo Pizzinelli, Emma Rockall and Marina Mendes Tavares (IMF Staff Discussion Note 2024/001)
Green Jobs and the Future of Work for Women and Men - with Naomi-Rose Alexander, Stefania Fabrizio, Florence Jaumotte, Longji Li, Jorge Mondragon, Sahar Priano and Marina Mendes Tavares (IMF Staff Discussion Note 2024/003)