Saroj Bhattarai, Arpita Chatterjee, and Gautham UdupaWe estimate distributional implications of global food and oil price shocks by utilizing monthly panel data on consumption and income from India, and an IV strategy that removes variation coming from global demand shocks. While both shocks lead to stagflationary aggregate dynamics, they differ in terms of distributional consequences.
Martin Neil Baily, David M. Byrne, Aidan T. Kane, and Paul E. SotoWith the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question.
Cody KallenRising geopolitical tensions and supply chain vulnerabilities have driven recent fragmentation of foreign direct investment (FDI).
Mark Carlson, Zack Saravay, and Mary TianWe examine how primary dealers utilized repo operations conducted by the Federal Reserve from September 2019 until May 2020 and how usage affected dealer borrowing and lending.
Anushka Mitra and Aditi SinghThis paper examines which macroeconomic signals shape household expectations and finds that unemployment shocks play a more influential role than inflation shocks. Using daily data, we identify which announcements prompt households to revise their expectations. We construct two shock series—assuming households are either sophisticated or naive—based on the surprise components of announcements. Labor market news strongly influences both general economic sentiment and inflation expectations.
Charles Taragin and Marco TaylhardatWe propose a simple algorithm for defining merger-specific geographic antitrust markets based on merging firm proximity. Applying it to over a thousand hypothetical bank mergers, we compare concentration measures in our markets to those defined by the Federal Reserve, which are not merger-specific, finding broad agreement but also offering potential improvements upon current definitions.
Sinem Hacioglu-Hoke, Leo Feler, and Jack ChylakUsing zip-code median income as a proxy for household income is common in economics but can mask heterogeneity and yield misleading conclusions. Using zip-code median income and self-reported household incomes from a representative panel of 150,000 U.S. households, we decompose average retail spending for 2018-2024. When using self-reported incomes, we observe substantial divergence in spending between low- and high-income households starting in mid-2021.
Oleg SokolinskiyThis paper estimates trading costs in the off-the-run Treasury market using comprehensive transactions data and machine learning techniques. The analysis reveals several key findings that enhance the understanding of the off-the-run Treasury market liquidity. First, the indicative bid-ask spread is shown to be a biased measure of liquidity, even when not considering transaction volume.
Shantanu Banerjee, Paul Cordova, Michiel De Pooter, and Olesya V. GrishchenkoWe apply natural language processing tools to news articles in the financial press to construct a sentiment index—an index of the perceived semantic orientation of monetary policy communications around scheduled Federal Open Market Committee (FOMC) meetings. To that end, we develop several dictionaries that capture various monetary policy tools: conventional monetary policy, asset purchases, and forward guidance.
Eric EngstromAmid ongoing trade policy shifts and geopolitical uncertainty, concerns about stagflation have reemerged as a key macroeconomic risk. This paper develops a probabilistic framework to estimate the likelihood of stagflation versus soft landing scenarios over a four-quarter horizon. Building on Bekaert, Engstrom, and Ermolov (2025), the model integrates survey forecasts, structural shock decomposition, and a non-Gaussian BEGE-GARCH approach to capture time-varying volatility and skewness.
