Monetary Policy and Central Bank Design

Short and Variable Lags
with Gergely Buda, Vasco Carvalho, Giancarlo Corsetti, Joao Duarte, Afonso Moura, Alvaro Ortiz, Tomasa Rodrigo, Sevi Rodriguez Mora, and Guilherme Alves da Silva
Working paper

Using novel daily measures of consumption, firm sales, and employment, we show that the economy reacts within days to monetary policy shocks. Time aggregation of the measures to quarterly frequency masks these short-run dynamics.

Paper VoxEU Column
Policymakers' Uncertainty
with Anna Cieslak, Michael McMahon, and Song Xiao
Working paper

We use the structure of FOMC meetings to measure the uncertainty policymakers face about different macroeconomic conditions, and the associated impact on policy. Uncertainty amplifies the reaction function coefficients, especially for inflation.

The Long-Run Information Effect of Central Bank Communication
with Michael McMahon and Matthew Tong
Journal of Monetary Economics (2019) 108 (December): 185-202.

When central banks communicate signals on uncertainty in economic conditions, they can have their largest impact on long-run yields. This novel form of information effect explains the market reaction to the Bank of England's Inflation Report very well.

Transparency and Deliberation within the FOMC: a Computational Linguistics Approach
with Michael McMahon and Andrea Prat
Quarterly Journal of Economics (2018) 133 (2): 801-870

Using communication measures from machine learning, we find evidence for both the conformity and discipline effects predicted by the career concerns literature following an increase in transparency on the Federal Open Market Committee. On balance, the discipline effect appears stronger, as rookie members become more influential.

First Impressions Matter: Signalling as a Source of Policy Dynamics
with Michael McMahon
The Review of Economic Studies (2016) 83 (4): 1645-1672

A new model of reputation for monetary policy makers predicts that all preference types become softer on inflation over time and that this evolution is more pronounced for types that put more weight on output, predictions we confirm using voting data from the Bank of England.

Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication
with Michael McMahon
Journal of International Economics (2016), 38th NBER International Seminar on Macroeconomics: S114-S133.

We adopt an automated approach to measuring the extent to which Fed statements provide forward guidance versus information on economic conditions. Overall, the former has larger macroeconomic effects, particularly on market variables.

Preferences or Private Assessments on a Monetary Policy Committee?
with Michael McMahon and Carlos Velasco
Journal of Monetary Economics (2014) 67 (October): 16-32.

Experts on the Bank of England's Monetary Policy Committee differ both in terms of preferences and private forecasts. A committee of internal Bank members significantly outperforms an individual because of information pooling, but both large committees and those that add externals appear to add little value.


Organizational Economics and Firm Dynamics

Remote Work across Jobs, Companies, and Space
with Peter Lambert, Nick Bloom, Steven Davis, Raffaella Sadun, and Bledi Taska
Working paper

We use a large language model trained on tens of thousands of human labels to measure remote and hybrid work adoption with unprecedented granularity. We document large heterogeneity in adoption across narrow occupation categories, cities, and firms.

Paper VoxEU Column Website
The Demand for Executive Skills
with Tejas Ramdas, Raffaella Sadun, and Joseph Fuller
Working paper

We measure the demand for executive skills aross firms using a unique, large-scale corpus of job specifications. The importance of social skills is growing over time and is related to firm size, firm scope, and the information intensity of worker skills.

Paper HBR Article
Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19
with Steven Davis and Cristhian Seminario-Amez
Working paper

We combine elements of supervised machine learning and dictionary methods to uncover narrow, interpretable risk exposures from 10-K filings that account for firm-level reactions to aggregate shocks. We apply this idea to study equity returns during the COVID-19 pandemic.

Paper VoxEU Column
CEO Behavior and Firm Performance
with Oriana Bandiera, Andrea Prat, and Raffaella Sadun
Journal of Political Economy (2020) 128 (4): 1325-1369.

We use a machine learning algorithm applied to granular CEO survey data to construct a scalar behavioral index. The index is strongly correlated with firm performance, and this relationship appears only several years after CEO appointment. Evidence suggests the correlation is due to assignment frictions, which are substantially worse in low-income countries.

Paper HBR Column
Vertical Exclusion with Downstream Risk Aversion or Limited Liability
with Massimo Motta
Journal of Industrial Economics (2019) 67 (3-4): 409-447.

When downstream firms are sufficiently risk averse, or subject to limited liability, and cannot observe their competitors' shocks, an upstream firm offers contracts that offer all input to one downstream firm.

Organizing Public Good Provision: Lessons from Managerial Accounting
with Benito Arruñada
International Review of Law and Economics, (2015) 42 (June): 185-191

We describe how one can interpret public sector organizations through the lens of managerial accounting, and provide anecdotal evidence that mixing strong incentives with bureaucratic administration works well in various settings.

Performance Feedback with Career Concerns
Journal of Law, Economics, and Organization (2013) 29 (6): 1279-1316

When workers have career concerns, performance feedback increases the uncertainty of future effort but allows them to manipulate their employers' beliefs on future effort; the optimal policy only reveals intermediate performance levels.


Consumption in High Definition

National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption
with Gergely Buda, Vasco Carvalho, Alvaro Ortiz, Tomasa Rodrigo, and Sevi Rodriguez Mora
Working paper

We use comprehensive financial transactions from one of Europe's largest banks to build a large-scale consumption survey using national accounting principles. We use this to build aggregate and distributional accounts for consumption, as well as a detailed individual consumption panel.

Tracking the COVID-19 Crisis with High-Resolution Transaction Data
with Vasco Carvalho, Juan Garcia, Alvaro Ortiz, Tomasa Rodrigo, Jose V Rodriguez Mora, and Jose Ruiz
Royal Society Open Science (2021) 8: 210218

We use a database of 1.4 billion payments transactions to track the impact of the COVID-19 pandemic and policy response across time, space, and sectors in Spain.

Paper VoxEU Column

Econometrics and Statistics

Inference for Regression with Variables Generated from Unstructured Data
with Laura Battaglia, Tim Christensen, and Szymon Sacher
Working paper

Plugging measures derived from unstructured data into regression models leads to biased inference. Our proposed solution is to jointly model informational retrieval and regression, which modern computational methods make possible.

Graphical Model Inference With External Network Data
with Jack Jewson, Li Li, Laura Battaglia, David Rossell, and Piotr Zweirnik
Working paper

We show how observed connections among variables in multiple networks can improve estimation of the precision matrix by targeting regularization and develop a Bayesian model for inferring pairwise correlations.

Paper GitHub Repository
Estimating Bayesian Decision Problems with Heterogeneous Expertise
with Michael McMahon and Tang Srisuma
Journal of Applied Econometrics (2016) 31 (4): 762-771

Econometricians can use variation in the prior distribution to substantially improve the accuracy of existing techniques for estimating decision-makers' preferences and private signal distributions.


Review Articles for Text Data and Machine Learning

Text Algorithms in Economics
with Elliott Ash
Annual Review of Economics (2023) 15: 659–88

Review of algorithms for text analysis of economics; introduction of four core measurement problems; discussion of future challenges for text-as-data in economics.

Paper GitHub Repository
Text Mining for Central Banks
with David Bholat, Pedro Santos, and Cheryl Schonhardt-Bailey
Center for Central Banking Studies Handbook No. 33, Bank of England
Machine Learning for Economics and Policy
in Economic Analysis of the Digital Revolution, FUNCAS Social and Economic Studies (5), editors Juan Jose Ganuza and Gerard Llobet