About

Kirill Borusyak is the Edwin C. Voorhees Endowed Assistant Professor at UC Berkeley's Agricultural and Resource Economics department, with an affiliation at the Economics department. His research covers topics in international trade and applied econometrics. He holds a Ph.D. in Economics from Harvard University, obtained in 2018 under the supervision of Pol Antrás. Additionally, he earned an M.A. in Economics from the New Economic School in 2012, graduating Summa Cum Laude, and a Diploma in Economics and Mathematics from the Financial University in Moscow in 2009 with Highest Honors. Borusyak is also an Associate Editor at the Journal of European Economic Association and is affiliated with CEPR, CeMMAP, CReAM, IFS, UCL's Stone Centre for Inequality, and the CEP Trade Programme.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Econometrics
• Statistics
• Economics

Selected publications

• Nonparametric Identification of Demand without Exogenous Product Characteristics

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• A Practical Guide to Shift-Share Instruments

  The Journal of Economic Perspectives · 2025-02-01 · 101 citations

  articleOpen access1st authorCorresponding

  A recent econometric literature shows two distinct paths for identification with shift-share instruments, leveraging either many exogenous shifts or exogenous shares. We present the core logic of both paths and practical takeaways via simple checklists. A variety of empirical settings illustrate key points.

  PublisherOA PDFDOI

• Estimating Demand with Recentered Instruments

  ArXiv.org · 2025-04-05

  preprintOpen access1st authorCorresponding

  We develop a new approach to estimating flexible demand models with exogenous supply-side shocks. Our approach avoids conventional assumptions of exogenous product characteristics, putting no restrictions on product entry, despite using instrumental variables that incorporate characteristic variation. The proposed instruments are model-predicted responses of endogenous variables to the exogenous shocks, recentered to avoid bias from endogenous characteristics. We illustrate the approach in a series of Monte Carlo simulations.

  PublisherOA PDFDOI

• Design-based identification with formula instruments: a review

  Econometrics Journal · 2024-01-27 · 13 citations

  reviewOpen access1st authorCorresponding

  Summary Many studies in economics use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. We compare this design-based approach with conventional estimation strategies based on conditional unconfoundedness, and contrast it with alternative strategies that leverage a model for unobservables.

  PublisherOA PDFDOI

• Negative Weights are No Concern in Design-Based Specifications

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Replication package for: "Revisiting Event Study Designs: Robust and Efficient Estimation"

  Zenodo (CERN European Organization for Nuclear Research) · 2024-10-24

  datasetOpen access1st authorCorresponding

  This replication package contains the code and instructions necessary to replicate Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. "Revisiting event study designs: Robust and efficient estimation." Forthcoming Review of Economic Studies (2023).

  PublisherDOI

• Replication package for: "Revisiting Event Study Designs: Robust and Efficient Estimation"

  London School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2024-10-24

  datasetOpen access1st authorCorresponding

  This replication package contains the code and instructions necessary to replicate Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. "Revisiting event study designs: Robust and efficient estimation." Forthcoming Review of Economic Studies (2023).

  PublisherOA PDFDOI

• Negative Weights are No Concern in Design-Based Specifications

  National Bureau of Economic Research · 2024-01-01 · 4 citations

  reportOpen access1st authorCorresponding

  Recent work shows that popular partially-linear regression specifications can put negative weights on some treatment effects, potentially producing incorrectly-signed estimands.We counter by showing that negative weights are no problem in design-based specifications, in which low-dimensional controls span the conditional expectation of the treatment.Specifically, the estimands of such specifications are convex averages of causal effects with "ex-ante" weights that average the potentially negative "ex-post" weights across possible treatment realizations.This result extends to design-based instrumental variable estimands under a first-stage monotonicity condition, and applies to "formula" treatments and instruments such as shift-share instruments.

  PublisherDOI

• A Practical Guide to Shift-Share Instruments

  National Bureau of Economic Research · 2024-12-01 · 15 citations

  reportOpen access1st authorCorresponding

  A recent econometric literature shows two distinct paths for identification with shift-share instruments, leveraging either many exogenous shifts or exogenous shares. We present the core logic of both paths and practical takeaways via simple checklists. A variety of empirical settings illustrate key points.

  PublisherDOI

• Negative Weights Are No Concern in Design-Based Specifications

  AEA Papers and Proceedings · 2024-05-01 · 3 citations

  article1st authorCorresponding

  Recent work shows that popular partially-linear regression specifications can put negative weights on some treatment effects, potentially producing incorrectly-signed estimands. We show this is not an issue in design-based specifications, in which low-dimensional controls span the conditional expectation of the treatment. Specifically, the estimands of such specifications are convex averages of causal effects with ex-ante weights that average the potentially negative ex-post weights across possible treatment realizations. This result extends to design-based instrumental variable estimands under a first-stage monotonicity condition and applies to formula treatments and instruments such as shift-share instruments.

  PublisherDOI

Frequent coauthors

• Peter Hull

  John Brown University

  66 shared

• Xavier Jaravel

  Laser Scan Engineering (United Kingdom)

  55 shared

• Jann Spiess

  8 shared

• Clara von Bismarck-Osten

  University College London

  4 shared

• Uta Schönberg

  IZA - Institute of Labor Economics

  4 shared

• Brown Xavier

  University College Lahore

  4 shared

• Jaravel Lse

  Institute for Fiscal Studies

  4 shared

• Rafael Dix-Carneiro

  Duke University

  1 shared