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  • Article

How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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  • 2021
  • Working Paper

How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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  • 2021
  • Working Paper

Crisis Interventions in Corporate Insolvency
By: Samuel Antill and Christopher Clayton
We model the optimal resolution of insolvent firms in general equilibrium. Absent externalities, the optimal corporate-insolvency system encourages lending by letting banks assign liquidations ex-post. We show that a social planner optimally intervenes in such a system...  View Details
Keywords: Insolvent Firms; Government Intervention; Liquidation; Econometric Models; Insolvency and Bankruptcy; Governance; Policy
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  • 2021
  • Working Paper

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...  View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; Analysis; Theory; Measurement and Metrics; Performance Consistency
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  • January 2021
  • Article

A Model of Relative Thinking
By: Benjamin Bushong, Matthew Rabin and Joshua Schwartzstein
Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings,...  View Details
Keywords: Relative Thinking; Econometric Models; Behavior; Cognition and Thinking
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  • 2021
  • Working Paper

Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....  View Details
Keywords: Econometric Models; Business Cycles; Credit
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  • November 2020
  • Article

Taxation in Matching Markets
By: Arnaud Dupuy, Alfred Galichon, Sonia Jaffe and Scott Duke Kominers
We analyze the effects of taxation in two-sided matching markets, i.e., markets in which all agents have heterogeneous preferences over potential partners. In matching markets, taxes can generate inefficiency on the allocative margin by changing who is matched to whom,...  View Details
Keywords: Matching Markets; Labor Markets; Taxation; Labor; Markets
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  • Fall 2020
  • Article

Business Credit Programs in the Pandemic Era
By: Samuel G. Hanson, Jeremy C. Stein, Adi Sunderam and Eric Zwick
We develop a pair of models that speak to the goals and design of the sort of business-lending and corporate-bond purchase programs that have been introduced by governments in response to the ongoing COVID-19 pandemic. An overarching theme is that, in contrast to the...  View Details
Keywords: COVID-19; Business Lending; Government Intervention; Econometric Models; Health Pandemics; Credit; Governance; Policy
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  • August 2020 (Revised September 2020)
  • Technical Note

Assessing Prediction Accuracy of Machine Learning Models
By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...  View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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  • August 2020
  • Technical Note

Comparing Two Groups: Sampling and t-Testing
By: Iavor I Bojinov, Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih and Michael W. Toffel
This note describes sampling and t-tests, two fundamental statistical concepts.  View Details
Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
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  • 2020
  • Working Paper

Optimal Illiquidity
By: John Beshears, James J. Choi, Christopher Clayton, Christopher Harris, David Laibson and Brigitte C. Madrian
We calculate the socially optimal level of illiquidity in an economy populated by households with taste shocks and present bias (Amador, Werning, and Angeletos 2006). The government chooses mandatory contributions to respective spending/savings accounts, each with a...  View Details
Keywords: Illiquidity; Social Security; Econometric Models
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  • 2020
  • Article

A Practical Approach to Sales Compensation: What Do We Know Now? What Should We Know in the Future?
By: Doug J. Chung, Byungyeon Kim and Niladri B. Syam
Personal selling represents one of the most important elements in the marketing mix, and appropriate management of the sales force is vital to achieving the organization’s objectives. Among the various instruments of sales management, compensation plays a pivotal role...  View Details
Keywords: Sales Compensation; Sales Management; Sales Strategy; Principal-agent Theory; Structural Econometrics; Field Experiments; Machine Learning; Artificial Intelligence; Salesforce Management; Compensation and Benefits; Motivation and Incentives; AI and Machine Learning
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  • June 2020
  • Article

How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes...  View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
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  • 2020
  • Working Paper

A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is...  View Details
Keywords: Identification; Econometric Models; Data and Data Sets; Theory
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  • 2018
  • Chapter

Competing Interests
By: Joel Goh
Book Abstract: The editors, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field....  View Details
Keywords: Healthcare; Analytics; Health Care and Treatment; Research; Competition
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  • 2018
  • Working Paper

Detecting Anomalies: The Relevance and Power of Standard Asset Pricing Tests
By: Malcolm Baker, Patrick Luo and Ryan Taliaferro
The two standard approaches for identifying capital market anomalies are cross-sectional coefficient tests, in the spirit of Fama and MacBeth (1973), and time-series intercept tests, in the spirit of Jensen (1968). A new signal can pass the first test, which we label a...  View Details
Keywords: Investment Management; Anomalies; Portfolio Construction; Transaction Costs; Investment; Management; Asset Pricing; Market Transactions; Cost
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  • 2018
  • Working Paper

How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Many production processes are subject to inspection to ensure they meet quality, safety, and environmental standards imposed by companies and regulators. Inspection accuracy is critical to inspections being a useful input to assessing risks, allocating quality...  View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Performance Evaluation; Food and Beverage Industry; Service Industry
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  • Article

Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry
By: Antonio Moreno and Christian Terwiesch
We use a detailed data set from the U.S. auto industry spanning from 2002 to 2009 and a variety of econometric methods to characterize the relationship between the availability of production mix flexibility and firms’ use of responsive pricing. We find that production...  View Details
Keywords: Empirical Operations Management; Flexibility; Pricing; Automotive Industry; Production; Price; Management; Analysis; Auto Industry; United States
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  • 2020
  • Working Paper

Should Firms Move Talent from the Geographic Periphery to Hubs? A Strategic Human Capital Perspective
By: Prithwiraj Choudhury, Victoria Sevcenko and Tarun Khanna
A longstanding literature holds that firms should hire and move talent from the geographic periphery to hubs as a means to create value from human capital. They do so, however, at the risk of losing the worker to rivals located in the same geographic hub,...  View Details
Keywords: Geographic Location; Selection and Staffing; Employment; Residency; Technology Industry; India
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  • 2014
  • Chapter

Schumpeter's Plea: Historical Reasoning in Entrepreneurial Theory and Research
By: G. Jones and R. Daniel Wadhwani
This chapter draws on theories of entrepreneurship and history to explore the ways in which historical processes play an integral role in entrepreneurship. It builds off the plea by Joseph Schumpeter for an active exchange between historical approaches and theories of...  View Details
Keywords: Entrepreneurs; Business History; Entrepreneurship; History; Organizations
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