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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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  • April 2025
  • Article

Crisis Interventions in Corporate Insolvency

By: Samuel Antill and Christopher Clayton
We model the optimal resolution of insolvent firms in general equilibrium. Collateral-constrained banks lend to (i) solvent firms to finance investments and (ii) distressed firms to avoid liquidation. Liquidations create negative fire-sale externalities. Liquidations... View Details
Keywords: Insolvent Firms; Government Intervention; Liquidation; Econometric Models; Insolvency and Bankruptcy; Policy
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  • Research Summary

Overview

Downstream businesses that utilize global suppliers frequently use auditing programs to monitor their suppliers’ working conditions and are often deployed to address reputational concerns associated with procuring from unregulated suppliers. Despite their widespread... View Details
Keywords: Audit Quality; Remote Work; Outsourcing; Econometric Analysis; Switching Costs; Service Operations; Performance Effectiveness; Retail Industry; Service Industry
  • 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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  • 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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  • 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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  • 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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  • Research Summary

Overview

By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
Keywords: Channel Management; Pricing; Pricing Policies; Online Marketing; E-commerce; Analytics; Econometrics; Field Experiments; Data Analytics; Artificial Intelligence; Value Of Data
  • 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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    Jeff Gortmaker

    I am a Ph.D. candidate in Business Economics on the 2024-2025 academic job market. My research focuses on industrial organization, with secondary interests in econometrics and finance.

    You can find my personal website View Details

    • 06 Apr 2017
    • News

    Harvard Business School Professor Julio Rotemberg Dies at 63

    • Teaching Interest

    Overview

    Paul has been a teaching assistant for two years in the 2nd-year graduate level econometrics course, working with Professors Guido Imbens and Alberto Abadie. Additionally, he has been a TA in the undergraduate course "Household Finance." View Details
    • 25 Oct 2018
    • HBS Seminar

    Fanyin Zheng, Columbia University

    • 19 Jun 2019
    • Working Paper Summaries

    Migrant Inventors and the Technological Advantage of Nations

    Keywords: by Dany Bahar, Prithwiraj Choudhury, and Hillel Rapoport
    • 01 Aug 2012
    • News

    ISO standards stamp approval

    • 20 Jan 2012
    • News

    LRQA talks to world’s leading management systems expert

      Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

      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,... View Details
      • Research Summary

      The Connection Between Volatility and Leverage

      By: Emil N. Siriwardane

      Professor Siriwardane has co-developed a new econometric model that captures the link between equity volatility and financial leverage, driven by the desire to incorporate the record levels of both leverage and volatility that characterized the 2008 financial crisis... View Details

      • 28 Mar 2019
      • HBS Seminar

      Gabriel Weintraub, Stanford University

      • Research Summary

      Research overview

      The growth of consumer review websites over the past decade has revolutionized the way in which consumers learn about product quality. The centrality of information to consumer welfare has also been underscored in public policy debates, where quality disclosure has... View Details