About

Philipp Kircher is the Irving M. Ives Professor of Industrial and Labor Relations in the Department of Economics at Cornell University. His research primarily focuses on how firms and workers meet and stay together, examining topics such as how wages attract workers of different talent levels, the impact of this on firm productivity with various production technologies, and the effects of technological change and automation. Additionally, he explores the spread of infectious diseases through the lens of labor market meeting processes, analyzing how individuals protect themselves and how these behaviors influence policy effectiveness, with specific applications to HIV/AIDS and Covid-19. His work employs a combination of theoretical modeling, computational simulations, and statistical methods, and has been published in major economics journals. He has also served as managing editor and chairman of the Review of Economic Studies.

Research topics

• Artificial Intelligence
• Computer Science
• Algorithm
• Mathematical economics
• Microeconomics
• Economics

Selected publications

• Online Buddies for Job Seekers: A Field Experiment

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Advising Job Seekers in Occupations with Poor Prospects: A Field Experiment

  National Bureau of Economic Research · 2025-05-01 · 1 citations

  reportOpen access

  We study the impact of online information provision to unemployed job seekers who are looking for work in occupations in slack markets, i.e. with only few vacancies per job seeker.Job seekers received suggestions about suitable alternative occupations, and how the prospects of these alternatives compare to their current occupation of interest.Some additionally received a link to a motivational video.We evaluate the interventions using a randomized field experiment covering all eligible job seekers registered to search in the target occupations.The vast majority of treated job seekers open the message revealing the alternative suggestions.The motivational video is rarely watched.Effects on unemployed job seekers in structurally poor labor markets are large: their employment, hours of work and labor income all improve by 5% to 6% after 18 months.Additional survey evidence shows that treated job seekers find employment in more diverse occupations.

  PublisherDOI

• Leading Firms and the Future of Work

  Open Science Framework · 2025-01-01

  otherOpen access

  This study examines whether identifying ``leading firms" and postulating a catch-up process of other firms to leading firms improves the accuracy of forecasting occupational structures within industries, and ultimately for the whole economy. Accurately forecasting occupational changes is critical for determining the appropriate skills to teach workers of the future, effectively reskilling the unemployed, and enabling policymakers to navigate the evolving employment landscape and design targeted retraining programs. Despite the clear benefits of predicting future labor market trends, the literature has yet to establish a comprehensive method for forecasting labor market changes. Previous research has attempted to forecast occupational trends by studying specific mechanisms, such as \textit{robotization}, \textit{artificial intelligence}, or \textit{trade shocks}. However, a general approach independent of a specific mechanism has not yet been developed. The goal of this study is to fill this gap by introducing an algorithm to forecast the occupational structure of the labor market based solely on the identification of leading firms. We propose a method for identifying leading firms and a catch-up process for other firms to forecast occupational change. We define leading firms as those whose occupational structures are ahead of broader economic trends. Our main method for identifying leading firms uses a machine learning approach and a productivity-based approach. The study aims to address the following primary research question: does identifying leading firms enhance the accuracy of forecasting occupational structures within industries? Further, we aim at answering two additional related research questions. First, which firm characteristics most effectively predict a firm's role as a ``leader" in occupational change? Second, under what conditions (e.g., industry granularity, firm size, productivity metrics) is the predictive advantage of using leading firms most pronounced? The aim of this pre-registration is to outline our approach that has been developed using early parts of French administrative data. We aim to apply it to later parts of the French administrative data, and aim to outline the modelling choices we will make for this validation.

  PublisherDOI

• Do the Long-term Unemployed Benefit from Automated Occupational Advice during Online Job Search?

  The Economic Journal · 2025-05-29 · 1 citations

  article

  Abstract In a randomised field experiment, we provide suggestions about suitable occupations to long-term unemployed job seekers. The suggestions are automatically generated, integrated in an online job search platform and fed into actual search queries. Effects on ‘reaching a cumulative earnings threshold’ and ‘finding a stable job’ are positive, large and are more pronounced for those who are longer unemployed. Treated individuals include more occupations in their search and find more jobs in recommended occupations.

  PublisherDOI

• Job search online: overconfidence, information, and recommender systems

  AEA Randomized Controlled Trials · 2025-03-05

  dataset
  PublisherDOI

• Eliciting Time Preferences When Income and Consumption Vary: Theory, Validation, and Application to Job Search

  American Economic Journal Microeconomics · 2025-01-29

  articleOpen access

  We propose a simple method for eliciting individual time preferences without estimating utility functions even in settings where background consumption changes over time. It relies on eliciting preferences for receiving high stakes lottery tickets at different points in time. In a standard intertemporal choice model high rewards decouple lottery choices from variation in background consumption. We investigate robustness to other assumptions theoretically, and validate our elicitation method experimentally. We illustrate an application of our method with unemployed job seekers, which naturally have income/consumption variation. (JEL D12, D15, D91, G51, J22, J64)

  PublisherOA PDFDOI

• Advising Job Seekers in Occupations with Poor Prospects: A Field Experiment

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Advising Job Seekers in Occupations with Poor Prospects: A Field Experiment

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Job search online: overconfidence, information, and recommender systems

  AEA Randomized Controlled Trials · 2025-03-05

  dataset
  PublisherDOI

• Navigating behavioral biases in job search: overview and digital solutions

  Edward Elgar Publishing eBooks · 2024-12-10

  book-chapterOpen access

  This chapter reviews common behavioral biases in job search behavior and reviews recent literature that studies their consequences for job search effectiveness. We focus on time preferences (present bias), biased beliefs about labour market prospects, and psychological barriers such as discouragement. Next we describe how recent technological advances have opened up opportunities to overcome these biases and enhance job search effectiveness. Online platforms can offer low-cost and personalized support in the form of goal-setting assistance, improved information provision about institutional features, or tailored job search recommendations. We review the recent empirical evidence from field experiments with such tools and link the diverse results to a discussion of the challenges in designing effective recommendation systems. These include ensuring accurate information about current labour demand, the risk of increasing congestion, and offering recommendations to those sub-populations that benefit most.

  PublisherOA PDFDOI

Recent grants

• Sorting Effects in Competitive Search Markets

  NSF · $213k · 2008–2011

Frequent coauthors

• Michèle Tertilt

  University of Mannheim

  52 shared

• Manolis Galenianos

  Royal Holloway University of London

  38 shared

• Michèle Bélot

  36 shared

• John J. Horton

  National Bureau of Economic Research

  29 shared

• Ramesh Johari

  29 shared

• Jan Eeckhout

  Pompeu Fabra University

  27 shared

• Paul J. Müller

  25 shared

• Cezar Santos

  Inter-American Development Bank

  18 shared