Leonard Goff

I am a PhD student in the Economics department at Columbia University. I am on the 2020-2021 job market.

My primary research fields are applied econometrics and labor economics, and I also have interests in environmental and public economics.

You can reach me at leonard.goff@columbia.edu.

I have undergraduate degrees in physics and philosophy from the University of Maryland, and hold an MSc in physics and an MA in economics from the University of British Columbia.

Research

Job Market Paper

  • Treatment Effects in Bunching Designs: The Impact of the Federal Overtime Rule on Hours (Draft coming soon)
    • Abstract:The Fair Labor Standards Act (FLSA) mandates overtime premium pay for most U.S. workers, yet a lack of clean variation in the rule has made it difficult to assess its impacts on the hours they work. I use bunching observed at 40 hours in an administrative dataset of weekly paychecks to estimate this effect. To do so, I develop a general framework in which bunching at a kink point is informative about reduced form causal effects, nesting existing approaches and abstracting them from underlying structural models. Under a non-parametric shape constraint on the distribution of hours and flexible assumptions on choice, a local average treatment effect among bunchers is partially identified. The bounds are informative in the overtime context and suggest that affected workers in the U.S. work an average of at least half an hour less as a result of the FLSA mandate, in weeks that they do work at least 40 hours. Overtime policy may thus have positive employment effects, though a scale effect could dominate.

Working Papers

  • A Vector Monotonicity Assumption for Multiple Instruments
    • [Supplemental Material]
    • Abstract: When a researcher wishes to use multiple instrumental variables for a single binary treatment, the familiar LATE monotonicity assumption can become restrictive: it requires that all units share a common direction of response even when different instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply restricts treatment status to be monotonic in each instrument separately. This is a natural assumption in many contexts, capturing the intuitive notion of "no defiers" for each instrument. I show that in a setting with a binary treatment and multiple discrete instruments, a class of causal parameters is point identified under vector monotonicity, including the average treatment effect among units that are responsive to any particular subset of the instruments. I propose a simple "2SLS-like" estimator for the family of identified treatment effect parameters. An empirical application revisits the labor market returns to college education.

Work in Progress

  • How Pure-chance Matching Compares to a Labor Market: Norway's Shift away from Random Serial Dictatorship for Doctors Choosing First Jobs
  • Do Firms Fully Exploit Their Labor Market Power in Setting Wages? Evidence from Canada

Publications

Outside of economics:

Curriculum Vitae

Teaching Materials

Senior Honors Thesis Workshop (Fall 2019; Columbia ECON GU4999)

Code

Visit my Github

Some software projects:

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