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Michael Sheldon

Economist × Data scientist

 

About

I am a graduate of the University of Chicago's Economics program, where I graduated with honors in March 2016. Currently I am a Data Scientist at Uber Technologies, where I research the economic behavior of drivers. With Keith Chen of UCLA, I am studying the reaction of driver supply to unanticipated short-term variation in earnings; our current working paper discusses behavior heuristics such as "income-targeting" and the role they play in the supply of labor. 

 

Email

msx@uchicago.edu

 

Linkedin

LinkedIn Profile

Résumé

Michael Sheldon

msx@uchicago.edu

EDUCATION

The University of Chicago

Bachelor of Arts in Economics with Honors, March 2016
GPA: 3.75/4.00

EMPLOYMENT EXPERIENCE

Data Scientist

Uber Technologies, San Francisco California
June 2014- September 2014; June 2015- September 2015; April 2016- Present                       

  • Conducted independent study characterizing and quantifying driver supply responsiveness to transitory income shocks, establishing a supply elasticity   
  • Publishing a joint paper with Keith Chen (UCLA) contesting results from a seminal economic study; Camerer et. al (1997)
  • Assisted in founding, developing, and supporting a new team focused on optimal spend for city growth in both new and established markets                                                                                                                       

Research Assistant to Devin Pope, Behavioral Economics

University of Chicago, Booth School of Business
February 2014-March 2016                                                                                                

  • Compiled, cleaned, and analyzed datasets for a variety of behavioral topics
  • Designed and managed a 10,000+ participant survey on Mechanical Turk (MTurk)
  • Assisted in developing ideas and testable hypotheses for new research areas                                                                                    

Project Development Assistant

Collective Decision Engines, Chicago Illinois
March 2014- June 2014

  • Involved with developing a commercial implementation of Quadratic Voting under Glen Weyl
  • Designed and executed regression testing for functionality of new software

COMMUNITY SERVICE & LEADERSHIP                                                       

Saturday University Tutor

Black Star Project, Chicago Illinois
April 2013- June 2013

  • Instructed group of at-risk students in both mathematics and reading
  • Coordinated assignments, evaluations, and curriculum with other instructors                                                                                

Chicago BounD

September 2013

  • Helped create partnerships between non-profit organizations and the University of Chicago
  • Engaged and consulted community members in addressing social issues such as LGBTQ+ homelessness, reemployment programs, and recidivism

SKILLS

Technically Proficient in: R, STATA, SQL, LaTeX  
Familiar in: Python, Java, Mathematica
Language: Proficient in German

Working Papers

Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform

Joint with Keith Chen

Last Edited: 12/11/2015

This paper studies how the dynamic pricing of tasks in the “gig” economy influences the supply of labor.  In this paper, we study how driver-partners on the Uber platform respond to the dynamic pricing of trips, known as “surge” pricing. In contrast to income-target findings, we find that Uber partners drive more at times when earnings are high, and flexibly adjust to drive more at high surge times. A discontinuity design confirms that these effects are causal, and that surge pricing significantly increases the supply of rides on the Uber system.

Income Targeting and the Ridesharing Market

Last Edited: 02/18/2016

This paper examines the supply elasticity of individual contractors in the ridesharing market. Contrary to the results of Camerer et. al (1997) and the "income targeting" hypothesis, this study demonstrates substantial evidence of positive labor supply elasticities. Furthermore, the paper discusses the nature of measurement error in labor markets and how its presence can severely bias results downward.