Marc-Antoine Schmidt, University of Toronto

Valuing Flexibility: A Model of Discretionary Rest Breaks
Friday, 11 January 2019 - 2:30 pm to 4:00 pm
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Contact information
Contact person: 
Catherine Deri Armstrong
Email: 
cdarmstrong@uottawa.ca
Phone: 
613-562-5800
Extension: 
1690
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Registration required: 
No
Cost to attend: 
Free of charge
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As flexible work arrangements become increasingly prevalent in the labor market, more and more workers have discretion over when they take rest breaks—a feature that is likely appealing to many. Yet we do not have a formal economic model of the decision to take breaks, nor do we know how much workers value this ‘breaks’ flexibility. To fill the gap, I develop and estimate the first dynamic model of daily labor supply that incorporates rest breaks. The model includes several factors that influence the decision to take breaks: fatigue, opportunity costs, preferences across hours of the day, and random utility shocks. I estimate the model using high-frequency data on millions of taxi trips covering over 14,000 drivers in NYC during an entire year. This allows me to characterize heterogeneity across drivers in a flexible and transparent way, estimating the model separately for each driver. Using the estimated parameters, I first evaluate the welfare loss to workers if discretionary breaks were replaced by scheduled breaks. My results show that flexibility is valued highly: the average driver in my sample would require a 22 percent increase in revenue to accept a counterfactual fixed work schedule. Further, I find substantial heterogeneity in this valuation, indicating that for some workers, discretionary breaks bestow a large non-pecuniary benefit. I then use the model to study the effects of a realistic ‘mandatory breaks’ policy on the frequency of breaks and labor supply. Counterfactual evidence shows that such a policy would substantially increase the frequency of breaks but would reduce labor supply by 6 to 9 percent. This result highlights the need to weigh the benefits of break-oriented policies—including a reduction in accidents—with the negative consequences for labor supply and the welfare of workers. While I use a specific industry to estimate the model, the proposed framework is quite general and can be applied to various other contexts to understand how workers in a given industry make their short-term labor supply decisions.