Responsiveness to extreme weather days also increases following an increase in the minimum wage. When it becomes costlier for a business to keep workers on a shift when their productivity is not maximized, like on a slow-business day, schedules are more frequently adjusted at the last-minute.

Most workers in this setting earn near the minimum wage. I next show that a large wage hike increases schedule unpredictability: schedule inaccuracy rises about 45 min per week, and weekly hours become about 4 hours more variable, indicating firms adjust along this margin to offset higher wages.

Second, I show that business uncertainty contributes to schedule unpredictability. Using weather shocks that are known to reduce demand, I find schedule inaccuracy rises on very hot, cold, or rainy days, indicating firms shift the risk of slow days onto workers.

I also take advantage of the fact that I can follow workers across businesses in my data to show that the observed scheduling unpredictability is a business-specific characteristic and does not appear to be largely driven by worker preferences.

I use a large administrative dataset with workers’ planned schedules, hours worked, and wages. I first show that schedule unpredictability is widespread: last-minute changes are common, weekly hours fluctuate, and the lowest-wage, least-tenured workers face the most instability.

I’m on the #EconJobMarket! I study labor, extreme weather adaptation, and inequality.

My JMP addresses an under-studied aspect of the labor market: schedule unpredictability among hourly workers in the service sector.

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