About Us

In 2014, Daniel Schneider, a demographer and sociology professor at the University of California, Berkeley, and his colleague Kristen Harknett, a sociologist at the University of California, San Francisco, were studying how the 2008 recession had affected families. … [T]hey found an enormous and enormously debilitating sense of everyday uncertainty. Many workers reported that they didn’t know when or how much they would work, if they’d be asked to stay late, or if they’d be called in at all. Despite the scope of the problem, Schneider and Harknett quickly realized that there was no data about what was going on. …

In 2016, what began as Schneider and Harknett’s informal interviews with hourly retail and service-sector workers in the San Francisco Bay area grew into The Shift Project, which is now the largest source of data on work scheduling for hourly service workers, with reports from 84,000 workers in the retail and fast-food sectors from across the country. The data includes worker schedules, economic security, and the health and well-being of workers and families.

Brigid Schulte
"Why Today’s Shopping Sucks" (Washington Monthly, March 2020)

The Shift Data

The Shift Project employs an innovative approach to survey data collection using Facebook and Instagram to identify and recruit respondents. We purchase paid advertisements through Facebook that target users identified as employees of large service-sector firms in the United States. Users who click on the ad are routed to a Qualtrics online survey. This approach yields a custom, policy-relevant sample of employees matched to their respective employer.

We field surveys to workers in retail, food service, grocery, delivery and fulfillment, and hospitality. Our core survey asks workers about job quality, with a specific focus on unstable scheduling. We also ask about workers’ financial security, personal health, the health and wellbeing of their children, and other spillover effects.

The Shift data shows that the vast majority of service-sector workers experience instability in their weekly work schedules. Workers who experience more predictable scheduling report less stress and better overall health, compared to workers who experience less predictable and stable scheduling. In fact, our data reveal that while low wages are negatively associated with poor health outcomes, unstable and unpredictable schedules are a much more significant determinant.

Project Components & Analysis

National Monitoring

The Shift Project data allow us to describe workplace scheduling practices and to characterize the health and wellbeing of retail and food-service workers at some of the nation’s largest firms. We use these data to monitor changes over time in scheduling practices and worker outcomes, both nationally and for particular firms. We also use these data to estimate the associations between schedule instability and worker and family financial security, health, and wellbeing.

Shift Data and COVID-19

Our approach allows for dynamic data collection in response to rapid changes in policy and other environmental shifts. This is of particular value to researchers and policymakers during the COVID-19 outbreak. When the coronavirus spread in the United States in early 2020, The Shift Project was able to deploy a redesigned survey instrument within one week of these developments, and as a result we have collected timely data on workplace protective equipment, cleaning procedures, paid sick leave policies, unemployment claims, household financial security, childcare and school closures, and other key measures of the impact of COVID-19. Early findings have been released in a report on workplace safety procedures. Furthermore, Shift’s pre-COVID data on worker-reported access to paid sick leave was published in The New York Times and subsequently cited by numerous outlets and policymakers.


We use Shift data to rigorously estimate the effects of secure scheduling laws on work scheduling practices and on the health and wellbeing of workers and their families, focusing on laws passed in Seattle, New York City, and the state of Oregon. We collect baseline data from workers covered by each of the ordinances and from comparison samples, and then collect follow-up data following implementation of the law. We use these data in a difference-in-differences framework to identify the effects of unstable and unpredictable schedules on health.

Who Takes the High Road?

While service jobs are often thought of as uniformly “bad jobs,” existing research in economics, sociology, and labor studies in fact suggests substantial heterogeneity in workers’ experiences of precarious labor practices within this sector. We exploit Shift’s unique worker-firm matched dataset to understand whether low-wage service-sector jobs are uniformly precarious across employers, or if job quality varies across firms. We then examine if firm-level characteristics — including ownership structure, unionization, and exposure to public pressure — shape workers’ experiences of precarious scheduling and account for variation across companies.