Graduation Year


Document Type




Degree Granting Department


Major Professor

Dahlia Robinson, Ph.D.

Co-Major Professor

Christos Pantzalis, Ph.D.

Committee Member

Lisa Gaynor, Ph.D.

Committee Member

Jung C. Park, Ph.D.

Committee Member

Don Addison, Ph.D.


economics, gratutites, restaurants, servers


There is a movement underway to eliminate the practice of tipping restaurant servers that is gaining momentum (Goldberger, 2015). In lieu of gratuities, restaurants are simply raising menu prices or assessing a service charge and paying servers a fixed hourly wage (Kummer, 2016). Before restaurateurs can adopt such a strategy, they need to thoroughly understand the factors that affect tipping behavior in order to develop meaningful fixed wage rates that do not diminish service levels or employee morale. The first step in this process is a better understanding of the determinants of tipping rates.

The existing research has identified many factors that influence tipping rates. Some factors are outside of the server’s control such as the server’s and the customer’s race, the size of the bill, the size of the dining party, and whether or not alcohol was consumed. Other factors are within the servers control such as the quality of the service, whether they squatted at the table, or wrote a smiley face on the guest check. Most of this research, however, is based on empirical analysis using small samples sizes and/or questionnaires that may not reflect actual behavior, or data from interviews based on what consumers say they did. The contribution of this research is that I identified two additional significant determinants of tipping rates, sales tax rates and discount rates, which have not previously been studied. This research also extends the previous research related to the impact of bill size and dining party size but with a significantly larger sample. The study presented herein includes an analysis over 75 million guest checks from 43 brand-name restaurants across 1,202 locations over three years to understand precisely how customers behaved.

The data were analyzed using a two-way fixed effects, ordinary least squares model to limit time and spatial controls to a single national time trend with state fixed effects. The state fixed effects control for differences across states that are fixed over time and quarterly fixed effects will control for factors that impact tipping rates equally across all states in a given quarter. Robust standard errors were clustered for the 43 restaurant brands and twelve quarters of time to account for temporal serial correlation in the error terms within the locations.

The analysis revealed that there is a positive relationship between sales tax rates and tipping rates and an inverse relationship between discount rates and tipping rates. This essentially implies that consumers are tipping on the post-tax bill net of any discounts. The study also confirms the results of certain prior research in that both party size and bill size are inversely related to tipping rates up to a certain point.