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One of the biggest problems faced by freelance tutors is choosing a price. Too high or too low, and tutors lose out on earnings. What should a tutor take into account when setting a price? This project surveys the relevant economic literature—most importantly wage determination—to specify a tutor pricing model, and then applies econometric methods to test the model. The data set used is from Knowledge Roundtable, which is a website matching independent tutors to students, and contains data on 1,250 tutors from around the United States. Using the natural logarithm of tutor price as the dependent variable, it was found that education level, years of experience (tenure and age), having a professional certification related to teaching, teaching technical subjects, income level by zip code, and population density have positive and statistically significant effects on tutor price. Surprisingly, the coefficients on binary variables for gender, test prep, and versatility (offering both technical and nontechnical subjects) were not statistically significant. While the R2 of 0.21 in the final model is in line with conventional wage determination studies, it also supports the need for research into additional determinants of earnings, especially if the goal is to help individual tutors choose the right price.


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