August 2018

The Salary Calculator tool is here

We surveyed faculty members in degree-granting physics departments and those teaching physics in two-year colleges in the US in 2016. We offer our gratitude to the almost 1,700 who provided data on their employment status, rank, tenure status, gender identity, and academic training. We have used those data to estimate a series of regression models that we now use to provide average salaries of physics faculty members in US two-year colleges, colleges, and universities.

The results are average salaries and are not claimed to be actual salaries. Actual salaries will vary above and below the mean. We provide a range which indicates the variation in salaries. The range includes one standard error above and below the mean. The regression is run on the log of salaries (because salaries are skewed), so the range is not symmetric. We use a series of models because no one model meets every purpose. Each model includes all the variables in the previous model.

These regression models explain 85 – 86% of the variation in the 1,694 salaries included in our analysis. There is still 15% of the variation that is not explained. We recognize that salaries are a function of the variables included in the model and other factors. These salaries are not adjusted for required contributions for health insurance, for example.

The results are adjusted to 2019 levels using the US CPI.

# National estimate

The model for the national estimate includes:

**Institutional Characteristics**

- HBCU
- Public, four-year institution
- Two-year college

**Departmental Characteristics**

- Highest physics degree offered (does not apply if institution is a two-year college)

**Position Characteristics**

- Full-time or part-time
- Period salary covers (9-10 months, 11-12 months, or course-by-course)
- Rank
- Tenure status

**Personal Characteristics**

- PhD recipient (or not)
- Highest degree earned in the US
- Completed a postdoc
- Year of highest degree

This basic model explains 85.5% of the variation in salaries.

# Adding Gender

We add gender to the model. We understand that gender is not necessarily binary. However, we have data to estimate the averages salary only for people identifying as men and women. Adding gender alone is marginally significant (one-tail *p-*value = 0.075). However, adding gender and the interaction of gender and associate professor is statistically significant. The *p*-value for the F test for the change in R-square is 0.037. The adjusted R-square is 0.855, so this model, too, explains 85.5% of the variation in salaries.

# State-level estimate

We next add population density to the model. The *p*-value for the F test for the change in R-square is < 0.0005. We use population density instead of state because we do not have sufficient data from each state to provide estimates using states. However, population density is positively correlated with per capita income (*p-*value = 0.069), so we use that as a proxy for each state. While population density does explain slightly more of the variation in salaries, adding the Census Bureau region to the model helps refine the state-level estimate. This model explains 86.3% of the variation in salaries.

For questions, please contact Susan White at swhite [at] aip.org.