Factors that Influence Salary: The Regression Model
WE HAVE INCLUDED OUR FULL regression model in Appendix B. For this year’s report, we have made two important changes to the basic, parsimonious linear model we presented in the 2015 report. We have included: 1) external geographic data (GDP by US state and country), and 2) a square root transformation. The transformation adds one step to the linear model: we add up model coefficients, and then square the result. Both of these changes significantly improve the accuracy in salary estimates.
Our model explains about three-quarters of the variance in the sample salaries (with an R2 of 0.747). Roughly half of the salary variance is due to geography and experience. Given the important factors that can not be captured in the survey— for example, we don’t measure competence or evaluate the quality of respondents’ work output—it’s not surprising that a large amount of variance is left unexplained.
Impact of Geography
Geography has a huge impact on salary, but is not adequately captured due to sample size. For example, if a country is represented by only one or two respondents, this isn’t enough to justify giving the country its own coefficient. For this reason, we use broad regional coefficients (e.g., “Asia” or “Eastern Europe”), keeping in mind however that economic differences within a region are huge, and thus the accuracy of the model suffers.
To get around this problem, we’ve used publicly available records of per capita GDP of countries and US states. While GDP itself doesn’t translate to salary, it can serve a proxy function for geographic salary variation. Note that we use per capita GDP on the state and country level; therefore the model is likely to produce an inaccurate estimate with GDP figures for smaller geographic units.
Two exceptions were made to the GDP data before incorporating it into the model. The per capita GDP of Washington DC is $181K—much greater than in neighboring Virginia ($57K) and Maryland ($60K). Many (if not most) data science jobs in Maryland and Virginia are actually in the greater DC metropolitan area, and the survey data suggest that average data science salaries in these three places are not radically different from each other. Using the true $181K figure would produce gross overestimates for DC salaries, and so the per capita GDP figure for DC was replaced with that of Maryland, $60K.
The other exception is California. In all of the salary surveys we have conducted, California has had the highest median salary of any state or country, even though its per capita GDP ($62K) is not ranked so high (nine states have higher per capita GDPs, as do two countries that were represented in the sample, Switzerland and Norway). The anomaly is likely due to the San Francisco Bay Area, where, depending on how the region is defined, per capita GDP is $80K–$90K. As a major tech center, the Bay Area is likely overrepresented in the sample, meaning that the geographic factor attributable to California should be pushed upward; an appropriate compromise was $70K.
Considering Gender
There is a difference of $10K between the median salaries of men and women. Keeping all other variables constant—same roles, same skills—women make less than men.
Age, Experience, and Industry
Experience and age are two important variables that influence salary. The coefficient for experience (+3.8) translates to an increase of $2K–$2.5K on average, per year of experience. As for age, the biggest jump is between people in their early and late 20s, but the difference between those aged 31–65 and those over 65 is also significant.
We also asked respondents to rate their bargaining skills on a scale of 1 to 5, and those who gave higher self-evaluations tended to have higher salaries. The difference in salary between two data scientists, one with a bargaining skill “1” and the other with “5”, with otherwise identical demographics and skills, is expected to be $10K–$15K.
Finally, in terms of work-life balance, our results show that once you are working beyond 60 hours, salary estimates actually go down.
