This is part 1 of my 2020 analysis of software developer earnings. You can find the rest of the results and methodology here.
Age. Race. Gender. Sexual orientation.
In an ideal world, none of these factors would matter for what a software engineer earns. As characteristics, they shouldn’t necessarily influence the productivity or value of a developer, and hence shouldn’t affect pay.
Turns out, however, they do — though not always in the way or to the degree you might expect.
In this first part of my 2020 analysis of software engineering pay, I explore how these demographic characteristics match with developer pay, tease apart correlation from causation, and explain the confounding factors driving some of the more surprising results.
Racial minoritiesmake up both the highest and lowest-paid developers
Femalesoftware engineers earn ~10% lower salaries than their male counterparts, but
professional experienceexplains most of the gap
Gay and lesbianengineers earn more than
straightengineers after adjusting for observable factors
Parentsearn significantly more that
non-parents, but this is explained by other factors
If you haven’t already, please check out the methodology behind the analysis, otherwise the numbers below might be difficult to interpret.
Developer earnings rise fairly steadily from the early 20s through the late 40s. The late 40s represent the highest earnings of a developer’s life, where the average developer earns 28.7% more than the typical 26-30 year old (the most common age range in the data), after which pay stabilizes before finally beginning to decline in the early 60s.
However, adjusting for other characteristics,
age is most “advantageous” in the 31-35 range, when a developer can expect to earn 4.7% more than an equivalent developer five years their junior. This advantage is highly statistically significant.
The pay bump quickly dissipates with additional years however, losing statistical significance and even turning somewhat negative by the 51 to 55 range, though this is not precisely estimated.
The key point is that the additional income earned by older developers is entirely explained by factors unrelated to age. When we control for other factors, pay does not vary much by
age after 35.
Why does the correlation between
age and income disappear after controlling for other factors? Let’s dig in:
The analytics demonstrate that
years of professional coding experience matters much more than age itself, which is what you’d hope to see. These additional
years of professional experience effectively explain the entire earnings premium for older software engineers.
Additionally important variables include
self-rated competence, having
influence over technology purchases in their organization, and
working remotely (which older workers are more likely to do), and having dependents.
As in last year’s analysis, the largest pay gaps are between minority groups, which make up both the highest and lowest-paid developers.
South Asians see the most statistically significant pay premiums relative to white developers with or without controls in the regression. In the case of
East Asians, their pay premium increases after controlling for various factors.
These premiums are large and meaningful —
East Asians earn 7.4% more than white developers and 13.9% more controlling for observable characteristics, while
South Asians earn 13.1% unadjusted and 8.1% adjusted more than
Breaking down the explainable
East Asian earnings premium yields some interesting findings:
years of professional coding experience is far and away the biggest factor holding down the pay of
East Asian software engineers.
East Asian developers typically have less work experience than whites, which holds down their earnings.
East Asiandeveloper would earn 8.0% more if they had similar amounts of professional experience as whites
Age also holds back
East Asian developers, as they are typically younger than white developers. This amounts to a 2.0% pay disadvantage.
Lastly, the sheer magnitude of the earnings premium enjoyed by
Asian developers should be noted. That the premium remains so large even after controlling for various factors is puzzling.
Let’s look at one more — the pay gap for
black developers. The unadjusted gap — which again simply compares average earnings of
white developers — is -7.6%, while the adjusted gap contracts meaningfully to -0.3%, which is not statistically significant:
Breaking down the explainable gap for
blacks reveals similar drivers as we saw in the
East Asian case.
Years of professional coding experience is the main contributor to lower pay for black software engineers relative to
whites, in total driving 5.8 percentage points of the overall 7.6% gap. Nothing else matters nearly as much.
In a sense, this is heartening. Assuming a
black engineer gets as much out of an additional year of work experience as anyone else, purely closing the gap there would bring black pay nearly in line with white pay.
On the other hand, that the unadjusted gap is explainable via
years of experience also means that the gap is unlikely to close anytime soon.
age structure of the workplace is slow to change — it takes decades to see sizable shifts
blackprofessionals entering the software development career track start off at the bottom rung of the ladder
blackearnings, as fresh out of bootcamp developers don’t earn nearly as much as seasoned veterans
This is not a bad thing — but it does mean I can’t conclude something nefarious is behind the slow convergence of
white wages without other evidence.
The other factor worth touching on is
ImpSyn which is a variable representing a respondent’s own
confidence in their skills as a software developer. More
confident developers earn more, and there appears to be a confidence gap between black and white developers driving 1.1% of the earnings gap.
Women earn 10.0% less than male software engineers on average, a sizable difference. However, this gap is effectively eliminated once we adjust for controllable factors, falling to only 1.4%, which is not statistically significant.
In diagnosing the unadjusted 10.0% pay gap for
years of experience pops up once again as a dominant factor explaining most of the gap:
5.7 percentage points of the
gender pay gap can be explained by the fact that
female developers have less professional experience than
male developers on average. Adding in overall
coding experience explains 7.1 total percentage points of the overall gap.
women are only 1.7 years younger than
men on average in the dataset, they have 3.3 fewer
years of professional coding experience (7.1 years for
women vs. 10.4 for
men). We can see this in the histogram / kernel density estimation comparing the respective distributions of
years of professional coding experience of
women, where the distribution for
women is shifted and more clustered to the left. This meaningful difference explains why
professional experience is such as major driver of the gender wage gap.
ImpSyn) comes up again as a factor pulling down
female wages. Here, the
confidence gap explains 1.0% of the overall
gender gap, very similar in magnitude to that of
black developers vs.
In line with other research,
women are also less confident about their own programming skills than
men are (who for all we know might be overconfident), which explains another 1.0% of the total gap (because higher confidence leads to higher pay, as I cover in a later post).
Experience and confidence collectively explain 8.1% of the gender pay gap among software engineers, leaving only a 1.9% gap, including the 1.4 percentage point difference that we cannot explain.
Unadjusted pay gaps among non-straight software engineers ranges from 2.5% for
lesbian developers to 9.6% for
bisexual developers, which simply means these individuals earn less on average than
In the case of
lesbian developers however, this gap closes and actually reverses once I add controls. The gap becomes a pay advantage of 3.4%.
Starting at the adjusted / unexplained 3.4% premium, the lower average unadjusted earnings is largely accounted for by
years of professional coding experience (a recurring theme) and
age, suggesting that
lesbian developers are simply earlier in their careers than
straight developers, on average.
The decomposition of the difference in adjusted and unadjusted pay gaps is quite similar for
Professional experience and
age continue to the do the most explanatory work here:
Software engineers with
dependents (typically children) earn 17.2% more than those without, but this earnings premium can be almost entirely accounted for via the other factors described in this analysis.
Controlling for these variables reduces the parenthood earnings premium to 1.5%, which is small but statistically significant.
years of software development experience and
age account for most of the earnings premium among parents. These are largely mid and late career engineers.
Working remote explains another 1.1% of the pay premium among parents, as they are more likely to work from home, which makes sense given they may have a child to take care of.
Not to editorialize, but I was encouraged by many of the results here. In general, along most dimensions, discrimination in software developer earnings appears small once various factors are controlled for. In most cases, the biggest factors were some combination of
years of coding experience and
age which are both “problems” that will largely fix themselves as the industry diversifies.
With the exception of
race, most of the gaps are no more than a few percentage points in magnitude. In the case of race, the gaps are meaningful in some cases but difference is in fact in favor of minority groups like
South Asian, and
Middle Eastern software developers. Their pay advantages are substantial, and the data from the Stack Overflow survey fail to fully explain these gaps.
The usual caveats to pay analyses apply here as well. Finding little discrimination on pay, after controlling for factors such as
job title, does not disprove discrimination in its entirety.
femalesoftware engineers face discrimination in the form of reduced upward career mobility, that will not show up in this analysis, even though it depresses their earnings
Analogous statistical caveats abound.
That said, the data suggest the software development industry is well on its way to pay parity across the important dimensions of age, race, gender, and sexuality.
Thanks for reading part 1 of my 2020 analysis of software developer pay. You can find the rest of the analysis and methodology here.