Compensation Data and Trends in the USA
dataforest.sequoia.com
dataforest.sequoia.com
A surprising finding here - that doesn’t match what I would have expected - is how Engineering is compensated 90% in cash. I would have assumed equity is pretty heavy for software engineers, especially in senior and above positions.
I do wish there were more takeaways to what seems to be data confirming these:
1. Compensation is going up, and especially software engineering compensation.
2. Startups are starting to compete stronger on cash compensation (thanks to a strong funding environment, and the market).
3. Remote work is slowly eroding regional differences in the US (and, as a note, globally, as well).
Sadly, there’s no data on remote work here, nor is there anything on what % the market moved up the last year in various disciplines. At least for engineering, the past year has been a major jump upwards in compensation.
This is from Europe, but I think it's true in the US as well. Software engineers at large tech companies and venture funded startups get paid a lot, including equity. Software engineers at consultancies or doing internal development at businesses with several thousand employees are on a different salary scale.
FAANG and similar jobs are equity heavy, but standard engineering employment is often more cash-heavy.
Working outside of FAANG, my base comp was significantly higher than FAANG base comp, but of course the total comp at FAANG would edge out my total non-FAANG comp.
It is not costly to provide quintile or decile data, yet no one ever does when that is where the meat and pickles are. I assume most of the time, averages (and especially unspecified averages where you do not know if it is mean or median), are used to invoke emotions and land more clicks.
> our gender gap data shows that companies are still struggling to offer their female and BIPOC workers equal pay for equal work
Does this data account for levels or years of experience? For example, if there are more junior-level women in the industry than senior-and-above level women, then of course you'd expect women to earn less, percentage-wise.
If it's not accounted for, then the quoted statement seems false.
Similarly, how come asian workers are left out in the following statement:
> With a broad stroke, male and White/Caucasian workers simply earn more than their counterparts.
It seems asian workers earn even more so it's weird how only white/caucasian is called out.
In my experience in tech, staying at the same company (long-tenured) is the best way to ensure you are underpaid. They seem to allude to that with:
> The “Great Resignation” may present an opportunity for long-tenured, underpaid workers to find competitive pay elsewhere.
If my understanding is correct then that doesn't account for what I'm calling experience or level.
Tenure just refers to how long someone has been with a specific company, not how long they've been in the industry.
So it's suggestive of _something_ bad that the pay gap increases with tenure for many categories of job, but it's not at all conclusive.
The ethnicity graph in that section begins with, "Asian and White/Caucasian workers see similar annual pay".
How you present data is important and I'm wondering why it's not presented there.
/shrug
My conclusion would have been:
"With a broad stroke, male, asian and White/Caucasian workers simply earn more than their counterparts."
which is different from:
> With a broad stroke, male and White/Caucasian workers simply earn more than their counterparts.
I could be misunderstanding the point you are making.
I don't disagree with the broader point that you're making, I'm just pointing out that you're doing what you're critiquing as you critique it. Your first post says that they don't actually say what the data shows, and you go on to suggest that an improper presentation of data can cause people take away the wrong idea.
I'm saying that you choosing not to mention the fact that they do state that fact accurately merely a few sentences later also could cause people to take away the wrong idea.
> Your first post says that they don't actually say what the data shows
And I maintain that the summary omits and misrepresents what the data shows.
> I'm saying that you choosing not to mention the fact that they do state that fact accurately merely a few sentences later
It's behind a click. It actually doesn't show at all without a click. I missed it the first time I skimmed the article (thank you for pointing it out) and I'm sure other people will as well.
This post shows an average in every region more than double that. It's not a representative sample. The charts are pretty but I don't think this analysis has any value.
says "People 15 years old and over beginning with March 1980, and people 14 years old and over as of March of the following year for previous years." seems it would be reduced by full-time students, retired people earning Social Security, etc.
> This post shows an average in every region more than double that.
says "this report reveals some of the rapidly changing dynamics in how companies of all sizes are compensating their employees." seems it only includes employees and thus would not include students, retired, etc.
If this is meant to be a representative sample of the US, the data doesn't pass the sniff test. They don't provide enough context to understand how else we should understand it.
The data here appear to come from salary surveys of working individuals. The US Census Bureau data appear to be reports of income from all sources for all persons, which appears to include children 14 and over, full-time students, disabled persons, retired persons, and other persons unlikely to be reporting a salary to Sequoia.
[1] https://fred.stlouisfed.org/series/MEPAINUSA672N
also, this report only covers a listed number of "departments" and leaves off many lower wage occupations. retail, child care, most unskilled labor, etc dont appear to be included in the Strategy, Sales, Design, Engineering, Leadership, etc.
Selection bias. Household income percentile is based on all households. Salary distribution excludes people who are not employed.
She said that, if we take an average male and female that have no skills whatsoever, male can still be a coal miner or do a construction job or be a mover better.
This means male inherently has higher earning potential. It makes sense that male earns higher.
As a side note, I appreciate she talked about this at work openly. As a male, I am scared to discuss such a thing at work.
I agree that it's often been awkward to talk about my compensation at work, though I continue to try if only to make such disparities more clear.
The article shows pay gap, and I offered one of the possible reasons that contributed to pay gap.
How is it not related?
This should nontrivially impact the average salary.
If the electronics industry wants to hire me, they would have to pay higher salaries. So the salaries in one industry (software) can drive up salaries in another (electronics)
Admittedly, nobody's leaving programming to become a miner - but plenty of jobs pay less than mining, so it could explain a pay gap in some parts of the salary range. Especially in countries like Australia with big resource extraction industries.
But a gender gap in industry A will not lead to an gender gap in industry B. Industry B will just hire the cheaper gender until industry B has no gender gap.
At non-physically demanding jobs, men and women are competing with each other fairly, so if the salaries demanded by one group go up, the employers will hire the other group until the salaries are equal.
Unfortunately certain activists continue to argue that there are no innate behavioural differences between men and women at the social level.
Those things show how prices/wages could be linked across industries through opportunity cost of the laborer. It doesn't suffice to explain how a sex pay gap could emerge. If hiring men were more expensive (because they have greater opportunity costs), then just don't hire them--hire the women, or whomever is willing to accept the wage you're willing pay for the particular labor services desired.
But they might have to pay more for the people who haul heavy boxes around in the back, which might be male-dominated, leading to a pay gap for 'supermarket worker' if not for 'supermarket cashier'.
Yes exactly. It would only explain a pay gap at the low end of the pay scale. It wouldn't explain it in the middle or high end.
Prostitute is highly frown upon socially and morally around the world... It is also often illegal in many parts.
They can be a construction worker or mover.
Edit: In S/W that might not matter at all, but some business functions hours might matter. More data the merrier.
I would be cautious when reading these kinda charts from non-scientific community.
Who knows what kind of biases are lurking there at data collection and interpretation level
Why does this sentence leave out Asian workers, when the Asian workers' bell curve actually skews towards even more pay than White/Caucasian workers'?
Alternative is that Asians are a minority in the US, being 6% of Americans, while White people are 60%, and so it is more interesting for talking about this larger group.
Interesting stuff for future perhaps: foreign vs. domestic; and breaking out Chinese + Indian at least for Asian.
Is this startups? All companies? Are food processors and cement producers and machine shops included here?
Hard to know how much you can trust the results with a small and biased sample like this.
I wonder if there are any legitimate non-dark pattern use-cases.