
Jupyter Notebook Analysis of Salary Data Spreadsheet - talloaktrees
https://cdn.rawgit.com/jrenner/hacker-news-salaries-data/master/explore_salaries.html
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NeutronBoy
It's interesting - the company I work for (non-US, consulting) did analysis of
gender pay gaps internally and noted that on average, females get 70% of male
pay. However, _within the same role_ , the difference was just 3%. Their focus
is now on flexible working and figuring out why women are excluded/not
interested in particular roles.

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qznc
That seems to be the current state of knowledge.

> Although additional research in this area is clearly needed, this study
> leads to the unambiguous conclusion that the differences in the compensation
> of men and women are the result of a multitude of factors and that the raw
> wage gap should not be used as the basis to justify corrective action.
> Indeed, there may be nothing to correct. The differences in raw wages may be
> almost entirely the result of the individual choices being made by both male
> and female workers.[21]

– U.S. Department of Labor as cited by Wikipedia
[https://en.wikipedia.org/wiki/Gender_pay_gap](https://en.wikipedia.org/wiki/Gender_pay_gap)

~~~
pc86
And yet people will still throw out the ~76 cents on the dollar figure that
has been disproven again and again. Even 3% may be statistically significant
so I don't see why some people feel the need to be blatantly dishonest
regarding the problem.

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matthewowen
The issue isn't just about "choice" etc: it's also about how jobs that women
predominantly do are lower in prestige and pay than jobs that men
predominantly do.

The change in programming from being a low prestige, low salary job done by
women to being a high prestige, high salary job done by men is sometimes cited
an example of that. I don't know if there are flaws in that example, but I
think the overall idea makes sense. I read an interesting article about this,
but I don't recall the link. Sorry.

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pc86
Perhaps its important to determine if there are flaws in the example before
using it as an example.

~~~
matthewowen
Alternatively, perhaps it's worthwhile to suggest an idea that might be
relevant to a topic, even if one doesn't currently have the time or
inclination to explore it in depth. I mean, you're interested in the truth
right? Not just the rhetoric necessary to argue a position you already hold.

But sure, go for it. Why would you want someone to suggest an alternative way
to explore or think about a problem when you can respond with snark instead?

You might find life more fulfilling if you don't treat new information as
inherently antagonistic.

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metakermit
I'm trying to collect a similar dataset for freelancer hourly rates on Reddit:

[https://www.reddit.com/r/freelance/comments/4bsk14/freelance...](https://www.reddit.com/r/freelance/comments/4bsk14/freelance_hourly_rates_a_crowdsourced_spreadsheet/)

Tried it on Hacker News as well, but it didn't get any attention:

[https://news.ycombinator.com/item?id=11337833](https://news.ycombinator.com/item?id=11337833)

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wyldfire
As best I can tell, that last graph would've been clearer as a bar graph and
not a scatter plot (or at least better labels). The x axis merely indicates
the location. So, "mean annual salaries by location (in kUSD)" might be a good
title for the last one.

~~~
eoinmurray92
Also are all the salaries in USD? I know Europe might pay less in tech - but
the bottom three paying locations being Sweden, Berlin and London seems
strange to me. Perhaps those locations salaries are not being converted to
USD...

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torrance
I appreciate this if only as a great example of using jupyter notebook and
pandas. Thanks!

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talloaktrees
github repo:

[https://github.com/jrenner/hacker-news-salaries-
data](https://github.com/jrenner/hacker-news-salaries-data)

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friendzis
What strikes me that salary~experience (both company and overall) correlation
is basically nonexistent in this particular dataset, at least looking
visually.

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zanybear
I had the same thought, but then you have to remember years of experience does
not equal mastery.

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Toenex
I may have missed it but it looks like the plot from cell 'In [64]' hasn't
converted from Euros and GBP to Dollars.

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TheAppGuy
I've got 99 problems, but a boss ain't one!

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markovbling
Awesome! Think you should convert the currencies at current spot rates instead
of just removing the currency sign though :)

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jchendy
It looks like all the analysis is just on base salary. It would be much more
interesting if it included bonus and stock.

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chucky_z
Interesting there seems to be a shift in the 0-2 year job length position. I
wonder if this is because the common trend is to jump ship at 2 yrs, so folks
are going out job hunting, and getting a competitive raise at their current
position with the job offer in hand?

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slashcom
Most interesting to me was that the gender gap was so much more pronounced in
SV than outside

non-SV male median: 97000

non-SV female median: 90000 (92% of male)

SV male median: 137120

SV female median: 99187 (72% of male!)

Unless there's some external factor here, things look pretty damning in SV...

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pedrosorio
Without looking at the distribution of job_title between male/female in SV and
non-SV, these numbers are meaningless.

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Xorlev
Not to mention, the sample sizes are quite skewed -- the quantiles _might_
line up better with more data.

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stared
Log-log scales would really help. Otherwise - points in the low range are too
dense to be useful (big differences in density make be hard to spot, or even -
invisible).

