
The effect of markets on discrimination is nuanced - luu
http://danluu.com/tech-discrimination/
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yummyfajitas
The article sets up a straw man and nicely destroys it:

 _Not only did competition not end discrimination, there was enough
discrimination that the act of not discriminating provided a significant
competitive advantage for Townsend-Greenspan...But even that wasn’t enough to
equalize wages between men and women..._

The absence of discrimination does not imply wages will be equal. It implies
female wages will be equal to female marginal products. If female marginal
product < male marginal product, then wages will be lower.

Further, the existence of discrimination does suggest Greenspan-Townsend like
firms arbing this irrationality will exist. Can anyone find such firms in the
US? I can certainly find firms arbitraging other non-productivity based pay
gaps, e.g. US vs Indian engineers.

~~~
RodericDay
What's the strawman, exactly?

You say,

> Further, the existence of discrimination does suggest Greenspan-Townsend
> like firms arbing this irrationality will exist.

The article counters statements like,

> Marc Andreesen’s point is that the market is too competitive for
> discrimination to exist.

With,

> In the long run, that can put pressure on firms that discriminate, but as
> we’ve seen, that timescale can easily be longer than half a century. As the
> saying goes, in the long run, we are all dead.

It seems like he's taking on a widely held position, even by yourself. Far
from "a strawman".

~~~
yummyfajitas
The strawman is that if discrimination did not exist, pay would be equal.

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geofft
The market-based defense makes so little sense in a market like the startup
world. If you're evaluated on product and technical performance, maybe
(although there are still enough ways for human bias to creep in). But
startups are, by definition, not valued for their existing technical /
business performance, but on the collective perception of future performance,
informed by the knowledge that people (VCs, customers, potential employees,
etc.) have of previous startups and how successful they were. If these people
are doing their job _correctly_ , there's at least some bias present (e.g. all
other things being equal, you should invest in the Harvard dropout with an
idea for a NoSQL database but no code, over the Juilliard dropout with an idea
for a NoSQL database but no code).

So naturally, if all the examples you have of successful startups involve
people with characteristic X, and you have no data about people without
characteristic X, the rational thing to do as a VC or potential early employee
looking for startups, or a founder or hiring manager looking for employees, is
to look for people with characteristic X. Let someone else do the research;
it's not up to you to invest your money or time on figuring out if people
without characteristic X can also be successful.

And this is _stable_ , regardless of what characteristic X is. Once the market
is biased in favor of such people, for whatever reason -- including the
"actual" racism or sexism that we think we're past, but is sufficiently within
living memory to have affected the market -- as long as the market is
generally doing well (as startups are), it is more risk to figure out if
characteristic X has any rhyme or reason behind it than to just assume it
does. And especially given the high personal investment made by founders and
early employees, adding even more unknowns is not super compelling.

The Greenspan anecdote is interesting because he knew -- not empirically, but
_fundamentally_ \-- that women were capable of doing work as good as men, and
he was willing to risk his company on that.

~~~
avs733
>So naturally, if all the examples you have of successful startups involve
people with characteristic X, and you have no data about people without
characteristic X, the rational thing to do as a VC or potential early employee
looking for startups, or a founder or hiring manager looking for employees, is
to look for people with characteristic X. Let someone else do the research;
it's not up to you to invest your money or time on figuring out if people
without characteristic X can also be successful.

I tend to take what you are saying a little differently. It is 'rational' in
the sense of the heuristics (e.g. the recognizability heuristic) that people
commonly use to deal with probability. However, the underlying philosophy
(i.e. constraints/assumptions) rely on the certain characteristics of the
problem for them to actually be rational instead of just seem rational. We use
probability heuristics a lot, poorly, and uncorrelated to the statistical
expertise of those using the heuristic. We use them exactly because they seem
rational, even when they are not.

To go way back in economics history (Frank Knight), they rely on analytical
uncertainty...which the human brain has a strong bias towards. Analytical
uncertainty relies upon a potential outcome space being knowable and
independent of the actions of those making the predictions. Knight argues that
in entrepreneurship the future performance is fundamentally not predictable in
this way. The result is that the classical conceptions of uncertainty,
probability, and (which is grounded on their being a tangibly solvable or
characterizable uncertainty) risk do not apply/are not appropriate. Generally
these more 'Knightian' conceptions are where we see truly new ideas (i.e.
disruption) come from in ways that look obvious and form new markets in
retrospect but are not foreseeable using a priori prediction tools.

Saying that X is stable is accurate...it is choosing to make a prediction,
which is fine. I would argue that it is better approached in a Bayesian sense
to try and figure out whether X is really the thing you are looking for or
merely a symptom that matches your algorithm for the underlying nugget.
However what is often problematic to success is a reliance on these attempts
to predict what is fundamentally unpredictable, especially for startups[1].
The successes of prediction (what successes there are) are more traceable to
the existence of Nash/Nash-like equilibrium in these newly created, previously
unpredictable markets where there is space for competition simply because the
new markets crystallize very quickly once they are initiated. The speed of
crystallization (and the ability to make money via prediction) has always been
a product of the rate of information dissemination being greater than the
ability of companies to grow.

Your Harvard drop out may incrementally improve NoSQL, she might be
convincing, and she might be worth a large long term investment with a
measurable/calculable risk for a target ROI. However, your Juliard student may
have a higher potential to fundamentally shift NoSQL thinking and be worth a
small (hypothesis driven)investment to test the potential that we cannot
calculate. What you are interested in as an investor is largely up to you but
they are not interchangeable. This stuff feeds into so much of the parts of
the startup ecosystem that work and don't.

[please forgive the state of this response, it was written quickly at my
bedtime...mostly a random statistical aside to your larger point, I kind of
tangented at 'market like the startup world']

[1]Wiltbank, R., Read, S., Dew, N., & Sarasvathy, S. D. (2009). Prediction and
control under uncertainty: Outcomes in angel investing. Journal of Business
Venturing, 24(2), 116–133.
[http://doi.org/10.1016/j.jbusvent.2007.11.004](http://doi.org/10.1016/j.jbusvent.2007.11.004)

~~~
geofft
Yeah, I think I don't disagree with any of that. To clarify a bit, I assert
(but don't have any good proof for):

1\. If you're involved with a startup, you're inclined to take risks (explore
unexplored spaces in the market) on specific things that are closely
correlated with the specific experiment the startup is investigating, but not
particularly more willing than non-startup folks -- and probably _less_
willing, because of how much risk you already have -- to do risky things
outside of that specific experiment. If you want to place one risky bet,
that's worth doing. If you want to place dozens of risky bets _on the same
venture_ , you'll lose. (VCs know this, and place dozens of risky bets one per
venture.) For instance, just about no startup will, or will be advised to, run
on a nonstandard OS just in case there's some secret sauce. There are
certainly a few, but they have founders who believe firmly, a priori, that the
secret sauce exists and the market hasn't recognized it. ~Nobody says, "Well,
we built our system on AIX because nobody uses AIX so we figured we might get
competitive advantage out of it."

2\. Yes, the Juilliard drop-out _might_ do way better than the Harvard one.
But in the (hypothetical) case that you have literally zero information to
differentiate the two founders other than their school -- let's say, they have
the same programming background and ability, their parents pushed them both
towards music, both were admitted to Harvard but one's Juilliard application
was lost in the mail -- very few investors would prefer the Juilliard kid.

------
yuhong
My argument is that anti-discrimination laws are not an effective tool for
anything other than manual labor and the like because of the enforcement
problems.

