5 ms·

The effect of markets on discrimination is nuanced

19 points by luu 11y ago

yuhong 11y ago
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.
yummyfajitas 11y ago
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 11y ago
  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 11y ago
    The strawman is that if discrimination did not exist, pay would be equal.
- geofft 11y ago
  It suggests those firms will be marginally more profitable if they exist; it does not suggest that they must necessarily exist. The author was able to give one example of a firm successfully being more profitable, and that because someone in a hiring role had a deep-seated belief that female marginal product was equal to male marginal product (or at least, not as much lower as female wages from male) and was willing to act immediately on that belief, regardless of industry best practice, regardless of waiting for any actual data on gender-based marginal product differences. (And startup profitability is so wild and noisy that, given that even generous estimates of the tech wage gap don't indicate orders of magnitude of difference, any individual startup that takes this strategy may have its resulting profit masked by a million other things. If you want to infer anything from the experience of such startups, you need a lot of startups.) Further, given current rhetoric about pay in the tech industry, it may be unconscionable (or if you wish to be cynical, "it may lead to sufficient bad PR that it will blow away any positive results") to focus recruiting on an undervalued group but also continue to underpay them. So there may not actually be an arbitrage opportunity; any employer that would want to recruit an undervalued group may feel compelled to not act on the arbitrage as far as possible.
  - yummyfajitas 11y ago
    The author was able to give one example of a firm successfully being more profitable, and that because someone in a hiring role had a deep-seated belief that female marginal product was equal to male marginal product (or at least, not as much lower as female wages from male) That's not what Greenspan thought at all. Greenspan thought you can get a $100k economist for only $80k. That belief is completely orthogonal to the distribution of employee quality. I don't think Indian developers are "equal" to American developers - I think they are (on average) a lot worse. (The bottom of the Indian developer market is a lot lower than the bottom of the US market, and the top of the Indian market tends to migrate west.) I also think I can get a $150k data scientist for $70k if I look hard enough. Further, given current rhetoric about pay in the tech industry, it may be unconscionable (or if you wish to be cynical, "it may lead to sufficient bad PR that it will blow away any positive results") to focus recruiting on an undervalued group but also continue to underpay them. So there may not actually be an arbitrage opportunity; any employer that would want to recruit an undervalued group may feel compelled to not act on the arbitrage as far as possible Interesting hypothesis; social barriers created by social justice types actually cause inequality to persist.
geofft 11y ago
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 11y ago
  >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

19 points by luu 11y ago

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.

yummyfajitas 11y ago

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 11y ago
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 11y ago
  The strawman is that if discrimination did not exist, pay would be equal.
geofft 11y ago
It suggests those firms will be marginally more profitable if they exist; it does not suggest that they must necessarily exist. The author was able to give one example of a firm successfully being more profitable, and that because someone in a hiring role had a deep-seated belief that female marginal product was equal to male marginal product (or at least, not as much lower as female wages from male) and was willing to act immediately on that belief, regardless of industry best practice, regardless of waiting for any actual data on gender-based marginal product differences. (And startup profitability is so wild and noisy that, given that even generous estimates of the tech wage gap don't indicate orders of magnitude of difference, any individual startup that takes this strategy may have its resulting profit masked by a million other things. If you want to infer anything from the experience of such startups, you need a lot of startups.) Further, given current rhetoric about pay in the tech industry, it may be unconscionable (or if you wish to be cynical, "it may lead to sufficient bad PR that it will blow away any positive results") to focus recruiting on an undervalued group but also continue to underpay them. So there may not actually be an arbitrage opportunity; any employer that would want to recruit an undervalued group may feel compelled to not act on the arbitrage as far as possible.
- yummyfajitas 11y ago
  The author was able to give one example of a firm successfully being more profitable, and that because someone in a hiring role had a deep-seated belief that female marginal product was equal to male marginal product (or at least, not as much lower as female wages from male) That's not what Greenspan thought at all. Greenspan thought you can get a $100k economist for only $80k. That belief is completely orthogonal to the distribution of employee quality. I don't think Indian developers are "equal" to American developers - I think they are (on average) a lot worse. (The bottom of the Indian developer market is a lot lower than the bottom of the US market, and the top of the Indian market tends to migrate west.) I also think I can get a $150k data scientist for $70k if I look hard enough. Further, given current rhetoric about pay in the tech industry, it may be unconscionable (or if you wish to be cynical, "it may lead to sufficient bad PR that it will blow away any positive results") to focus recruiting on an undervalued group but also continue to underpay them. So there may not actually be an arbitrage opportunity; any employer that would want to recruit an undervalued group may feel compelled to not act on the arbitrage as far as possible Interesting hypothesis; social barriers created by social justice types actually cause inequality to persist.

geofft 11y ago

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 11y ago
>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