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Gender, race, and entrepreneurship: A randomized field experiment on investors

papers.ssrn.com

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Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#31

Post by jari_mustonen »

From abstract: ” Female entrepreneurs received 9% more interested replies than males pitching identical projects and Asians received 6% more than Whites. Our results suggest that investors do not discriminate against female or Asian entrepreneurs when evaluating unsolicited pitch emails and that future research on investor biases should focus on networks and in-person interactions.”

In other words: The preferred results were not found (females are oppressed) so we ignore wrong-think results (males seem to be) and continue to hunt evidence for preferred results elsewhere.

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#33

Post by tinyhouse »

I never understand why classify people on their looks. Talking primarily about the US. There is such a wide spectrum of white people coming from different cultures. Also, you're considered Asian if you're a Chinese immigrant but also if you were born in the US to Chinese descent let's say. The main common thing between the two groups is looks. Same for Africans and African Americans who are both classified as blacks but have very different culture. Then there is the latino group who once the lose their accent and don't look so latino anymore they drift to the white group...

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#34

Post by msoad »

hkt wrote: post #13
msoad wrote: post #11

Earlier quoted context omitted.

It will get even more India focused because of the latest laws.

Which are? (non-american here so I don't know about your immigration system)

They removed the caps on green card applications be country (I think, I don’t know exactly either)

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#35

Post by cameronh90 »

dash2 wrote: post #19

I wondered why they didn't report results for black versus white. Interestingly, they didn't run that: "After long and thoughtful consideration, we discarded the idea of assigning African-American names to our fictitious entrepreneurs. First, there has been a concern about the ability to disentangle race and socioeconomic perception by using distinctly African-American names (Fryer and Levitt (2004)). Second, and mos…

"are there any unambiguously African-American names"

Something like DeShawn is almost certainly going to be a black person, but that would have socioeconomic perception issues as the text points out. It's an interesting point though. I had a look for data and couldn't really find much of use.

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#36

Post by inglor_cz »

throwaway894345 wrote: post #21
jhgb wrote: post #5

Earlier quoted context omitted.

> Why write it the other way around. Maybe that's how the hypothesis under test was phrased?

Indeed, reading further into the article they seem to be trying to answer, “At what stage in the funding pipeline do VCs become biased against women?”. It’s interesting that they don’t entertain the possibility that the gap might be caused by something other than bias (at least so far... I’m only 6 pages in). For example: > Combating bias requires understanding how and where it manifests. Combating bias requires unde…

"It’s interesting that they don’t entertain the possibility that the gap might be caused by something other than bias"

Imagine a medicinal study that would concentrate on the hypothesis that sinners get cholera as a divine retribution for their sinful lives, and would not even entertain other possible causes, such as ... dirty drinking water.

We would rightly locate such a study into, say, 1840, but definitely not 2021.

That is where authors of this study belong mentally. Into the science of the era when Lincoln was young.

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#37

Post by musingsole »

tinyhouse wrote: post #33

I never understand why classify people on their looks. Talking primarily about the US. There is such a wide spectrum of white people coming from different cultures. Also, you're considered Asian if you're a Chinese immigrant but also if you were born in the US to Chinese descent let's say. The main common thing between the two groups is looks. Same for Africans and African Americans who are both classified as blacks…

Arguably that's why the US has less severe racism/classism than much of the rest of the world (despite whatever pop narrative).

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#38

Post by ajsnigrutin »

adflux wrote: post #12

As long as there aren't as much women studying all kinds of engineering, you're going to see disparaties in the workforce. Lets stop trying to force equal distribution but rather equal opportunity. Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.

> Funny thing to see is that as a country becomes MORE developed, women tend to be LESS inclined to attend STEM studies.

Stem jobs are relatively shitty jobs... paid well, but work schedule can be relatively screwed up. If you add enterpreneurship to that, it makes the work-life balance even worse. Women on average prefer better work-life balance, and men prefer careers where they can get higher and earn more.

In some countries (less developed), the only way to lead a realatively OK life is to get a stem degree and work something related to that. In more developed countries, the job market (and social support) makes it possible to live OK even with a "worse" degree (ie. the ones where you can't get a job with in less developed countries).

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#39

Post by tomcooks »

baybal2 wrote: post #24
ArkanExplorer wrote: post #10

Same thing with job applications: "In an attempt to increase workplace diversity, the Australian public service implemented a .. trial removing all mention of gender and ethnicity from applications. It was found that removing gender from thousands of applications made women less likely to advance through the recruitment process compared to cases where gender was visible. The trial found that a woman's name on an appl…

Nobody prevents Indian women to apply for H1B jobs in US. > Its counter-productive to institute gender and diversity requirements at (tech) companies, where the core problem is at the fundamental immigration level. I don't see anything counterproductive in blind recruitment. It's a normal practice, along with randomizing resume review in a few big companies in places like India, or China exactly because the issue of…

> Nobody prevents Indian women to apply for H1B jobs in US

Not even perception in India?

Re: Gender, race, and entrepreneurship: A randomized field experiment on investors

#40

Post by sokoloff »

bluecalm wrote: post #18
maccard wrote: post #8

Earlier quoted context omitted.

This is exactly how science should work. You can't perform a study, look at the data and pick a result that suits your bias. You're supposed to suggest a hypothesis, and conduct your experiment. If the results contradict your hypothesis, it doesn't mean the opposite of your hypothesis is true, simply that your hypothrsis was false, and that's ok. It's also more impressive that they have simply published their results…

So you think the data is only relevant to the hypothesis you decided it's relevant for before you collect it? "Have you seen the fire sir?" "I have only seen smoke" "But have you seen the fire?" "It's irrelevant because I was only looking for smoke!". I mean I know they teach it in naive introductory experiment design in some soft science departments but c'mon it's neither smart nor correct. If you need a separate ex…

The reason you don’t collect some data and then see what curves you can fit to it is exactly the problem you describe. “Eureka, a ninth-order polynomial function fits the data quite closely!”

You can’t look at your A/B test results every morning and stop the test the moment one measurement “shows significance”. That’s not how it works.

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