Google Manifesto #GoogleManifesto

Bei Google soll intern ein „Manifesto“ zur dortigen Diversitypolitik umhergehen, dass von einem anonymen Mitarbeiter erstellt worden ist.

Es handelt sich um diesen Text:

Reply to public response and misrepresentation

I value diversity and inclusion, am not denying that sexism exists, and don’t endorse using stereotypes. When addressing the gap in representation in the population, we need to look at population level differences in distributions. If we can’t have an honest discussion about this, then we can never truly solve the problem. Psychological safety is built on mutual respect and acceptance, but unfortunately our culture of shaming and misrepresentation is disrespectful and unaccepting of anyone outside its echo chamber. Despite what the public response seems to have been, I’ve gotten many personal messages from fellow Googlers expressing their gratitude for bringing up these very important issues which they agree with but would never have the courage to say or defend because of our shaming culture and the possibility of being fired. This needs to change.


  • Google’s political bias has equated the freedom from offense with psychological safety, but shaming into silence is the antithesis of psychological safety.
  • This silencing has created an ideological echo chamber where some ideas are too sacred to be honestly discussed.
  • The lack of discussion fosters the most extreme and authoritarian elements of this ideology.
  • Extreme: all disparities in representation are due to oppression
  • Authoritarian: we should discriminate to correct for this oppression
  • Differences in distributions of traits between men and women may in part explain why we don’t have 50% representation of women in tech and leadership. Discrimination to reach equal representation is unfair, divisive, and bad for business.

Background [1]

People generally have good intentions, but we all have biases which are invisible to us. Thankfully, open and honest discussion with those who disagree can highlight our blind spots and help us grow, which is why I wrote this document.[2] Google has several biases and honest discussion about these biases is being silenced by the dominant ideology. What follows is by no means the complete story, but it’s a perspective that desperately needs to be told at Google.

Google’s biases

At Google, we talk so much about unconscious bias as it applies to race and gender, but we rarely discuss our moral biases. Political orientation is actually a result of deep moral preferences and thus biases. Considering that the overwhelming majority of the social sciences, media, and Google lean left, we should critically examine these prejudices.

Left Biases

  • Compassion for the weak
  • Disparities are due to injustices
  • Humans are inherently cooperative
  • Change is good (unstable)
  • Open
  • Idealist

Right Biases

  • Respect for the strong/authority
  • Disparities are natural and just
  • Humans are inherently competitive
  • Change is dangerous (stable)
  • Closed
  • Pragmatic

Neither side is 100% correct and both viewpoints are necessary for a functioning society or, in this case, company. A company too far to the right may be slow to react, overly hierarchical, and untrusting of others. In contrast, a company too far to the left will constantly be changing (deprecating much loved services), over diversify its interests (ignoring or being ashamed of its core business), and overly trust its employees and competitors.

Only facts and reason can shed light on these biases, but when it comes to diversity and inclusion, Google’s left bias has created a politically correct monoculture that maintains its hold by shaming dissenters into silence. This silence removes any checks against encroaching extremist and authoritarian policies. For the rest of this document, I’ll concentrate on the extreme stance that all differences in outcome are due to differential treatment and the authoritarian element that’s required to actually discriminate to create equal representation.

Possible non-bias causes of the gender gap in tech [3]

At Google, we’re regularly told that implicit (unconscious) and explicit biases are holding women back in tech and leadership. Of course, men and women experience bias, tech, and the workplace differently and we should be cognizant of this, but it’s far from the whole story.

On average, men and women biologically differ in many ways. These differences aren’t just socially constructed because:

  • They’re universal across human cultures
  • They often have clear biological causes and links to prenatal testosterone
  • Biological males that were castrated at birth and raised as females often still identify and act like males
  • The underlying traits are highly heritable
  • They’re exactly what we would predict from an evolutionary psychology perspective

Note, I’m not saying that all men differ from women in the following ways or that these differences are “just.” I’m simply stating that the distribution of preferences and abilities of men and women differ in part due to biological causes and that these differences may explain why we don’t see equal representation of women in tech and leadership. Many of these differences are small and there’s significant overlap between men and women, so you can’t say anything about an individual given these population level distributions.

Personality differences

Women, on average, have more:

  • Openness directed towards feelings and aesthetics rather than ideas. Women generally also have a stronger interest in people rather than things, relative to men (also interpreted as empathizing vs. systemizing).
  • These two differences in part explain why women relatively prefer jobs in social or artistic areas. More men may like coding because it requires systemizing and even within SWEs, comparatively more women work on front end, which deals with both people and aesthetics.
  • Extraversion expressed as gregariousness rather than assertiveness. Also, higher agreeableness.
  • This leads to women generally having a harder time negotiating salary, asking for raises, speaking up, and leading. Note that these are just average differences and there’s overlap between men and women, but this is seen solely as a women’s issue. This leads to exclusory programs like Stretch and swaths of men without support.
  • Neuroticism (higher anxiety, lower stress tolerance).This may contribute to the higher levels of anxiety women report on Googlegeist and to the lower number of women in high stress jobs.

Note that contrary to what a social constructionist would argue, research suggests that “greater nation-level gender equality leads to psychological dissimilarity in men’s and women’s personality traits.” Because as “society becomes more prosperous and more egalitarian, innate dispositional differences between men and women have more space to develop and the gap that exists between men and women in their personality becomes wider.” We need to stop assuming that gender gaps imply sexism.

Men’s higher drive for status

We always ask why we don’t see women in top leadership positions, but we never ask why we see so many men in these jobs. These positions often require long, stressful hours that may not be worth it if you want a balanced and fulfilling life.

Status is the primary metric that men are judged on[4], pushing many men into these higher paying, less satisfying jobs for the status that they entail. Note, the same forces that lead men into high pay/high stress jobs in tech and leadership cause men to take undesirable and dangerous jobs like coal mining, garbage collection, and firefighting, and suffer 93% of work-related deaths.

Non-discriminatory ways to reduce the gender gap

Below I’ll go over some of the differences in distribution of traits between men and women that I outlined in the previous section and suggest ways to address them to increase women’s representation in tech and without resorting to discrimination. Google is already making strides in many of these areas, but I think it’s still instructive to list them:

  • Women on average show a higher interest in people and men in things
  • We can make software engineering more people-oriented with pair programming and more collaboration. Unfortunately, there may be limits to how people-oriented certain roles and Google can be and we shouldn’t deceive ourselves or students into thinking otherwise (some of our programs to get female students into coding might be doing this).
  • Women on average are more cooperative
  • Allow those exhibiting cooperative behavior to thrive. Recent updates to Perf may be doing this to an extent, but maybe there’s more we can do. This doesn’t mean that we should remove all competitiveness from Google. Competitiveness and self reliance can be valuable traits and we shouldn’t necessarily disadvantage those that have them, like what’s been done in education. Women on average are more prone to anxiety. Make tech and leadership less stressful. Google already partly does this with its many stress reduction courses and benefits.
  • Women on average look for more work-life balance while men have a higher drive for status on average
  • Unfortunately, as long as tech and leadership remain high status, lucrative careers, men may disproportionately want to be in them. Allowing and truly endorsing (as part of our culture) part time work though can keep more women in tech.
  • The male gender role is currently inflexible
  • Feminism has made great progress in freeing women from the female gender role, but men are still very much tied to the male gender role. If we, as a society, allow men to be more “feminine,” then the gender gap will shrink, although probably because men will leave tech and leadership for traditionally feminine roles.

Philosophically, I don’t think we should do arbitrary social engineering of tech just to make it appealing to equal portions of both men and women. For each of these changes, we need principles reasons for why it helps Google; that is, we should be optimizing for Google—with Google’s diversity being a component of that. For example currently those trying to work extra hours or take extra stress will inevitably get ahead and if we try to change that too much, it may have disastrous consequences. Also, when considering the costs and benefits, we should keep in mind that Google’s funding is finite so its allocation is more zero-sum than is generally acknowledged.

The Harm of Google’s biases

I strongly believe in gender and racial diversity, and I think we should strive for more. However, to achieve a more equal gender and race representation, Google has created several discriminatory practices:

  • Programs, mentoring, and classes only for people with a certain gender or race [5]
  • A high priority queue and special treatment for “diversity” candidates
  • Hiring practices which can effectively lower the bar for “diversity” candidates by decreasing the false negative rate
  • Reconsidering any set of people if it’s not “diverse” enough, but not showing that same scrutiny in the reverse direction (clear confirmation bias)
  • Setting org level OKRs for increased representation which can incentivize illegal discrimination [6]

These practices are based on false assumptions generated by our biases and can actually increase race and gender tensions. We’re told by senior leadership that what we’re doing is both the morally and economically correct thing to do, but without evidence this is just veiled left ideology[7] that can irreparably harm Google.

Why we’re blind

We all have biases and use motivated reasoning to dismiss ideas that run counter to our internal values. Just as some on the Right deny science that runs counter to the “God > humans > environment” hierarchy (e.g., evolution and climate change) the Left tends to deny science concerning biological differences between people (e.g., IQ[8] and sex differences). Thankfully, climate scientists and evolutionary biologists generally aren’t on the right. Unfortunately, the overwhelming majority of humanities and social scientists learn left (about 95%), which creates enormous confirmation bias, changes what’s being studied, and maintains myths like social constructionism and the gender wage gap[9]. Google’s left leaning makes us blind to this bias and uncritical of its results, which we’re using to justify highly politicized programs.

In addition to the Left’s affinity for those it sees as weak, humans are generally biased towards protecting females. As mentioned before, this likely evolved because males are biologically disposable and because women are generally more cooperative and areeable than men. We have extensive government and Google programs, fields of study, and legal and social norms to protect women, but when a man complains about a gender issue issue [sic] affecting men, he’s labelled as a misogynist and whiner[10]. Nearly every difference between men and women is interpreted as a form of women’s oppression. As with many things in life, gender differences are often a case of “grass being greener on the other side”; unfortunately, taxpayer and Google money is spent to water only one side of the lawn.

The same compassion for those seen as weak creates political correctness[11], which constrains discourse and is complacent to the extremely sensitive PC-authoritarians that use violence and shaming to advance their cause. While Google hasn’t harbored the violent leftists protests that we’re seeing at universities, the frequent shaming in TGIF and in our culture has created the same silence, psychologically unsafe environment.


I hope it’s clear that I’m not saying that diversity is bad, that Google or society is 100% fair, that we shouldn’t try to correct for existing biases, or that minorities have the same experience of those in the majority. My larger point is that we have an intolerance for ideas and evidence that don’t fit a certain ideology. I’m also not saying that we should restrict people to certain gender roles; I’m advocating for quite the opposite: treat people as individuals, not as just another member of their group (tribalism).

My concrete suggestions are to:

De-moralize diversity.

  • As soon as we start to moralize an issue, we stop thinking about it in terms of costs and benefits, dismiss anyone that disagrees as immoral, and harshly punish those we see as villains to protect the “victims.”

Stop alienating conservatives.

  • Viewpoint diversity is arguably the most important type of diversity and political orientation is one of the most fundamental and significant ways in which people view things differently.
  • In highly progressive environments, conservatives are a minority that feel like they need to stay in the closet to avoid open hostility. We should empower those with different ideologies to be able to express themselves.
  • Alienating conservatives is both non-inclusive and generally bad business because conservatives tend to be higher in conscientiousness, which is require for much of the drudgery and maintenance work characteristic of a mature company.

Confront Google’s biases.

  • I’ve mostly concentrated on how our biases cloud our thinking about diversity and inclusion, but our moral biases are farther reaching than that.
  • I would start by breaking down Googlegeist scores by political orientation and personality to give a fuller picture into how our biases are affecting our culture.

Stop restricting programs and classes to certain genders or races.

  • These discriminatory practices are both unfair and divisive. Instead focus on some of the non-discriminatory practices I outlined.

Have an open and honest discussion about the costs and benefits of our diversity programs.

  • Discriminating just to increase the representation of women in tech is as misguided and biased as mandating increases for women’s representation in the homeless, work-related and violent deaths, prisons, and school dropouts.
  • There’s currently very little transparency into the extend of our diversity programs which keeps it immune to criticism from those outside its ideological echo chamber.
  • These programs are highly politicized which further alienates non-progressives.
  • I realize that some of our programs may be precautions against government accusations of discrimination, but that can easily backfire since they incentivize illegal discrimination.

Focus on psychological safety, not just race/gender diversity.

  • We should focus on psychological safety, which has shown positive effects and should (hopefully) not lead to unfair discrimination.
  • We need psychological safety and shared values to gain the benefits of diversity
  • Having representative viewpoints is important for those designing and testing our products, but the benefits are less clear for those more removed from UX.

De-emphasize empathy.

  • I’ve heard several calls for increased empathy on diversity issues. While I strongly support trying to understand how and why people think the way they do, relying on affective empathy—feeling another’s pain—causes us to focus on anecdotes, favor individuals similar to us, and harbor other irrational and dangerous biases. Being emotionally unengaged helps us better reason about the facts.

Prioritize intention.

  • Our focus on microaggressions and other unintentional transgressions increases our sensitivity, which is not universally positive: sensitivity increases both our tendency to take offense and our self censorship, leading to authoritarian policies. Speaking up without the fear of being harshly judged is central to psychological safety, but these practices can remove that safety by judging unintentional transgressions.
  • Microaggression training incorrectly and dangerously equates speech with violence and isn’t backed by evidence.

Be open about the science of human nature.

  • Once we acknowledge that not all differences are socially constructed or due to discrimination, we open our eyes to a more accurate view of the human condition which is necessary if we actually want to solve problems.

Reconsider making Unconscious Bias training mandatory for promo committees.

  • We haven’t been able to measure any effect of our Unconscious Bias training and it has the potential for overcorrecting or backlash, especially if made mandatory.
  • Some of the suggested methods of the current training (v2.3) are likely useful, but the political bias of the presentation is clear from the factual inaccuracies and the examples shown.
  • Spend more time on the many other types of biases besides stereotypes. Stereotypes are much more accurate and responsive to new information than the training suggests (I’m not advocating for using stereotypes, I [sic] just pointing out the factual inaccuracy of what’s said in the training).

[1] This document is mostly written from the perspective of Google’s Mountain View campus, I can’t speak about other offices or countries.

[2] Of course, I may be biased and only see evidence that supports my viewpoint. In terms of political biases, I consider myself a classical liberal and strongly value individualism and reason. I’d be very happy to discuss any of the document further and provide more citations.

[3] Throughout the document, by “tech”, I mostly mean software engineering.

[4] For heterosexual romantic relationships, men are more strongly judged by status and women by beauty. Again, this has biological origins and is culturally universal.

[5] Stretch, BOLD, CSSI, Engineering Practicum (to an extent), and several other Google funded internal and external programs are for people with a certain gender or race.

[6] Instead set Googlegeist OKRs, potentially for certain demographics. We can increase representation at an org level by either making it a better environment for certain groups (which would be seen in survey scores) or discriminating based on a protected status (which is illegal and I’ve seen it done). Increased representation OKRs can incentivize the latter and create zero-sum struggles between orgs.

[7] Communism promised to be both morally and economically superior to capitalism, but every attempt became morally corrupt and an economic failure. As it became clear that the working class of the liberal democracies wasn’t going to overthrow their “capitalist oppressors,” the Marxist intellectuals transitioned from class warfare to gender and race politics. The core oppressor-oppressed dynamics remained, but now the oppressor is the “white, straight, cis-gendered patriarchy.”

[8] Ironically, IQ tests were initially championed by the Left when meritocracy meant helping the victims of the aristocracy.

[9] Yes, in a national aggregate, women have lower salaries than men for a variety of reasons. For the same work though, women get paid just as much as men. Considering women spend more money than men and that salary represents how much the employees sacrifices (e.g. more hours, stress, and danger), we really need to rethink our stereotypes around power.

[10] “The traditionalist system of gender does not deal well with the idea of men needing support. Men are expected to be strong, to not complain, and to deal with problems on their own. Men’s problems are more often seen as personal failings rather than victimhood,, due to our gendered idea of agency. This discourages men from bringing attention to their issues (whether individual or group-wide issues), for fear of being seen as whiners, complainers, or weak.”

[11] Political correctness is defined as “the avoidance of forms of expression or action that are perceived to exclude, marginalize, or insult groups of people who are socially disadvantaged or discriminated against,” which makes it clear why it’s a phenomenon of the Left and a tool of authoritarians.

Update 7:25pm ET: Google’s new Vice President of Diversity, Integrity & Governance, Danielle Brown, issued the following statement in response to the internal employee memo:


I’m Danielle, Google’s brand new VP of Diversity, Integrity & Governance. I started just a couple of weeks ago, and I had hoped to take another week or so to get the lay of the land before introducing myself to you all. But given the heated debate we’ve seen over the past few days, I feel compelled to say a few words.

Many of you have read an internal document shared by someone in our engineering organization, expressing views on the natural abilities and characteristics of different genders, as well as whether one can speak freely of these things at Google. And like many of you, I found that it advanced incorrect assumptions about gender. I’m not going to link to it here as it’s not a viewpoint that I or this company endorses, promotes or encourages.

Diversity and inclusion are a fundamental part of our values and the culture we continue to cultivate. We are unequivocal in our belief that diversity and inclusion are critical to our success as a company, and we’ll continue to stand for that and be committed to it for the long haul. As Ari Balogh said in his internal G+ post, “Building an open, inclusive environment is core to who we are, and the right thing to do. ‘Nuff said. “

Google has taken a strong stand on this issue, by releasing its demographic data and creating a company wide OKR on diversity and inclusion. Strong stands elicit strong reactions. Changing a culture is hard, and it’s often uncomfortable. But I firmly believe Google is doing the right thing, and that’s why I took this job.

Part of building an open, inclusive environment means fostering a culture in which those with alternative views, including different political views, feel safe sharing their opinions. But that discourse needs to work alongside the principles of equal employment found in our Code of Conduct, policies, and anti-discrimination laws.

I’ve been in the industry for a long time, and I can tell you that I’ve never worked at a company that has so many platforms for employees to express themselves—TGIF, Memegen, internal G+, thousands of discussion groups. I know this conversation doesn’t end with my email today. I look forward to continuing to hear your thoughts as I settle in and meet with Googlers across the company.



Bei Gizmodo, siehe den Link oben, heißt es:

The text of the post is reproduced in full below, with some minor formatting modifications. Two charts and several hyperlinks are also omitted.

Gerade die Charts und die Links, vermutlich auf Studien, wären natürlich interessant gewesen.

Das Video dazu habe ich gefunden:

ich ergänze mal auf die Schnelle ein paar Links von mir:

Women, on average, have more:


„Frauen erhalten auch bei Ebay weniger für die gleichen Artikel“

Eine interessante Studie hat untersucht, ob sich das Geschlecht bei Ebay auswirkt:

Gender inequality in contemporary U.S. society is a well-documented, widespread phenomenon. However, little is known about gender disparities in product markets. This study is the first to use actual market data to study the behavior of women and men as sellers and buyers and differences in market outcomes. We analyze a unique and large data set containing all eBay auction transactions of most popular products by private sellers between the years 2009 and 2012. Women sellers received a smaller number of bids and lower final prices than did equally qualified men sellers of the exact same product. On average, women sellers received about 80 cents for every dollar a man received when selling the identical new product and 97 cents when selling the same used product. These findings held even after controlling for the sentiments that appear in the text of the sellers’ listings. Nonetheless, it is worth noting that this gap varied by the type of the product being sold. As a policy, eBay does not reveal the gender of users. We attribute the price differences to the ability of buyers to discern the gender of the seller. We present results from an experiment that shows that people accurately identify the gender of sellers on the basis of typical information provided in postings. We supplement the analysis with an additional off-eBay experiment showing that, in a controlled setting, people are willing to pay less for money-value gift cards when they are sold by women rather than men.

Quelle: How many cents on the dollar? Women and men in product markets

Demnach würden also Männer bei neuen Produkt mehr bekommen, nämlich einen Dollar, während Frauen nur 80 Cent erhalten, bei gebrauchten erhalten Frauen fast das gleiche, nur 97 Cent für den männlichen Dollar.

Die Studie ist allerdings erheblicher Kritik ausgesetzt. Bereits die Bestimmung des Geschlechts ist dabei sehr schwierig:

In addition, the study has an Achilles heel. While the authors claim to know the gender of eBay sellers, they admit that most sellers don’t publicly identify their genders:

“Potential buyers do not receive direct information from eBay about the sellers’ gender. Yet, the gender of a private seller can be gleaned from the range of items a merchant is offering for sale (for example, selling female clothing suggests that the seller is likely a woman) and, at times, from the seller’s username.”
The authors want to attribute their findings to gender discrimination, but they can’t unless they can prove that buyers can figure out the gender of anonymous eBay sellers. So the authors are trying to claim that buyers routinely accurately guess the gender of a seller (again – assuming the selling actually has one gender).

“Four hundred people participated in the experiment, each evaluating five user profiles. Of the 2000 evaluations, the gender was correctly identified in 1127 cases and mistakenly identified in 170. In 701 cases, participants reported that they could not discern whether the sellers were male or female”
So 170 (mistaken gender) + 701 (could not discern) = 871 (couldn’t correctly guess gender). 871 (couldn’t correctly guess gender)/1127 (could correctly guess gender) = 56.35% chance can correctly guess gender

1127 (could correctly guess gender) + 170 (incorrectly guessed gender) = 1297 (could make any guess about gender)/2000 = 64.85% chance confident enough to make make any guess (true/false) about gender.

A 56.35% chance respondents could guess the correct gender and a 64.85% respondents felt confident enough to make any guess regarding gender. This doesn’t seem like a very convincing argument that buyers consistently guess a seller’s gender, especially when guessing gender is already only a 50% coin toss. Indeed, it shows that over one-third (100%-64.85%=35.15%) of the time a buyer doesn’t even feel confident enough to guess a seller’s gender. Also, none of this actually proves that actual buyers try to guess the gender of the seller or that it affects their bidding decisions.

Zudem sind auch andere Faktoren nicht gleich:

The study claims:

“Auctions are ideal for testing for gender differences in outcomes because, after an item has been listed, the final price is not affected by the seller’s behavior, only by the bidding of potential buyers.”
This is not true. There are numerous other variables that could affect a buyer’s bid: the sellers rating, the sellers return policy, the seller’s page layout, the product description, shipping fees, shipping location, warranties, pictures of the item, whether the buyer is currently bidding on competing items, time left in the auction, number of other bids, number of competing auctions currently available, etc.

These seem much more obvious and likely decisive factors than what gender a buyer thinks a seller might be. The study claims it controls for many (but not all) of these of possible factors, but I don’t see how they could possible effectively control for qualitative subjective variables such as the quality of a product photo or product description. They also don’t appear to include major factors like return policies or warranties.

Furthermore, the study finds that men and women tend to sell very different items. Women also tend to sell cheaper items and set lower starting bids. In addition, female sellers only make up only 23.07% of the sellers in the study’s dataset. So even more statistical voodoo is used to try to make data on male and female sellers comparable.

Wenn ich es richtig gelesen habe, dann haben die Männer den Startpreis 15% höher angesetzt und auch bei der Einstellung des Angebots doppelt so häufig Titel mit Fettdruck verwendet, 50% mehr Fotos eingestellt, wobei sie bei den Fotos 33% Prozent weniger „Stockfotos“, also Bilder, die anderweitig als bei dem konkreten Produkt erstellt worden sind, verwendet haben.

In der Studie wurden dann noch ein direkter Test gemacht, wie sich ein weiblicher oder männlicher Name auf den Verkauf von Gutscheinkarten auswirkt:

To test whether people evaluate products that women sell as less valuable than the same products when sold by men, we conducted another experiment on Amazon Mechanical Turk, asking participants to report the monetary value they assign to an Amazon $100 money-value gift card (“How much are you willing to pay?”) when sold by either Alison or Brad.

As shown in Fig. 2, the results of the experiment support the hypothesis that a lower value is assigned to products when sold by women than by men. One hundred sixteen people participated in the experiment; 59 were asked to report their evaluation of a $100 gift card sold by Alison, and 57 were asked to report their evaluation of a $100 gift card sold by Brad. The average value assigned to the gift card sold by a woman was $83.34, whereas the average value assigned to the same card sold by a man was $87.42 (P < 0.05; see table S10). Recall that similar differences in price between women and men sellers were found when we analyzed transactions of gift cards on eBay (Table 2). (The gender of the participants in the experiments did not affect the final price, nor did it affect the differences between the prices of gift cards sold by a woman and the prices of gift cards sold by a man.)

Hier die Daten von diesem Experiment:

Geschenkgutschein Mann Frau Ebay

Geschenkgutschein Mann Frau Ebay

Da scheint mir gleichzeitig die Streuung etwas höher zu sein bei den Frauen, die Teilnehmerzahl ist nicht sehr hoch, es wäre interessant, ob die Ergebnisse damit zusammenhängen könnten, dass da mehr Abweichungen nach oben und nach unten vorhanden waren und wie groß da die Bedeutung einzelner Angebote war.

Auch so gibt es recht wenig über das Experiment: Waren in beiden Gruppen gleich viele Männer und Frauen? Was für Gutscheine waren es? (wenn zB eine Frau einen Gutschein für einen Frauenuntypischen Bereich verkauft, dann könnte ein Käufer davon ausgehen, dass sie ihm einen geringeren Wert zuweist. Wie waren die Texte? Waren sie bei beiden gleich? Wie hätten sie mit anderen Namen abgeschnitten? Beschwört vielleicht Brad das Bild eines härteren Verhandlers herauf als ein anderer Männername?)


Formen der Diskriminierung

Dummerjan schreibt in einem Kommentar:

Es erscheint mir daher vernünftig einmal einige Diskriminierungsbegriffe zu sammeln und zu formulieren.

Ich fange mal mit dem einfachsten an:

Ökonomische Diskriminierung (nach Gary Becker):

Eine Gruppe von Menschen diskriminiert eine andere, wenn sie direkt oder indirekt Einkommens- oder Nutzenverluste in Kauf nimmt, um den Kontakt mit dieser Menschengruppe zu vermeiden oder zu minimieren. Entsprechende Zahlungen/Nutzenverluste um mit dieser Menschengruppe in Kontakt zu treten nennt man Paternalisierung.

Die Gruppe mit denen der Kontakt vermieden werden soll, nennt man “ökonomisch diskriminiert”, die Kontakt vermeidende Gruppe “diskriminierend”.

Statistische Diskriminierung:

Eine Menschengruppe wird “statistisch diskriminiert”, wenn bei dieser unabhängig von deren Anstrengungsniveau immer das Entlohnungsschema gewählt wird, dass das minimale Anstrengungsniveau entlohnt.

Rechtliche Dskriminierung:

Rechtliche Diskriminierung liegt vor, wenn eine Menschengruppe rechtlich anders behandelt wird als eine andere, und diese Ungleichbehandlung unabhängig vom rechtlichen Inhalt erfolgt, bzw. nicht rechtlich oder inhaltlich kausal begründet ist.

Institutionelle Diskriminierung:

Strukturelle Diskriminierung:

Ich ergänze mal die unteren beiden nach der Wikipedia:

Institutionelle Diskriminierung:

Als Institutionelle Diskriminierung werden in der politischen Theorie gesellschaftliche Phänomene bezeichnet, denen zugleich diskriminierender und institutioneller Charakter zugeschrieben wird. Sie wird verstanden als Ergebnis von organisatorischem Handeln in einem Netzwerk gesellschaftlicher Institutionen. Der potentielle Ort institutioneller Diskriminierung wird in den formalen Rechten, den organisatorischen Strukturen, Programmen und Routinen von Institutionen ausgemacht.

Im Macpherson-Report wird institutioneller Rassismus definiert als das „kollektive Versagen einer Organisation, angemessene und professionelle Dienstleistungen für Personen wegen ihrer Hautfarbe, Kultur oder ethnischen Herkunft anzubieten. Dies kann in Entwicklungen gesehen oder festgestellt werden. Abwertende Einstellungen und Handlungsweisen tragen zur Diskriminierung und der Benachteiligung Angehöriger ethnischer Minderheiten bei. Dies erfolgt unwissentlich durch Vorurteile, Ignoranz, Gedankenlosigkeit und rassistische Stereotypisierungen.“ [Macpherson-Report 1999] Beachtenswert an dieser Definition ist, dass nicht nur offen diskriminierende/rassistische Handlungen als solche benannt werden, sondern das gemeinschaftliche Handeln von Institutionsmitarbeitenden gegenüber ethnischen Minderheiten in das Zentrum der Aufmerksamkeit gerückt wird. Gibt es generell benachteiligende und unprofessionelle Handlungspraxen gegenüber Minderheitenangehörigen, handelt es sich nach dieser Definition um institutionellen Rassismus. In einigen Punkten ist Macphersons Definition ergänzungsbedürftig: Diskriminierungen können nicht nur unbeabsichtigt und unbewusst, sondern auch durch bewusste, wissentliche Ausgrenzungen, Vorurteile und Ignoranz erfolgen. Das kollektive Versagen erfolgt nicht WEGEN der ‚Hautfarbe‘, ‚Kultur‘ oder ‚ethnischen Herkunft‘, sondern aufgrund der Konstruktion und Abwertung von Gruppen und den damit verbundenen Handlungen.

Nicht das Nicht-Beachten der ‚Hautfarbe‘ kann das Ziel von Antidiskriminierung sein, sondern eine Veränderung der Einteilungsmuster, Zuschreibungen und Wertungen, die auf bestimmte Hautfarben und Physiognomien zielen sowie die damit verbundenen Ausgrenzungshandlungen und -mechanismen. Außerdem können Diskriminierungen nicht nur durch das unprofessionelle Handeln von Mitarbeitenden erfolgen, sondern auch durch die professionelle Umsetzung von diskriminierenden Gesetzen, Erlassen, Verordnungen und (Zugangs-)Regeln. Unklar bleibt auch, unter welchen Kriterien von Institutionenmitarbeitenden mehrfach ausgeübte ausgrenzende Handlungen gegenüber ethnisierten oder rassialisierten Personen als kollektiv zu bezeichnen sind.

Unter diesen Gesichtspunkten schlägt Claus Melter eine neue Definition von institutionellem Rassismus vor: „Institutioneller Rassismus in Deutschland ist von Institutionen/Organisationen (durch Gesetze, Erlasse, Verordnungen und Zugangsregeln sowie Arbeitsweisen, Verfahrensregelungen und Prozessabläufe) oder durch systematisch von Mitarbeitern der Institutionen/Organisationen ausgeübtes oder zugelassenes ausgrenzendes, benachteiligendes oder unangemessenes und somit unprofessionelles Handeln gegenbüer ethnisierten, rassialisierten, kulturalisierten Personen oder Angehörigen religiöser Gruppen sowie gegenüber so definierten ‚Nicht-Deutschen‘ oder Nicht-Christen.

Strukturelle Diskriminierung:

Als Strukturelle Diskriminierung werden die Formen von Diskriminierung gesellschaftlicher Gruppen, die in der Beschaffenheit der Struktur der Gesamtgesellschaft immanent begründet liegen, bezeichnet. Das Gegenstück zu Struktureller Diskriminierung stellt die Interaktionelle Diskriminierung dar.

Ausgangspunkt sind Normen und Regeln, die für alle Gesellschaftsteile gleichermaßen gelten. Sie ziehen strukturelle Diskriminierung nach sich, wenn durch ihre Anwendung in Form von Haltungen oder Handlungen gesellschaftliche Teilgruppen gravierender Ungleichbehandlung ausgesetzt sind. Die Psychologin Ute Osterkamp stellt beispielsweise für den Rassismus fest, „dass rassistische Denk- und Handlungsweisen nicht Sache der persönlichen Einstellungen von Individuen, sondern in der Organisation des gesellschaftlichen Miteinanders verortet sind, welche die Angehörigen der eigenen Gruppe systematisch gegenüber den Nicht-Dazugehörigen privilegieren“. Strukturelle Diskriminierung beruht auf eingespielten und dauerhaften, oft formalisierten und explizit geregelten institutionellen Praktiken.

„Männerhass = irritierte Männer, Frauenhass = tote Frauen“

Das hier bereits einmal besprochene Argument ist mir nun noch einmal bei Twitter über den Weg gelaufen:


Der Text sicherheitshalber noch einmal

Misandry → irritated men.

Misogyny → dead women.

That’s the difference.

Erstaunlich, dass man so etwas tatsächlich denken kann. Denn viele Beeinträchtigungen von Männern treffen diese ja sehr deutlich, von „4 Tote, darunter auch Frauen und Kinder“, dem Wehrdienst, dem Umstand, dass von Männern auch von Frauen Schutz eingefordert wird oder die Abwehr von Gefahren und auch die Entziehung von Kindern oder eine Falschbeschuldigung führt nicht zu einer leichten irritation, sondern zu handfesten Nachteilen.

Zudem sollte gerade im Netzfeminismus auch sehr deutlich werden, dass nicht alles, was dort als Misogyny angesehen wird – ein Blick, ein Kinofilm, das Vorhandensein von Kindern – gleich tödlich ist. Natürlich kann man anführen, dass wegen dieser Einstellung, die sich in Kleinigkeiten zeigt, Frauen getötet werden. Aber das ist ja auch bei Männern so: Wenn man meint, dass ein Mann nichts wert ist, wenn er seine Freundin nicht beschützt, dann führt das eben langfristig ebenso zu Toten.

Wie prüft man, ob eine Diskriminierung wegen des Geschlechts vorliegt?

Es wäre aus meiner Sicht interessant einmal ein Schema für die Prüfung, wann eine Diskriminierung wegen des Geschlechts vorliegt, vorzunehmen?

Die Kriterien, die mir dazu einfallen, wären:

  1. Liegt eine Abweichung zwischen Männern und Frauen vor?
  2. Welche Gründe bestehen für diese Abweichung? Welche Umstände begründen die Abweichung
  3. Rechtfertigen die Gründe/Umstände eine Abweichung?
  4. Kann man es ändern und mit welchen Kosten

Bei der Anwendung dieses Schemas würde  man meine ich eher zum Kern der Sache vordringen.

Man könnte dann diskutieren, inwieweit es Gründe für den Gender-Gap beim Gehalt gibt (Männer sind eher auf Karriere ausgerichtet, setzen weniger aus, machen mehr überstunden etc) oder ob es Gründe dafür gibt, dass Frauen eher nach einer Scheidung die Kinder bekommen (Kontinuitätsprinzip, Frauen übernehmen auch vorher im größeren Umfang die Betreuung der Kinder) oder ob es Gründe dafür gibt, dass Männer mehr Arbeitsunfälle erleiden (Frauen suchen bewußt sichere Jobs, Jobs die mehr Kraft erfordern haben auch häufig ein höheres Schadenspotential etc). Bei dem Punkt 3 ist natürlich auch einiges an subjektiver Wertung möglich. Und Punkt 4 kann schließlich Debatten dazu eröffnen, welche Kosten entstehen und ob eine Änderung überhaupt möglich ist. Dabei wäre auch zu prüfen, ob die Leute es überhaupt wollen (weil es die Kosten erhöht) und welchen sozialen Druck man ausüben muss, um eine Verhaltensänderung entgegen evtl biologischer Dispositionen zu bewirken.