White Canadians earn more, because they have white privilege.

White Canadians earn more than non-white Canadians, even when comparing only the whites and non-whites with the same education and of the same age. Comparing only the foreign-born white Canadians with the foreign-born non-white Canadians, white people earn more. Comparing only the second-generation, Canadian-born white Canadians with the second-generation, Canadian-born non-white Canadians, white people still earn more.

In other words, even when controlling for age, education, and generation, white Canadians earn more than non-white Canadians. Racial appearance causes the difference in earnings.

Wellesley Institute’s study, Canada’s Colour Coded Labour Market, was released in March 2011 and draws on data from the last mandatory long-form Census (which has been cancelled recently by the politically-conservative Harper government):

THE LAST AVAILABLE CENSUS DATA before the federal government cancelled the country’s mandatory long form Census reveals a troubling trend in Canada.

Despite years of unprecedented economic growth and an increasingly diverse population, this report confirms what so many Canadians have experienced in real life: a colour code is still at work in Canada’s labour market.

Racialized Canadians encounter a persistent colour code that blocks them from the best paying jobs our country has to offer.

[…]

Default explanations like “it takes a while for immigrants to integrate” don’t bear out. Even when you control for age and education, the data show first generation racialized Canadian men earn only 68.7% of what non-racialized first-generation Canadian men earn, indicating a colour code is firmly at play in the labour market. Here, the gender gap — at play throughout the spectrum — becomes disturbingly large: Racialized women immigrants earn only 48.7 cents for every dollar non-racialized male immigrants earn.

The colour code persists for second generation Canadians with similar education and age. The gap narrows, with racialized women making 56.5 cents per dollar non-racialized men earn; while racialized men earn 75.6 cents for every dollar non-racialized men in this cohort earn.

Further Reading:


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Rock Star Programmer: The Charlie Sheen Guide To Passing a Job Interview

In The Charlie Sheen Guide To Passing A Job Interview, John P. Lopez provides a very compelling case that answering interview questions exactly like Charlie Sheen would get you hired. I argue that job seekers applying for the position of a “rock star programmer” at a tech startup would do especially well if they had the aura of Charlie Sheen.

Lopez writes:

Seriously, if you didn’t know the back-story — you didn’t know the trainwreck that Charlie Sheen’s life has become, and the history of drug use and decadence — wouldn’t Sheen’s recent quotes be impressive?

Let’s say you were an employer, looking to add to your sales staff? Wanna play? Here are some typical job interview questions and REAL Charlie Sheen answers.

Admit it, you’d hire the guy if you didn’t know any better:

What is your greatest strength?

“I’m bi-winning. I win here. I win there.”

Describe a typical work week.

”I’m proud of what I created. It was radical. I exposed people to magic. I exposed them to something they’re never going to see in their boring normal lives.”

How many hours do you normally work?

“Sometimes sleep is for infants. I don’t sleep. I wait. When I can’t sleep I don’t fight it. I just figure that there’s a higher calling.”

What is your greatest weakness?

I am on a drug. It’s called ‘Charlie Sheen!’ It’s not available because if you try it once you will die. Your face will melt off and your children will weep over your exploded body.”

[…]

What are your salary expectations?

“I’m not [broke] but I was kind of counting on some of that money to get me through the summer. Now I’ve got to like work. But that’s alright. Work’s good. Work fuels the soul.”

[…]

What do people most often criticize about you?

“You borrow my brain for five seconds and just be like dude, can’t handle it, unplug this bastard. It fires in a way that is, I don’t know, maybe not from this terrestrial realm.

Seriously, there is something very wrong with a culture in which programmers’ outrageous self-descriptions are taken at face value.

Men tend to over-estimate their abilities and self-promote more than women when it comes to math and coding ability. Instead of hiring programmers who act like Charlie Sheen, recruiters and interviewers should take imposter syndrome into account.

Discrimination against job applicants with non-white names continues.

Muslim ‘refused job because of his name’ accuses airline bosses of racism:

A Muslim airport worker has accused airline Cathay Pacific of racism after he was refused a job interview – only to be offered one when he applied two days later using a fake white British-sounding name.

Algerian-born Salim Zakhrouf applied to Cathay Pacific for a job as a passenger services officer at Heathrow Airport.

Mr Zakhrouf, 38, who has lived in Britain since 1991 and is a UK citizen, was told by email he had not been selected for interview.

But applying 48 hours later as ‘Ian Woodhouse’ with an identical CV and home address, he was invited for an interview by the same personnel officer who had first refused him.

via Resist racism, who points out, “For clarity:  My name is not the problem, as others have suggested.  Racism is the problem.”.


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The Hidden Job Market – Whiteness Has Its Privileges

© Copyright 2010 by Joseph Worrell. Reproduced with permission on Restructure!.

In February 2006, The Canadian Labour Congress presented a disturbing study on Canadian workers. The report maintained that Canadian-born visible minorities faced the highest barriers to steady, well-paying jobs of any group in the country.

Post 911 Arab-West Asians came in first with a 14% unemployment rate, Blacks at 11.5% and Latin Americans at 10.5%. Aboriginal Canadians also failed to reap many job rewards but statistics curiously grouped them with unemployed Euro-Canadians.

The Labour Congress’ study caused a bit of quandary, except among those who are already “in the know” about the dilemma.

Leslie Cheung, of Simon Fraser University, declared the report could not disavow “workplace inequality with education disparities because non-White Canadians are better educated as a whole than native-born Whites and immigrants”. The Labour Congress predicts the situation to worsen as huge numbers of non-White young people enter the job market.

Read the rest of this entry »

Women of colour earn 53 cents for every white man’s dollar.

Race, gender remain workplace barriers in Ontario, Census data reveal – Employment equity programs needed to level workplace playing field for visible minorities (Toronto Star):

A new report based on 2005 Census data being released [June 3, 2010], shows that visible minorities in Ontario are far more likely to live in poverty, have trouble finding a job and earn less in the workplace.

Sexism and racial discrimination “pack a double wallop,” for visible minority women who earned 53.4 cents for every dollar a white man earned, said economist Sheila Block who wrote the report for the Canadian Centre for Policy Alternatives.

“The Census data reveals that in 2005, at the height of pre-recession economic prosperity, women from racialized backgrounds working in Ontario faced real barriers to success,” she said. “They earned about half as much as non-racialized men.

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Kyriarchy in Canada: where oppressions intersect

Complaints overwhelm human rights watchdog (Toronto Star):

Ontario’s newly streamlined human rights watchdog is swamped with allegations of sex, race and disability discrimination, the Star has found.

[…]

Tribunal decisions show that women, minorities and the disabled are most vulnerable to discrimination by employers, landlords and businesses. In some cases both the victim and the defendant belong to racial minorities but are from different backgrounds.

One complaint example is of a Chinese doughnut shop owner blatantly expressing her hatred of “Turkish” people and calling a customer a “gypsy”. Another is of a company policy banning three Muslim women from speaking French (which happens to be one of the official languages of our country), as well banning the microwaving of foods that fit the criteria of “You don’t know until you smell.”

Another example:

• A black couple received $5,000 and a letter of apology after they were ignored at a restaurant they had gone to as part of a corporate training session.

After arriving, the couple were asked several times by restaurant staff if they were aware they were standing in a private function area. The couple twice showed them their tickets – and finally propped the tickets on their table.

The waitress ignored them but served drinks to all the white people at the table. Finally, a white person had to order drinks for them. Later, the manager tried to apologize for his staff’s behaviour, saying the black couple was dressed better than the rest of the group and suggesting the woman looked like she could be a “lady of the night.”

At the end of the evening, the manager stopped the couple at the elevators and tried to give them some souvenir boxes, which he said would be good for storing drugs. They told him they didn’t use drugs. The manager insisted they take the boxes.

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Environmental and social barriers restrict women in science, tech, engineering, and math.

Why So Few? Women in Science, Technology, Engineering, and Mathematics (PDF) is a new, publicly-accessible research report by AAUW that “presents in-depth yet accessible profiles of eight key research findings that point to environmental and social barriers – including stereotypes, gender bias and the climate of science and engineering departments in colleges and universities – that continue to block women’s participation and progress in science, technology, engineering, and math.”

The report is quite comprehensive, and summarizes and integrates studies from different research areas. At the end of each chapter are practical recommendations based on research findings. Here is a list of the detailed chapters: Chapter 1: Women and Girls in Science, Technology, Engineering, and Mathematics; Chapter 2: Beliefs about Intelligence; Chapter 3: Stereotypes; Chapter 4: Self-Assessment; Chapter 5: Spatial Skills; Chapter 6: The College Student Experience; Chapter 7: University and College Faculty; Chapter 8: Implicit Bias; Chapter 9: Workplace Bias; Chapter 10: Recommendations.

Commentary in the blogosphere: