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The Sexual Politics of Compensating Employees

Canada’s long history of employment protections and high level of acceptance for sexual minorities notwithstanding, its labour markets are strung up by sexual orientation.

Gay men, it turns out, earn less than their heterosexual counterparts. Gay women, meanwhile, earn more than straight women working in similar positions.

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This revelation comes courtesy of a just-released study out of McGill University published in the journal Gender & Society.

Using data from the 2006 census and controlling for differences like hours worked, education and occupation, doctoral researchers Nicole Denier and Sean Waite found, on average, that:

  • gay men earn about five percent less than straight men;
  • gay women earn about eight percent more than straight women;
  • gay women earn about nine percent less than straight men (a much smaller gap than the 26 percent that exists between women and men overall)

The largest wage variances were found in the highest-paid occupations, such as those in management and business. And the wage disparity for sexual minorities was smaller or non-existent in the public sector, where a unionized framework wields more control over the pay scale.

“Court cases and attitudinal research show employers and customers, in some cases, have a preference for working with heterosexuals,” Waite offered as explanation in conjunction with the release of his report. “For gay men, we think maybe this is explaining some of the wage disadvantage.”

Additionally, the researchers point out, it’s important to note that four percent more gay men than straight men have part-time jobs, a reality that has an obvious impact on wages.

And as for gay women’s wage-earning advantage, Waite calls that more to do with “perceptions of productivity” related to assumptions about their lack of a family, says Waite.

Waite and Denier want their “wage penalties based on sexual orientation” research to be taken into consideration in future conversations about whether sexual orientation should be included in federal employment equity legislation.

Big Data: It’s Not How Big it is, it’s How You Use it

The recent rush of businesses to get aboard the big data train is a bit like the folks who stock up on exercise equipment and then congratulate themselves for being fit while the unused treadmill lives out its days as a drying rack.

In other words, it’s one thing to have reams of data at your disposal, but quite another to make effective use of it.

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No doubt, the big data revolution has blown wide our view into customers’ preferences, behaviours and brand loyalties and into competitors’ activities and strategies. But until businesses get better at applying analytic insights to their stockpile of info, it’s all for naught. Indeed, a recent study by SAS and the MIT Sloan Management Review identified the ability to translate available analytics into a business action plan as the biggest barrier to success with big data.

The best way to clear the barrier? To approach your data with specific goals in mind.

That means honing in on what you hope your access to this information will do for your company. Do you want to increase market penetration? Boost profits? Find out what people are saying about you? Understand why your customers are leaving you?

In identifying these questions, it’s clever to think in more strategically oriented and forward-looking terms, rather than of questions that are purely operational or historical customer- or product-performance in nature. So instead of taking stock of who your customers are, for example, you might tilt the data to consider who your customers will be.

And always remember the value that technology can lend to your pursuits. The smartest means of parsing your massive stash of data, after all, is to apply the smartest and most sophisticated data-analytics tools to the task.

The gifts big data lays before us are considerable, but only if we make the right demands of it. A study recently conducted by Oracle found that 94 percent of executives heading up companies who use big data are losing money because they’re not leveraging the stuff to the extent they might.

By nominating a list of specific questions about what you hope your data can do for you, you can sidestep joining their ranks.