Published: | Canada

Understanding the Gender Wage Gap Using Conditional Reasoning

The gender wage gap does exist in our community and society at large, but to reduce this problem only as a consequence of discrimination is the easy way out.

To claim that Tamil women are paid less than Tamil men for doing the same work because of gender discrimination, in my opinion, is a flawed argument. Here’s why:

The gender wage gap does exist in our community and society at large, but to reduce this problem only as a consequence of discrimination is the easy way out. It’s far more difficult to critically think this problem through.

I want to make sense of the gender wage gap using conditional reasoning and a multivariate analysis. Conditional reasoning is commonly used in the logical reasoning sections for the LSAT. It’s a form of logical reasoning based on conditional statements. It follows this approach: if p, then q, q, therefore, p. P is your independent variable, and q is your dependent variable. Let’s propose an argument using this approach.

If you fly (independent variable), then you are a bird (dependent variable). Kawhi Leonard recently flew to Brazil. Therefore, Kawhi Leonard is a bird.

Though this argument follows the approach of conditional reasoning, the conclusion is a false premise. We know that Kawhi Leonard is not a bird. Our argument led us to this conclusion because we accepted the first premise to be true. We make this mistake in our everyday conversations by not questioning the validity of opening claims.

Now let’s test the claim that Tamil women are paid less than Tamil men for doing the same work because of gender discrimination.

If there is income discrimination against Tamil women in the workforce, there will be income disparity between Tamil men and Tamil women. There is income disparity between Tamil men and Tamil women. Therefore, Tamil women are victims of income discrimination in the workforce.

With the previous example, it was easier for us to detect the conclusion as a false premise. Not the case with this example. If we take a closer look at the first premise in this syllogism, it’s not true that “if there is income discrimination against Tamil women in the workforce, there will be income disparity between Tamil men and Tamil women”. By doing a multivariate analysis, it would reveal that income disparity between Tamil men and Tamil women could be a consequence of many factors, some having a larger effect than others. A multivariate regression analysis is an analysis of more than one statistical outcome variable at a time. In order to examine the relationship between gender and income disparity, we need some control variables. These control variables help rule out alternative explanations.

I enjoy using linear regression analysis to find associations related to sports. If I want to examine the association between fantasy sports (FS) participation on gambling related behaviour, and I hypothesize that those who have excessive levels of FS participation are highly likely to develop gambling related behaviour, then I need to make sure those who develop gambling related behaviour in my research are not influenced by other factors. If I don’t control for these other factors, then there’s no plausible explanation for my hypothesis. One could say people develop gambling related problems because of poor mental health, or past gambling related problems, or alcohol abuse - making no case for FS participation being associated with gambling related problems.

Similarly, there are other factors that need to be accounted for when looking at the association between gender and pay disparity. Discrimination does have an effect on pay disparity, on some occasions, but the effect is not commensurate to other factors. Level of education, years of work experience, skill, and so on are other factors (each with different significance levels) we ignore when trying to understand why Tamil women are paid less than Tamil men for doing the same work. Consider the following example.

Kumar and Priya work at XYZ company. They both work as account managers. Kumar has a Masters degree in Engineering, and Priya has a Bachelor’s degree in Humanities. Priya only works three days a week to take care of her little children. Kumar is much more experienced than Priya, and has obtained a certification from the workplace to deal with higher levels of customer complaints. Priya complains to her friends that she gets paid less for doing the same work. Her friends believe her without further investigating. Jessica, one of Priya’s friends plans to make her story go viral on Instagram.

Most people will conclude that Priya was a victim of gender discrimination without understanding the details of her story. Physiologists say that human beings are wired to interpret new information to match our beliefs and reject it if it runs contrary to it. What we need to understand independent of dogmatic beliefs is that on average, higher levels of education pays more money (this also varies based on university, field of study), people who work longer hours get paid more, and higher skill levels get rewarded.

I’m aware that on some occasions women do get paid less than men for equal work because of gender discrimination. But to claim that the gender wage gap is only a consequence of gender discrimination is not a convincing explanation.

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