Microeconomics Perspectives on the Study of Racial Descrimination
It is a great idea to examine other social sciences through the lens of economics to truly test where economic theory shines through and where it breaks down to capture what succumbs in reality. One closely related subject that aims to combine economics with psychology and philosophy is sociology, whose central goal to study all human societal structures through the insight of its many epistemological derivatives. In this specific case, we aim to use our background in microeconomics to understand how sociologists study and classify different sources and causes of racial discrimination. I believe that this is in fact a great way to view sociology. Microeconomics aims to describe what happens on the minute details of how preferences work and how individuals and firms choose to allocate their resources. Microeconomics can help to showcase how discrimination occurs in the labor market, in the government, and where individuals conduct their business. Racism and discrimination may be a problem that is grandiose in magnitude, but at the end of the day, it is the summation of many interactions between individuals on a smaller scale. This is comparable to how macroeconomics can be inferred to be the summation of many of the key players in microeconomics: firms, individuals, and governments. This economic viewpoint of racial discrimination often wears down in the face of actuality. Assumptions such as having perfect information exchange in a labor market are a far cry from what happens day-to-day.
One of the underlying themes of this piece that really stood out was the dichotomy between the causal and the anecdotal. What caught my eye about this major theme was a recent trend that was noted. Sociologists tended to “prioritize the ability to make convincing causal claims, ” possibly over the ability to bring anecdotal and “purely descriptive” claims to light. The reason this tradeoff struck a chord with me is that I realized that this is how I viewed racism and descrimination in my life. Through reading this paper, I understood how I saw racism in two different compartments: descrimination claims that could be explained by a purely quantitative trend and descrimination claims that were anecdotal in nature. An example of the first category would be the fact that on average, black men were 4.3 to 5.9 more likely to be killed in metropolitan areas compared to white men, according to an article on a publication called MarketWatch. This is a veritable claim that I could take into account and reason through analytic methods to get to a causal answer. I could possibly run regressions and difference-in-difference trend tests to imply causality. The other vector of descrimination claims came in the form of anecdotal stories friends would confide to me. A cogent example of a purely anecdotal claim is that in high school a couple of my black colleagues would tell me instances of times they felt they had been profiled and pulled over by police. I could not run regressions or significance tests on what my colleagues would tell me, I had to take them at face value. All this time, I realized that I had been discounting anecdotal and purely descriptive claims of racism in exchange for more factual based claims of racism. This is a bias I had in the way I viewed racism. Both factual and descriptive claims are needed to fully understand the whole picture of racism. I have to do more work in lifting up anecdotal claims that are not indeed not necessarily supported by fact.
Another part of this paper that surprised me came in the form of Statistical Descrimination. One assumption we as macroeconomists like to make is that in competitive markets (such as labor markets) all parties have the same information. Another way of saying this is that there exist no information asymmetries. I found it interesting that one of the most widely accepted theories of descrimination in sociology was based upon having an information asymmetry. In this specific case, for example, employers who had limited information had to use characteristics and stereotypes to make inferences and decisions about a certain group or gender. An example of this may be that an employer may see a certain gender or race to be unreliable or less qualified. This employer will then make hiring decisions based on his bias and information asymmetry. If the labor employer were to do more research and be a bit more open to hiring against his/her pre-existing biases, he/she may see that their biases don’t hold up in actuality and decisions about a certain group or gender are misguided.
