Opinion

Missing the mark on race and poverty

While there are several things that can be said about UNC professor Gene Nichol’s article on critical race theory, there is one, in particular, I find intellectually dishonest: the descriptive claim that “in North Carolina twice as many blacks as whites live in poverty.”

The issue with the assertion is that it is incorrect. In 2019, the U.S. Census Bureau estimated about 601,800 white and 448,700 black North Carolinians are living in or below poverty. This means 153,100 more white North Carolinians are living in poverty as compared to black North Carolinians.

I understand where the assertion is coming from, however. This is a reference to the difference between the in-group percentage of poverty for white and black North Carolinians. In 2019, the white only population in North Carolina was about 6.6 million and the black only population was about 2.3 million. The in-group percentage of living in poverty for white and black North Carolinians is about 9 percent and 19 percent, respectively. This in-group percentage gets us the “twice as many blacks as whites live in poverty” assertion. Another way of saying it is 1 in 10 white North Carolinians are living in poverty as compared to 1 in 5 black North Carolinians living in poverty.

While this type of data transformation is appropriate for normalization purposes, the issue is that the assertion seeks to make a descriptive claim which is objectively wrong. This type of data transformation from a unit to a ratio is properly understood as expressing a general expectation.

Think of rolling a die with the faceup numbers being a one, two, three, etc. The faceup numbers are descriptive and the event that it will be a particular number on a roll is inferential. The distinction between a descriptive and inferential statistic is important because it distinguishes between ‘what is’ and ‘what could be.’ As alluded to previously, a descriptive statistic is an observation, whereas an inferential statistic is an expectation. Recall the die example: I observe there are six sides to the die and each side represents a number (descriptive). I expect that the faceup number has an equally likely chance of being one of the six numbers upon rolling the die, whereby the probability of the faceup number being one of the six is equally distributed to be 1/6 or 16.7 percent (inferential). Now, the in-group percentage of poverty, roughly, highlights the likelihood of randomly encountering someone that is black and living in poverty within the black population is twice the number of randomly encountering someone that is white and living in poverty within the white population, on average. Consequently, we cannot assert in descriptive terms that twice as many individuals that identify as black are living in poverty as compared to individuals that identify as white in North Carolina because we are deriving an expected experience from the measure—as opposed to an objective observation.

Now this is where things get tricky. While it may be a valid inference that black North Carolinians experience higher disparity rates as compared to white North Carolinians based on in-group experiences, it is not a valid inference to assume it is due to discrimination as Professor Nicol is implying.

This view on disparities being a proxy for likely experience, and thus discriminatory, is misleading from a probability standpoint without rigors examination into the data. Take for instance the probability of drawing a particular type of card from a 52-card deck. The probability of drawing a Jack, for example, from a selection of face cards is 4/12 or 33.3 percent; however, we can change the feature of consideration to be, for instance, the two-number card and get the probability of drawing a two from a selection of number cards to be 4/36 or 11.1 percent. In doing so, we have artificially created a disparity between face cards and number cards, whereby the likelihood of randomly drawing a two is three times the number of randomly drawing a Jack.

Here, disparity is just the result of moving features around and observing the consequences. The point being is that disparity could be the result of how we slice the data and not necessarily the consequence of some observable independent variable. Therefore, suggesting disparity implies discrimination is misleading—especially when applying heuristics to real-world observations whereby visibility into the exact causal condition or a vector of causal conditions is not readily evident or measurable.

The empiricist in me demands the following clarification be made: while disparity does not mean discrimination, if there was discrimination there would be disparity. So, we cannot rule out completely disparity is the consequence of discrimination. Therefore, we must engage in a more comprehensive and robust understanding of the data and isolate what features are predictive of poverty.

It is often the case that more tangible and predictive conditions help explain poverty as opposed to skin color, however. These tangible and predictive conditions are both external and internal to households specifically, and the community more broadly. Externalities that amplify poverty in communities are generally due to high crime rates, political abandonment, poor housing and economic development, lack of internet access and poor healthcare. Internalities that contribute to the persistence of poverty from one generation to the next are often attributed to devaluing education, growing up in single-parent or dysfunctional homes, drug and alcohol addiction, no sense of community, poor social skills, and a lack of a moral compass. While the evidence of a few of these features may be benign or indictive of acute poverty, a collection of them creates and cultivates the condition of chronic poverty. The previous conditions appear to be a much better explanation of why communities experience above average rates of poverty as opposed to skin color.

Joshua Peters is a philosopher and social critic from Raleigh, NC. His academic background is in western philosophy, STEM, and financial analysis. Joshua studied at North Carolina State University (BS) and UNC Charlotte (MS). He is a graduate of the E.A. Morris Fellowship for Emerging Leaders.