The Spring Term 1 Master of Quantitative Management (MQM) Competition is a data visualization competition where students use data to communicate and analyze problems. Alongside my teammates, Yiqi He and Selina Zhou, I participated in the competition to explore racial disparity in the United States.

While disparities exist in many areas, we were particularly interested in voting, given that the 2020 presidential election had the highest voter turnout in modern history.

I came into the project assuming that because Black Americans have been fighting for their rights for decades, that equality should have been reached in an “easy” field, such as voting. However, as I researched more, I realized that things are much more complicated.

What We Did

We analyzed data from the Census Bureau and the Bureau of Justice Statistics using Tableau. Based on our analysis, the restrictive voting laws disproportionately affect Black voters.

In our research, we discovered several historical events can demonstrate Black representation in the U.S. Congress. Between the 14th Amendment of 1866 (extending liberties and rights granted by the Bill of Rights to former slaves) and the 15th Amendment of 1869 (granting African American men the right to vote) the U.S. had its first Black Senator.  

The Voting Rights Act of 1965 was established to protect the voting rights of minorities. The number of Black members in Congress has increased since then, especially among Black Democrats.

There were multiple points during our research into the voting process where we noticed restrictions were imposed, such as what is needed for people to vote and where they can vote. In this project, we explored voter ID laws and felony disenfranchisement laws to see if they led to disparity in voting rates. We also researched voter ID laws in different states to see if they led to a rate in voting disparity.

What We Discovered

My initial reaction when seeing the data and research findings was shock. It is unbelievable that after so many years, the issues are still fairly serious.

In the U.S., six states strictly require photo ID, and three states strictly require non-photo ID at polls. We found that, in five of those states, the voting rates of Black voters dropped more after the voter ID law was enacted.

Felony disenfranchisement laws have prohibited millions of citizens convicted of felony offenses from voting since 1792.

Our analysis found that when comparing similar drug offenses, Black people are four times more likely to be imprisoned than white people. Therefore, felony disenfranchisement laws disproportionately affect Black voters.

What I Learned About the Power of Data

This MQM competition provided us a valuable opportunity to hone our data visualization skills, explore the issue of racial disparity, and raise awareness about the problem.

I quickly surmised that working on real-world issues like this is a rewarding process. I have learned many things that I never knew before. I always thought that voting was a process as simple as putting a piece of paper in a box, so there should not be any issue with it, let alone disenfranchisement or discrimination.

I think analyzing a social problem is similar to analyzing a business case study in terms of identifying the root cause of a problem. In the business world, solving the root cause problem, not the symptom, is extremely important because that is how we create long-lasting values. If we only fix the symptom, the same issue will happen again and again. The same logic applies when analyzing a social problem. We have to identify what fundamentally caused the social problem and then fix it.

However, I think a social problem is much more complicated than a business case study. It has a much broader context and countless variables—both can make it very difficult to identify the relationship between two things.

For example, in this competition, we researched the relationship between strict voter ID laws and voter turnout. Although, intuitively, they are related, such a relationship is blurry and highly controversial, even among experts because there are so many variables. For example, a potential voter may not cast their vote simply because of a long and arduous work schedule that does not allow for many days off, or free time to go and vote.

What I Learned Personally

This experience makes me more conscious about inequality in the real world. Such an issue truly exists, potentially everywhere. With such a consciousness, I am and will be more inclusive. If I were to lead others, I will ensure that everyone is involved and can express their ideas freely. If I were to see any discriminative actions happening, I also feel confident that I will step in and help the person in need.

Other than the things I have learned related to the topic during the competition, I will always remember the time I spent with my team. As a team, we are all highly motivated and caring. We each made great contributions and fought for the same goal.

I have learned a lot from my team. It has been such a collaborative environment, and is reflected elsewhere by Team Fuqua. I have worked with many talented people and they have made me realize how powerful a team can be. These invaluable memories are something I will always remember.