Extending a Hand: Economics as a Common Language for Diversity and Inclusion
I recently spoke with a colleague who was contemplating a return to academia and expressed irritation about the growing number of diversity and inclusion statements requested by economics departments. As a white male, my colleague felt these initiatives were personal attacks. He believed that he treated people equally, regardless of their race or ethnicity, and felt that this was not the perspective sought by diversity and inclusion statements. He was indignant at having to defend his impartial treatment of others. However, this wasn’t the first time we had differing views on matters. Rather than seeing him as an adversary, I engaged in intellectual discourse, using economics as our common language to communicate.
It’s true that diversity and inclusion initiatives are gaining momentum across various fields, including economics. To understand the need for these initiatives, we must recognize that socially optimal levels of diversity and inclusion differ from the private levels due to the externalities they generate. And these externalities are not homogenous. They have positive effects for some people and negative effects for others. Diversity and inclusion initiatives aim to address the differences between private and public costs and benefits that arise from these externalities.
To better comprehend the need for diversity, let’s consider the analogy of regression models in statistical analysis. Regression analysis involves predicting an outcome using several explanatory variables, similar to how a diverse workforce can contribute unique perspectives to solve problems. However, incorporating too many highly correlated variables can result in overfitting, where the model becomes overly complex and less accurate. In the context of diversity and inclusion, overfitting could be compared to assuming that every individual from a particular background will offer a unique perspective, leading to inefficiencies.
To counter this, researchers evaluate information gain for each variable and employ techniques like LASSO to select the most informative variables and eliminate redundancy. In the realm of diversity and inclusion, we can apply a similar approach by identifying the most valuable perspectives and minimizing redundancy. However, it’s impossible to predict in advance which perspectives will be diverse or redundant. As a result, we use race, ethnicity, and other observable diversity indicators as proxies for different life experiences and viewpoints on issues.
Diversity and inclusion initiatives and policies can help society achieve pareto efficiency, but only if we acknowledge that increasing diversity and inclusion will generate both positive and negative externalities. Consequently, there will be winners and losers. To reach pareto efficiency without worsening anyone’s situation, it’s crucial to compensate those who lose as a result of these policies. Unfortunately, I suspect that some individuals will be reluctant to aaccept that others could be worse off under these policies. Even if they do recognize this, they may make a judgment call that those negatively affected do not deserve compensation for their utility loss.
In conclusion, diversity and inclusion initiatives hold the potential to foster a more efficient allocation of resources. It is crucial to acknowledge the possible negative externalities and compensate those who might be disadvantaged by these initiatives while also appreciating their benefits. By using economics as a common language, I hope that I’ve helped my colleague understand the nuances of these initiatives and their alignment toward socially optimal goals. My hope is that this piece inspires you to engage in open dialogue with individuals holding different perspectives and extend a hand across political and ideological divides to bridge them. Let’s continue to engage in constructive discourse and collaborate to shape a world that values the unique contributions of each individual.
