Missing Millions

Reflecting on William E. Spriggs’s article, titled “Economics,” part of the postpandemic special section (Issues, Winter 2021), led me to focus on his main point that “modern economics … greatly rests on a host of assumptions.” In the context of novel coronavirus pandemic, Spriggs argues that there are several revealed shortcomings in the assumptions and models that economists traditionally use for decisionmaking—assumptions that led to missed opportunities and perhaps negative impacts on the health and well-being of the nation’s workforce.

Yet there are many extensions to traditional models that include the interdisciplinary work between economists and psychologists (neuroeconomics), economists and political scientists (political economy of digital media), economists and computer scientists (data science), and economists and medical practitioners (health economics and analytics). Research in these areas has led to breakthroughs that get us closer to solutions to the problems related to the human condition. However, even with these tools there is one major shortcoming beyond assumptions and models: the paucity of data representing all residents in America.

The “missing millions” is a concept that has emerged in the discussion about the need for greater diversity, equity, and inclusion in science, technology, engineering, and mathematics—the STEM fields. An extension of this missing millions concept in the COVID-19 pandemic era relates to the lack of access to health and communications services for millions of marginalized residents. A recent New York Times article titled “Pandemic’s Racial Disparities Persist in Vaccine Rollout” stated that “communities of color, which have borne the brunt of the Covid-19 pandemic in the United States, have also received a smaller share of available vaccines.” More importantly, the article stated that the data were inconsistent and that the full accounting of individuals of various ethnicities was unknown, noting that “in some states as much as a third of vaccinations are missing race and ethnicity data.”

No matter the mea culpa of Spriggs’s article on behalf of economists regarding the gaps in economic analysis related to false assumptions; more importantly, our empirical analyses, policies, and implementation of those policies are grossly inadequate because of gaps in data collection and accountability. The nation can do much better at protecting all members of the workforce if we can deploy the vaccine—and clean water, energy-saving technologies, job opening announcements, and other public goods, all things that rely on knowing the magnitude of these problems in underserved communities. Models and algorithms that decisionmakers rely on have limited efficacy because of the missing millions problem. The invisible people, not the invisible hand, is the problem to be solved. How can we make better economic policy if everyone isn’t counted?

Kaye Husbands Fealing