Policy Recommendation

Coordinate Policy Areas and Create Dynamic Datasets

Policy field

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CUNY Queens College

This memo is part of Beyond Flattening the Curve, a series of policy recommendations for the COVID-19 crisis.

The present health crisis has exposed once again the stark racialized inequalities in the United States. As already reported in the media, minority groups are disproportionately affected by COVID-19 for two main reasons. Firstly, they live in communities which traditionally suffer from disinvestment, concentrated poverty and lower performing schools. As a result, minorities are overrepresented in low-paying jobs, which they could not perform from home or were deemed as “essential” — transportation workers, hospital staff, grocery stores and pharmacy workers, delivery and others. Furthermore, these jobs do not always carry health insurance and other benefits, such as paid leave, unemployment, sick days, etc. Secondly, minority neighborhoods are traditionally exposed to environmental hazards, which has been shown to cause higher rates of asthma and other respiratory health issues, putting them at great risk of severe COVID-19. Improving neighborhood conditions calls for greater coordination between housing, education, and health policy, and the creation of dynamic datasets that would allow experts to help craft incisive interventions.

Research has shown time and again that the neighborhood context, where children grow up, has a profound effect on their economic success as adults. I believe that successful strategies in creating a beneficial community context necessitate more coordinated programs, which combine three main policy domains: housing, education and health. A mixed-income housing policy has long been pursued, but not in unison with educational and health policies. We can use the advancements in data science to bridge empirical evidence from these domains and establish a wider policy agenda for economically diverse neighborhoods.

Many cities now have data portals, like NYC Open Data portal, where different types of data have become available. Data from the Departments of Health can be utilized to see which neighborhoods contain the highest concentrations of COVID cases. For example, data shows that one such neighborhood is Elmhurst, in Queens, NY. It is a neighborhood with per capita income of about $21,000, a 27% poverty rate for the population under 18 years of age, where immigrant minorities constitute more than half of the population, and one in five people has no health insurance

Communities like Elmhurst need to be identified as soon as possible and resources can be allocated to competitive projects, which will apply new data analytics methods to merge administrative records from various agencies. Dynamic datasets can be created to allow for more informed and coordinated policy decisions. For example, health records will show the most pressing needs, the housing records will show how many subsidized units exist in the neighborhood and where more could be built faster (Elmhurst also is overcrowded), and educational records will show which schools in the area need the most support. If the most vulnerable neighborhoods are identified immediately, policy efforts can be much more efficient and impactful.