Abstract

The Coronavirus Pandemic has exposed mass systemic inequalities experienced by vulnerable populations. Vaccines are critical in protecting people from disease and the rest of the damages COVID inflicted on the world, but vaccination distribution might not be equal. The purpose of this thesis is to discover which factors impact uptake of vaccination in New York City. My empirical analysis combines the data on the earnings, educational attainment, gender, age, population size, access to reliable internet, political leanings, and racial makeup of each ZIP code tabulation area (ZCTA) in New York City. The data on vaccination rates is collected at two points of time (May 22, 2021 and February 28, 2022). The two time periods examine the factors of those who had greater access at the beginning of the rollout and then those that are hesitant later in the rollout. Using regression analysis and descriptive statistics, I find that age, earnings, population size, race, and education were significant predictors of vaccination in the earlier rollout phase. Later in the rollout phase, I found that earnings and race were significant predictors of vaccination. ZIP codes that are older, more educated, wealthier, less crowded were more likely to have more vaccinations. As the rollout continued and restrictions on who could be vaccinated lessened, these factors were less significant (if at all) by February 2022. Still, earnings were a highly significant predictor of vaccination.

Publication Date

11-11-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Science, Technology and Public Policy (MS)

Department, Program, or Center

Public Policy (CLA)

Advisor

Qing Miao

Advisor/Committee Member

Sandra Rothenberg

Advisor/Committee Member

Nathan Lee

Campus

RIT – Main Campus

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