Research Preview: HII and the P20W Pipeline
Ari Fenn, Researcher
June 23, 2021
As new research (which will also be my first report as a researcher with the UDRC) moves from the initial writing stage to the internal peer-review process, I offer this blog post as an amuse-bouche. The research establishes the relationship between area deprivation and P20W outcomes. The Utah Department of Health initially proposed this research to integrate their measure of area deprivation, the Health Improvement Index (HII) , into UDRC’s P20W research. To do this, I use 12 different P20W measures ranging from kindergarten preparedness to post-secondary attendance to test for a relationship between area deprivation and P20W outcomes.
How the HII is Used
The Utah Department of Health uses the HII to test for the existence of health disparities. Health disparities occur when a given health outcome is affected by the individual and the social and economic characteristics of the area in which that individual lives. The HII is a factor analysis of nine social and economic variables, where the first factor is used as the HII. Thus, with a single measure of a complex socio-economic phenomenon, it is possible to test for disparities. Health disparities exist when an outcome of interest systematically varies with the measure of area deprivation.
How UDRC uses HII for this Study
An approach that addresses area deprivation naturally translates to UDRC’s P20W research, so the only challenge was how to integrate HII, not why. HII is measured at the Utah Small Area level, which does not necessarily translate to geographies such as school districts. To link HII to our P20W data, I exploited the geographic nature of both HII data and our data along with packages for R that are designed to work with spatial data . Each outcome is measured as a yes or no, binary outcome. These are proficiency in standardized tests, if a student is chronically absent, or if a graduate from a Utah high school attends a public post-secondary institution in Utah.
I include descriptive statistics that compare outcomes between HII groups. I also compare HII groups to the state average. An apples-to-apples comparison is needed to isolate the relationship between area deprivation and P20W outcomes. My goal was to explain the relationship between HII and various P20W outcomes, making regression the preferred technique. Since the outcomes are binary in nature, I use a probit regression to control individual characteristics associated with P20W outcomes to isolate the relationship between HII and each P20W outcome. I cannot share specific results at this time, but I can say they are interesting and illuminate the importance of understanding area deprivation.
This research project will be entering internal peer-review over the next few weeks and hopefully will be available by the end of summer. I hope this research provides initial insight into how area-level social and economic factors are associated with individual education and workforce outcomes. Additionally, I hope this research piques interest for policymakers and for further research to broaden understanding of the role area deprivation plays.
 More information about the HII is available On the Utah Department of Health website.
 I covered how to do this in a previous blog post.