Art designed by Minnah Munif
We will be diving deep into the concept of child opportunity so it is important that we define this terminology and introduce context for the circumstances it creates. We consider child opportunity to be “the ability of children to achieve healthy development in all areas (physical, cognitive, emotional, and social) and to reach their full potential” (Noelke et al., 2020). The development in these areas for children depends on many factors, which are measured in the Child Opportunity Index 2.0 (COI 2.0). These factors are involved with many aspects of a children’s life, such as education quality and access to healthcare. We are investigating child opportunity in the U.S., whose cities create diverse circumstances for children to grow up in. By comparing the child opportunity levels across these areas, we can better understand which variables and factors impact a child’s ability to achieve full development physically, cognitively, emotionally, and socially. Child opportunity can seem to be an abstract concept, but, while measuring these impactful variables, we are able to put numbers to the term.
The Child Opportunity Index 2.0 (COI 2.0) is intended to represent child opportunity which is very difficult to measure quantitatively. The datasets part of the index are able to filter through a multitude of factors that are compiled to represent the overall child opportunity along with the other individual phenomena that ultimately compose it. The COI 2.0 database generated data indicators measured at the census tract level in two time periods, 2010 and 2015. These census tracts are neighborhoods in the 50 states of the U.S. and, as defined by the Census Bureau, involve an area within local boundaries such as bodies of water or roadways with about 4000 individuals as residents. These data sources contained indicators on a variety of scales that included U.S. dollars, percentages, and counts. For these indicators to be consolidated into one index, a z-score transformation was utilized to standardize the raw data values. The indicators were grouped in the three domains of Education, Health and Environment, and Social and Economic.
Our analysis of the Education domain data showcases that there is a negative association between school poverty and the math and reading proficiency of third graders respectively. Third graders from metro/micro areas with schools that are higher in poverty on average scored lower in the math and reading tests on average. In the Health & Environment domain, our research shows that the higher the median household income, the less likely residents in that area will have lower access to supermarkets and healthy food. This emphasizes that children from lower-income communities are also likely to have lower health outcomes. Lastly, in investigating the Social & Economic domain, we found that areas with higher rates of greater educational attainment are much more likely to have higher employment rates. In other words, a correlation is found between access to educational and financial resources for higher education and future employment opportunities. There also remains a positive correlation between the two other variables: the greater the number of early childhood education centers, the greater the number of high-skill jobs. In relation to what was discussed in the book Closing Opportunity Gaps, our findings aid in stressing that poor neighborhoods and communities struggle because they have fewer financial opportunities and less political power to rely on to help gain resources that offer children the opportunities they deserve (Carter, 2016).
Our team would like to thank Dr. Francesca Albrezzi, Grace Skalinder, & Edgar Reyna. The guidance provided by our Digital Humanities 101 instructional team allowed us to learn to think about data and its analysis through a humanistic lens and gain experiences in the use of digital tools.