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Data Journalism Edition: How Income Affects Education

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Since the ratification of the No Child Left Behind Act in 2001, standardized testing has become typical in the majority of public schools, for the stated purpose of measuring whether students are learning. However, there is a correlation between income and success on these standardized tests, indicating that the students taking these tests are disadvantaged when they come from a lower income background than from a higher income background. The results demonstrate that the students’  aptitudes are not  naturally at different levels. This raises a question as to why performance in lower income districts is poorer than in higher income districts.

One factor behind this disparity is the income of the town itself. Many scholars have noted that test scores and town income are positively correlated. Poorer districts tend to have less money to allocate towards public education than wealthier districts, meaning that wealthier towns can provide more resources to their students than poorer towns.

For the majority of schools, approximately 8.3% of funding comes from the federal government, 45.6% comes from the state, and 37.1% comes from the district itself.

Poorer districts tend to have less money to allocate towards public education than wealthier districts, meaning that wealthier towns can provide more resources to their students than poorer towns.”

A secondary cause comes from within the households of the schools’ students. According to a survey by Pew, students in lower income houses have fewer out of school resources, meaning that, if they struggle in school, it is less likely that they will receive the necessary extra help.

A research project from Harvard also showed a correlation between poverty and mental development in children. In the study, it was discovered that children from lower-income backgrounds tended to have had higher levels of stress than those from wealthy backgrounds, as well as less exposure to vocabulary at a younger age. Researchers found that the density of nerve tissue in areas of the brain associated with both vision and knowledge acquisition and retention were thicker in wealthier children than in children from lower-income backgrounds. Researchers found that this difference in nerve tissue thickness could account for up to 44% of the achievement gap between these two groups.

It is also possible that population density may have an effect on poverty and by extension academic performance. According to economist Mary Cleveland, areas with higher populations tend to have lower incomes. A possible explanation for this phenomenon may be that in cities, the cost of living is much higher than that in a suburban area. Additionally, people prefer to live in suburban, instead of urban, areas. For this reason,  people from poorer backgrounds most likely cannot afford suburban homes.

The correlation between economic status and test performance may be related additionally to classroom size. According to a study conducted by the Center for Public Education, larger classes tend to perform less favorably than smaller classes. The report found that classes below 20 students showed significant improvement compared to those of 20 or more students.  This effect can also be seen in Bergen County.

The strange but rarely discussed association between population density and academic achievement could be an effective proxy for income. ”

 

Above is a chart representing all 65 towns in Bergen County that had sufficient data. There is a weak positive correlation between income and PARCC scores. The third variable of population density has also affected passing scores. For example, the district of Lodi and Fairview have similar median incomes, in the low fifty thousands. Lodi has a population density of 10,657.6 people per square mile and a passing rate of 54.2%, while Fairview has a population density of 16,421.8 people per square mile and a passing rate of only 41.3%.

The strange but rarely discussed association between population density and academic achievement could be an effective proxy for income. These two factors appear to be correlated as interrelated variables here in Bergen County. This correlation may differ in other regions or nations. However, it is clear that more crowded areas of similar, low incomes have more crowded classrooms. Paying careful attention to class size may help crowded districts to reduce the achievement gap.

 

Sources utilized:

http://www.huffingtonpost.com/2015/04/22/kansas-schools-funding_n_7112702.html

http://www.pewsocialtrends.org/2015/12/17/parenting-in-america/

https://www2.ed.gov/about/overview/fed/10facts/index.html

http://www.centerforpubliceducation.org/Main-Menu/Organizing-a-school/Class-size-and-student-achievement-At-a-glance/Class-size-and-student-achievement-Research-review.html

http://www.mcleveland.org/Class_notes/Population_Distribution_and_Poverty.pdf

https://www.psychologytoday.com/blog/the-athletes-way/201504/why-do-rich-kids-have-higher-standardized-test-scores

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The student news site of Bergen County Academies
Data Journalism Edition: How Income Affects Education