Chronic absenteeism is the main indicator of school quality and student success that New York State has used to meet the requirements of the 2015 Every Student Succeeds Act (ESSA) governing the country’s K–12 public education policy.
In 2016 the first US Education Department report since the Act showed that more than 6 million students in the US were chronically absent, meaning they missed more than 15 school days in a year.
Six years and a pandemic later, the latest figures remain disquieting. Looking at New York City only, the chronic school absenteeism rate for the school year 2020-2021 in public schools was 41%.
This project aims to understand the underlying causes/risk factors and propose measures to address them.
To understand the city’s exceptionally high school absenteeism rates, this project will answer the following research questions:
- How have the NYC chronic school absenteeism rates changed over time since the 2016-2017 school year?
- What schools and geographic areas in the city (zip code and school district) have the highest school absenteeism rates? Has the trend changed over time? If so, how?
- What are the chronic school absenteeism neighborhood risk factors?
- What can the city administration do to reduce the current chronic school absenteeism rate?
In all of the above questions, the project will look at the effect of age, gender, race, disability, students being English language learners, and in temporary housing.
This project analyzes public school attendance data covering the school years between 2016 and 2021.
As such, it represents the entire public school population in that period.
The data are first-party and are made publicly available by the NYC Department of Education on the NYC Open Data Platform (2016-2017) https://data.cityofnewyork.us/Education/2016-17-2020-21-Citywide-End-of-Year-Attendance-an/sgsi-66kk and on the NYC department of education website (2017-2022) https://infohub.nyced.org/reports/school-quality/information-and-data-overview/end-of-year-attendance-and-chronic-absenteeism-data.
The data were analyzed in respect of applicable data privacy laws. The analysis used no students’ personally identifiable information.
The data were stored by month on the server and downloaded in xlsx files on the analyst’s personal computer.