Ian Filmore and Dr. Eric Eide, Economics
While researchers have explored a wide variety of measures designed to improve student performance, the obvious issue of absenteeism has gone largely ignored. This is even more puzzling when we consider that most policy measures implicitly rely on the students’ physical presence in the classroom in order to work. We view this project as a first step toward understanding the role of absenteeism in shaping student outcomes.
No national data on the prevalence of absenteeism currently exists. However, some local studies suggest that the problem may be quite substantial. For example, Sundius and Farneth (2008) report that in Baltimore, Maryland, 14 percent of elementary school students, 34 percent of middle school students, and 44 percent of high school students missed 20 or more days of school.
Students who are repeatedly absent from school tend to struggle academically and in other areas. There is a strong correlation between absences and negative outcomes such as poor academic performance and involvement in risky behavior. Chang and Romero (2008) find that chronic absence in kindergarten predicts low levels of educational achievement in the fifth grade, with the most pronounced effects among poor children. Sundius and Farneth (2008) find that among older children, those who are frequently absent are more likely than their regularly attending peers to perform poorly in academics, to drop out of school, to use alcohol and drugs, and to be involved in the juvenile justice system.
Despite the strong negative connotations of the existing research, these correlational findings do not necessarily imply a causal effect. Perhaps students who tend to be absent more on average also tend to be lower achieving or less motivated students. This suggests correlational studies may overstate the actual effect of absences on test scores. Alternatively, students may tend to miss on less productive school days. In this case, truly random absences might actually be more detrimental than those observed in the data, which are a mixture of random and chosen absences. In either case, failing to account for the presence of other factors will lead to biased estimates of the effect of absences on test scores.
In our paper, we use data from the 2002 wave of the Child Development Supplement (CDS) of the Panel Study of Income Dynamics (PSID) to estimate the causal effect of absences on test scores for students in grades 7-12. The PSID is a nationally representative longitudinal study of households in the U.S. The CDS focused on the children of PSID families. Since the CDS does not have actual data on absences, we construct an absence variable using information on days missed due to illness, injury, and skipping school. In order to account for the possibility of other factors affecting both absences and test scores, we use a statistical method called Instrumental Variables (IV) estimation.
Our paper makes a number of contributions to the literature on the relation between student attendance at school and educational performance. The estimated effect from our IV models ranges from a 0.036 standard deviation decline in test score per yearly absence, up to a 0.087 standard deviation decline. In other words, an additional 10 absences per year cause students’ test scores to drop about half a standard deviation, on average. This amounts to a relatively large change in test scores. To our knowledge, these are the first estimates of the causal impact of absenteeism on student achievement. Additionally, ours is the first paper to study student absence using nationally representative data. We have submitted our findings to Economics of Education Review, a peer-reviewed academic journal, for publication.
Because our estimates suggest relatively large effects of absences on test performance, we also consider possible policy implications. First, we recommend that schools consider systematically recording and reporting absences. Currently, most schools do not have such reporting systems in place, so it is difficult to ascertain the extent of the problem. Some school and district level case studies suggest the problem can be quite extreme (Chang and Romero, 2008). Providing sufficient funding for such initiatives would be included as part of this policy proposal.
Second, reporting some measure of ‘chronic absences’ could be included in school report cards. In addition to reporting measures such as average test scores and advanced placement statistics, they could also include counts of the number of students who are chronically absent (e.g. 20 or more absences during a school year). These measures could be broken down by risk categories or subpopulations of interest (gender, race/ethnicity, grade, free-lunch eligible, etc).
Third, based on the reports, specific plans should be made to address at-risk groups of students. In the same way that schools create plans to accommodate children with special needs, if a student is identified as at-risk due to chronic absences, a student-specific plan should be created to address the problem.
While our study explores the causal implications of student absences in the U.S., further work is needed to better understand a range of issues associated with absences. For example, it would be useful to explore in more detail what types of absences (illness, injury, truancy) are most prevalent among students from different age and demographic groups, and how absences affect the educational performance of each of these sub-groups. It would also be valuable to understand the link between school absences and job performance later in an individual’s life.