Air filters and student learning

Last registered on August 16, 2022


Trial Information

General Information

Air filters and student learning
Initial registration date
August 10, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 16, 2022, 9:14 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator


Other Primary Investigator(s)

PI Affiliation
PI Affiliation
University of Chicago

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Residential sorting across socio-economic strata translates into students being exposed to higher levels of air pollution if they belong to lower socio-economic strata. Since there is evidence that pollution lowers cognitive ability, this could reduce learning, educational attainment, and
eventually salaries for disadvantaged students. Governments could minimize pollution exposure by installing filters that improve classroom air quality. This project aims to experimentally evaluate whether installing air filters inside classrooms enhances student learning. Bogotá's high levels of air pollution are likely negatively affecting the education of boys and girls in public (and private) schools. Installing air filters (HEPA) can mitigate the harmful effects of pollution and improve student performance. All in all, our project asks: what are the effects of installing air filters inside classrooms on student learning?
External Link(s)

Registration Citation

Kremer, Michael, Mauricio Romero and Santiago Saavedra. 2022. "Air filters and student learning." AEA RCT Registry. August 16.
Sponsors & Partners


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Experimental Details


There are three treatments arms.
Filter treatment: In these schools, HEPA filters will be installed in every classroom that serves Grade 11
Monitor treatment: In these schools, air quality monitors will be installed in every classroom that serves Grade 11. One more air quality monitor will be installed outside the classrooms.
Monitor+Filter treatment: In these schools, both filters and air quality monitors will be installed in every classroom that serves Grade 11. One more air quality monitor will be installed outside the classrooms.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We are interested in two outcomes:
1) “Indoor air quality”, measured as PM2.5 and PM10 levels.
2) “Student outcomes”: We will measure student outcomes using administrative data on school drop-out, school attendance, and scores in standardized tests taken at the end of high school (SABER 11 exam). The Saber 11 exam is typically used by universities to admit applicants and is widely used to rank schools in the country. Therefore, the exam is high-stakes for both students and schools.

Eventually, we are hoping to use administrative data to track these students as they enter the labor force to study employment outcomes.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
357 public schools across Bogotá will be randomly assigned into four groups. 75 of those schools will receive only HEPA air filters, 75 will receive only air quality monitors and 75 other schools will receive both HEPA air filters and air quality monitors. The remaining schools will be part of the control group and hence will not receive neither air filters nor air quality monitors. The air filters will be installed inside the classrooms that serve grade 11, which is the last grade of schooling in Colombia. Students from grade 11 will take the Saber 11 exam, which is a high-stakes national exam taken by all students in Colombia before graduating from school. The results from this exam will be used to measure the impact of air filters on student learning. As for the air quality monitors, these will be installed inside and outside clasrooms, in order to compare indoor air quality with out doors air quality. Installing only air quality monitors in some schools will allow us to determine whether having information pollution levels affects the behavior of teachers or students, as well as learning outcomes. The major´s office (Alcaldía de Bogotá) will offer training on how to use the air filters and monitors.
Experimental Design Details
Not available
Randomization Method
Randomization was done in Stata using randtreat
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
357 schools
Sample size: planned number of observations
39,270 students (roughly 110 students per school) per year
Sample size (or number of clusters) by treatment arms
132 schools control ,75 schools air filters 75 schools air quality monitors 75 schools air filters+ air quality monitors treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
There are three power calculations that must be made (all interlinked). First, is the effect of the filter on indoor air pollution. In this case, the unit of observation is the same as the unit of randomization (the school). We assume for the calculations that the power (β) is equal to 0.8, the level (α) is 0.05, and that the control variables (e.g., school characteristics, strata fixed effects, and historical pollution levels) explain 30% of the variance in the final result (i.e., in the indoor air pollution). Under these assumptions, using the formula described above, the MDE for the Intention-To-Treat (ITT) is 0.33SD against the control. The MDE for the ITT comparing treatments is 0.382SD. Assuming that only 70% of schools use the filter consistently (and correctly), the MDE for the treatment-on-the-treated is 0.47SD against the control. Next, we can study the power to detect the reduced form effect of providing schools with air filters on test scores. As the experiment is randomized at the school level, but the main results are observed at the student level (i.e., scores from standardized tests), it must be taken into account that these results have some correlation with each other. This is done by clustering the standard errors at the school level. Assuming an intracluster correlation of 0.3, and that we observe 80 students per school, the MDE (comparing against the control) for the ITT is 0.18 SD and for the TOT is 0.26 SD. Comparing across treatments, the ITT MDE is 0.21SD, and the TOT MDE is 0.3SD.

Institutional Review Boards (IRBs)

IRB Name
Social and Behavioral Sciences Institutional Review Board Chicago
IRB Approval Date
IRB Approval Number
IRB Name
Comité de Ética en Investigación de la Universidad del Rosario Sala de Ciencias Sociales
IRB Approval Date
IRB Approval Number
DVO005 638-CS397
IRB Name
Institutional Review Board, ITAM
IRB Approval Date
IRB Approval Number