Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Racial Discrimination in Charter Schools: A Large-Scale Field Experiment Exploring the Causal Mechanism of Discrimination
Last registered on May 03, 2021


Trial Information
General Information
Racial Discrimination in Charter Schools: A Large-Scale Field Experiment Exploring the Causal Mechanism of Discrimination
Initial registration date
May 02, 2021
Last updated
May 03, 2021 12:27 PM EDT

This section is unavailable to the public. Use the button below to request access to this information.

Request Information
Primary Investigator
Florida State University
Other Primary Investigator(s)
PI Affiliation
Georgetown University
Additional Trial Information
In development
Start date
End date
Secondary IDs
Much of the literature that deals with cream-skimming/ cropping of charter schools is concerned with adverse student selection procedures. Arguably, there are different mechanisms through which charter schools can select prospective students. At the initial stage of the enrollment process, parents seek information on how to enlist their kid in school, including where and how to apply for a lottery if there exists one. At this stage, administrative burdens in accessing relevant information can be increased. Indeed, the availability of information is a crucial criterion for parents to learn about schools of choice and subsequently apply to them. But why would charter schools select certain types of students by increasing administrative burdens (i.e., learning and compliance costs)?

Charter schools are privately managed schools that compete for students with traditional public schools. More than public district schools, they are subject to competitive pressures to sustain on the educational marketplace. We predict that charters will prioritize students who they perceive easier-to-serve and therefore less costly. In addition, the No Child Left Behind Act performance regime induces incentives to focus on students that will meet bureaucratic success criteria like high standardized test scores. Charters are hence under double-pressure to sustain economically while at the same time they need to meet performance targets in terms of students’ academic achievements. It is therefore reasonable to expect that charter schools have developed an inherent incentive structure that leads them to focus on an easier-to-serve clientele.

Direct signals about the future performance and/or costliness of prospective students are often not available during initial information requests send to charter schools. This is the first entry point for unequal treatment. Drawing on the statistical discrimination framework, we argue that charter schools instead use imperfect signals of students’ future performance and costliness. This means that in the absence of direct information, charter school principals will draw on population-based inferences about the average performance/ costliness of members of certain racial/ethnic groups, and use this population-specific statistical knowledge as a stereotype against individual applicants. As a consequence, racial/ethnic groups who are perceived to perform less well on average academically and hence potentially being costlier (like African-Americans and Latinx) will be discriminated against in accessing charter schools.

To test these theoretical predictions, we will send out information requests to all charter school principals in the US in January 2020. In the study, we will experimentally vary the race of putative senders as well as ‘cost criteria’ of hypothetical students. Crossing these experimental factors allows us to explore the mechanism of frontline discrimination by testing whether charter school principals engage in statistical discrimination as a means of cream-skimming. In addition, we expect heterogeneity in discrimination among charter schools based on their degree of market-orientation and overall educational mission. For instance, private for-profit schools that cater academically strong students will be more likely to discriminate against students they perceive as costly on the basis of test scores compared to nonprofit schools that focus on vulnerable students.

External Link(s)
Registration Citation
Bell, Elizabeth and Sebastian Jilke. 2021. "Racial Discrimination in Charter Schools: A Large-Scale Field Experiment Exploring the Causal Mechanism of Discrimination." AEA RCT Registry. May 03. https://doi.org/10.1257/rct.7573-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1) Any active and direct response to our inequity is counted as (1), otherwise (0)
2) Information on how to apply for the school is provided (including posting a link)?
3) Asking follow-up about kid or parent that may suggest it's an application criteria
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
1) Qualitative assessment of provided info on how to apply (comprehensiveness)
2) Asking follow-up about kid or parent that may suggest it's an application criteria
3) Subjective friendliness
4) Response involves a salutation
5) Offer for a school showing, or virtual meeting or phone call (anxiety reducing)
6) Time to response
7) Exploratory DVs based on induction
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will send information requests to school principals via email, experimentally varying the names of email senders. Each school principal will receive two requests, separated by a one-month wash-out period. Names will signal a Caucasian versus an African-American female parent, and will be selected by examining socio-economic status connotations of the 20 most common female African-American and Caucasian names used in previous studies. We aim to select 5 racially distinctive names which are perceived to be similar in terms of SES to avoid our results being driven by the particularities of a single name.

As a second factor, we will experimentally vary the direct signal of a prospective student’s future costliness: low costliness versus no signal. We do not include a signal of high costliness (bad grades and behavior) because we are interested in the presence of a direct signal that replaces the need to statistically discriminate. In addition, it is reasonable to expect that a negative signal (bad grades and behavior) is differentially perceived for white and black students, making it a difficult to interpret it as a comparison base to the ‘no signal’ condition. Lastly, we anticipate substantial heterogenous effects among different charter types and prefer to be well powered to detect these.

The set-up of experimental factors makes for a 2-by-2 factorial design with 4 experimental conditions. Since we will send two requests to each school principal, we will cross-over experimental conditions, meaning that we will send out the opposite factors in round two (i.e., a Caucasian name in round #1 would become an African-American name in round #1).
Experimental Design Details
Not available
Randomization Method
Randomization done in STATA, blocked by state and management organization type (EMO, CMO, freestanding, or missing in a small set of cases)
Randomization Unit
Randomization is at the individual school level.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
5,580 school principals, which each of them receiving two emails across two sounds, so 11,160 total
Sample size (or number of clusters) by treatment arms
For each of the four treatment groups (white/black*signal/no-signal) we will have 1,395 individual school principals that will be contacted twice, which will end up being 2,790 observations per arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Miami University
IRB Approval Date
IRB Approval Number