Cream Skimming and Pushout of Students Participating in a Statewide Private School Voucher Program

Abstract

A pervasive issue in the school choice literature is whether schools of choice cream skim students by enrolling high-achieving, less-challenging, or less-costly students. Similarly, schools of choice may “push out” low-achieving, more-challenging, or more-costly students. Using longitudinal student-level data from Indiana, we created multiple measures to examine whether there is evidence consistent with the claims of voucher-participating private schools cream skimming the best students from public schools or pushing out voucher-receiving students. We do not find evidence consistent with the claim of cream skimming. However, we find evidence consistent with the claim of private schools pushing out the lowest-achieving voucher students. This is the first study to examine these two issues within a statewide private school voucher program.

Keywords

educational policy private education elementary schools middle schools descriptive analysis school vouchers school choice selective enrollment cream skimming pushout

Since the inception of educational vouchers, many have argued that allowing students to use public money to attend private schools will theoretically lead private schools to “cream skim” the best students from traditional public schools (TPSs) and “push out” the weaker students in hopes of lowering costs and improving their academic profile (Berliner & Glass, 2014; Epple & Romano, 1998; Ravitch, 2010, 2013). If these claims are true, then the job of TPSs is more difficult as they are left with educating the challenging and costly students. Previously, there has been little research that has examined these issues within voucher programs.

The best research with implications for the cream skimming debate is by Fleming and colleagues (2015), who examined data of Milwaukee TPS students who used vouchers and found that parents of voucher students had higher educational attainment, but lower incomes. Meanwhile, students remaining in TPSs had higher initial test scores and were more likely to be special education students. While informative, it only indirectly addresses the central issue of the cream skimming claim—whether vouchers lead private schools to attract the best students from TPSs. To examine this issue more directly, it is important to examine whether the students exiting a TPS to attend a private school using a voucher are the highest-performing, least-challenging, or least-costly students relative to all their peers in the exiting school.

For the pushout question, the research is even thinner, with only two studies on voucher students in Milwaukee. Cowen and colleagues (2012) examined the characteristics of voucher students who exited a private school to return to a TPS and found that lower-performing students are more likely to exit private schools. Subsequently and using math and English language arts (ELA) achievement data on a statewide assessment, Carlson et al. (2013) found that the performance of voucher students who move out of a private school improved once they entered a TPS. However, neither analysis made comparisons to the likelihood of low-performing students exiting schools in general (including TPSs) or the achievement of students after making other school transitions, which limits the ability to speak to whether these are general trends among all schools or specific to voucher students exiting private schools.

We expand on existing work by examining claims of cream skimming and pushout in the context of Indiana’s voucher program. The Indiana Choice Scholarship Program is one of the largest statewide private school voucher programs, enrolling nearly 45,000 students in the 2021–2022 school year (Indiana Department of Education, 2022). We use 8 years (2010–2011 through 2017–2018) of statewide longitudinal data from the Indiana Department of Education. As all voucher-participating private schools report data to the state and take the same assessments as public schools, we can examine transition rates of voucher recipients to and from voucher-participating private schools. We build off empirical approaches of studies that have examined the cream skimming and pushout issues in charter schools across several states (Kho et al., 2022; Winters, 2017; Zimmer et al., 2011; Zimmer & Guarino, 2013) and draw comparisons with the transition rates of voucher-eligible peers in TPSs. We address two main questions:

Research Question 1: Do we have evidence consistent with the claim that private schools “cream skim” high-achieving, less-disruptive, or less-costly-to-educate students from TPSs via participation in the statewide voucher program?

Research Question 2: Do we have evidence consistent with the claim that private schools push out low-achieving or more-costly-to-educate voucher students back to TPSs?

We cannot prove definitively whether voucher-participating private schools are cream skimming or pushing out students using only administrative data. Administrative data do not allow us to discern the action of the schools (supply side) from the decision-making of families (demand side). Therefore, our analysis focuses on whether we can find patterns in the data consistent with the claims of cream skimming or pushout behaviors, which as suggested in the charter literature (Kho et al., 2022), is a necessary, but not sufficient condition, for identifying cream skimming and pushout behaviors by schools. From the TPS’s perspective, it does not matter whether a voucher student’s move is the result of the private school’s behavior or the choice of the family. In either case, the moves create a greater burden on TPSs, which are left educating the most challenging and costly students.

Empirical Approach

We provide a condensed overview of our empirical approach here. The full technical details of our data set, sample description, and robustness checks are available in the Supplementary Appendix in the online version of the journal. For the cream skimming question, we examine whether students entering the voucher program had above-average test scores, had fewer disciplinary incidents, or were less likely to be classified as either an English language learner (ELL) or a special education student relative to their previous voucher-eligible peers in the TPSs. For the pushout analysis, we examine whether students exiting the voucher program had below-average test scores or were more likely to be classified as either an ELL or a special education student relative to their former voucher peers in the private school they exit and the broader population of voucher-eligible students in TPSs. Private schools do not report disciplinary data to the state, so we cannot assess movement based on these characteristics for the pushout analysis. In both the cream skimming and pushout analyses, we compare the probabilities of voucher students changing schools with their respective comparison groups. For the cream skimming question, we include all school transitions, while for the pushout question, we only examine nonstructural school changes (i.e., students moving before the school’s terminal grade), as schools have little motivation to push out students upon reaching the maximum grade level offered by the school.

For our formal models, we do not include any controls. From a perspective of whether there is cream skimming, student characteristics (e.g., sex, race/ethnicity) may not be important as we want to know whether the voucher-participating private schools are skimming off higher-performing students or pushing out lower-performing students regardless of other student characteristics. Therefore, we run simplified linear probability models with the discrete outcome of whether the student exits their school as a function of a series of key independent variables. As a sensitivity analysis, we include student characteristics such as sex, race/ethnicity, ELL status, and special education status for both cream skimming and pushout and display the results below our main results in Tables 1 and 2. Our analyses do not yield causal estimates, but rather descriptive information about patterns in the data consistent with the cream skimming and pushout claims.

Table 1

Cream Skimming Main Results

Variables of interest	Math test scores			ELA test scores			Low discipline incidents	Non-ELL status	Non-special educ. status
Variables of interest	Above exiting school average	Top 75% of the state	Top 90% of the state	Above exiting school average	Top 75% of the state	Top 90% of the state	Low discipline incidents	Non-ELL status	Non-special educ. status
	No covariates
Above-test-score threshold (TPS)	−0.011*** (0.001)	−0.022*** (0.002)	−0.018*** (0.003)	−0.012*** (0.001)	−0.029*** (0.002)	−0.030*** (0.003)
Voucher Private × Above Threshold	−0.003(0.008)	0.000(0.002)	−0.024(0.017)	0.001(0.008)	0.028** (0.011)	0.033(0.017)
Low-discipline student (TPS)							−0.063*** (0.002)
Voucher Private × Low Discipline							−0.091*** (0.014)
Non-ELL student (TPS)								0.055*** (0.002)
Voucher Private × Non-ELL								0.015(0.010)
Non-special educ. student (TPS)									−0.011*** (0.002)
Voucher Private × Non-Special Educ.									0.001(0.013)
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Covariates	No	No	No	No	No	No	No	No	No
	With student covariates
Above-test-score threshold (TPS)	−0.011*** (0.001)	−0.022*** (0.002)	−0.017*** (0.003)	−0.014*** (0.001)	−0.025*** (0.002)	−0.025*** (0.003)
Voucher Private × Above Threshold	0.002(0.008)	0.005(0.011)	−0.016(0.017)	0.003(0.008)	0.029** (0.010)	0.032(0.017)
Low discipline student (TPS)							−0.057*** (0.002)
Voucher Private × Low Discipline							−0.071*** (0.014)
Non-ELL student (TPS)								0.028*** (0.002)
Voucher Private × Non-ELL								0.019(0.011)
Non-special educ. student (TPS)									−0.023*** (0.002)
Voucher Private × Non-Special Educ.									0.016(0.013)
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Covariates	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Note. Standard errors, clustered by school, in parentheses. Separate analyses conducted for each subject and achievement threshold as well as for the disciplinary, ELL status, and special education status indicators. Analyses include all voucher-using and voucher-eligible students in traditional public schools (TPSs). The dependent variable includes all structural and nonstructural transitions from public schools. The key estimates are the interaction terms, indicating the differential likelihood of high-achieving/low-discipline/lower cost to educate students use a voucher to transition to a private school relative to their similar peers to transitioning to any other school without a voucher. We do not display the results from the model of the parameter δ (the coefficient on the voucher private baseline transition rate). This parameter is necessary for estimation but not a useful to interpret (see Supplementary Appendix in the online version of the journal for more information). ELL = English language learner; TPSs = traditional public schools; ELA = English language arts.

p < .05. **p < .01. ***p < .001.

Table 2

Pushout Main Results

Variables of interest	Math test scores			ELA test scores			ELL status	Special educ. status
Variables of interest	Below exiting school average	Bottom 25% of the state	Bottom 10% of the state	Below exiting school average	Bottom 25% of the state	Bottom 10% of the state	ELL status	Special educ. status
	No covariates
Voucher private	−0.018*** (0.002)	−0.013*** (0.002)	−0.013*** (0.002)	−0.017*** (0.002)	−0.012*** (0.002)	−0.011*** (0.002)	−0.008*** (0.002)	−0.011*** (0.002)
Below-test-score threshold (TPS)	0.022*** (0.001)	0.055*** (0.001)	0.067*** (0.001)	0.019*** (0.001)	0.049*** (0.001)	0.059*** (0.001)
Voucher Private × Below Threshold	0.009* (0.003)	0.011** (0.004)	0.029*** (0.006)	0.009* (0.003)	0.019** (0.004)	0.029*** (0.006)
ELL student (TPS)							−0.041*** (0.001)
Voucher Private × ELL							− 0.018*** (0.004)
Special educ. student (TPS)								0.014*** (0.001)
Voucher Private × Special Educ.								0.012 (0.006)
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Covariates	No	No	No	No	No	No	No	No
	With student covariates
Voucher private	−0.013*** (0.002)	−0.011*** (0.002)	−0.010*** (0.002)	−0.014*** (0.002)	−0.010*** (0.002)	−0.009*** (0.002)	−0.005* (0.002)	−0.009*** (0.002)
Below-test-score threshold (TPS)	0.019*** (0.001)	0.046*** (0.001)	0.055*** (0.001)	0.018*** (0.001)	0.044*** (0.001)	0.052*** (0.001)
Voucher Private × Below Threshold	0.008* (0.003)	0.012** (0.004)	0.029*** (0.006)	0.009* (0.003)	0.020** (0.004)	0.029*** (0.007)
ELL student (TPS)							−0.018*** (0.001)
Voucher Private × ELL							− 0.022*** (0.004)
Special educ. student (TPS)								0.014*** (0.001)
Voucher Private × Special Educ.								0.013 (0.006)
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Covariates	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Note. Standard errors, clustered by school, in parentheses. Separate analyses conducted for each subject and achievement threshold. Analyses include all voucher-using students in private schools and all voucher-eligible students in traditional public schools (TPS). The dependent variable includes only nonstructural transitions from public schools. Italic estimates represent statistically significant differences in the likelihood of making a nonstructural transition at p < .025 between below-test-score threshold/ELL/special education voucher students and below-test-score threshold/ELL/special education TPS students, derived from the sum of the voucher private transition main effect and the Voucher × Below Threshold interaction term. Bold estimates represent statistically significant differences in the likelihood of making a nonstructural transition at p < .025 between below-test-score threshold/ELL/special education voucher students and above-test-score threshold/non-ELL/non-special education voucher students, derived from the sum of the below-test-score threshold main effect and the Voucher × Below Threshold interaction term. ELL = English language learner; TPS = traditional public schools; ELA = English language arts.

p < .025. **p < .005. ***p < .0005 after adjustment for multiple comparisons.

Cream Skimming Estimation

Our sample for the cream skimming analysis consists of all voucher-eligible students in TPSs (for a sample description, please see Supplementary Appendix Table A1 in the online version of the journal). For an analysis of student movement patterns consistent with the claim of cream skimming, we assess the likelihood of voucher students moving to a private school (the move could be either structural or nonstructural move) in a given year t who are classified as high-achieving, low-discipline, or not an ELL or special education student in the previous year t − 1. We compare this likelihood to all other voucher-eligible students who may make any move without a voucher. We estimate this using achievement as a measure of performance in Equation 1:

Y_{i t} = δ V P_{i t} + θ H A_{i t - 1} + γ V P_{i t} H A_{i t - 1} + λ_{t} + μ_{i t} .

(1)

Here, $Y_{i t}$ = 1 if student i makes a move from their public school in between years t − 1 and t. We identify the differential patterns of mobility across students through three indicators. The first ( $V P_{i t}$ ) denotes students who first received a voucher and transitioned into a private school as of year t. The second ( $H A_{i t - 1}$ ) indicates all students who are high-achieving at time t − 1. We estimated separate models for the different indicators of high-achieving students, including being above the school average, above the state 75th percentile, and above the state 90th percentile. The third ( $V P_{i t} H A_{i t - 1}$ ) is an interaction between the first two terms, specifically denoting voucher students who were high-achieving in their public school the year prior (t − 1) to making a transition to a private school in year t. We include a series of year fixed effects ( $λ_{t}$ ) to account for unobserved differences in transition rates between years.

In the cream skimming analysis, we want to know whether the rate in which high-achieving students transition from their public school to a private school after receiving a voucher (i.e., voucher users) is different from the rate in which high-achieving students—who are also voucher-eligible—transition from their public school to any other schools without a voucher. We capture this difference in the coefficient (γ) on the interaction term ( $V P_{i t} H A_{i t - 1}$ ) in Equation 1. A significant and positive γ suggests that higher-achieving students are more likely to receive a voucher and transition to a private school than high-achieving, voucher-eligible students are to make any other school transition without a voucher. The baseline likelihood that all high-achieving students (voucher-using and voucher-eligible) are to make any school transition relative to all low-achieving students is already captured in θ, so γ distinguishes the high-achieving, voucher-using difference in likelihood of transition. This estimate yields descriptive information about whether there are patterns in the data consistent with the claim of private schools cream skimming higher-performing voucher students.

Using this same structural model, we also examine whether students with below-average number of discipline infractions, non-ELL, and non-special education students are more likely to transfer to private voucher schools. In these analyses, a low-discipline, ELL status, or special education status indicator replaces the high-achieving student indicator, $H A_{i t - 1}$ in both the main effect and interaction term. Here, a significant and positive γ would suggest evidence consistent with the claim that less disruptive or less-costly-to-educate students are more likely to receive a voucher and attend a private school than less disruptive or less-costly-to-educate voucher-eligible students are to switch schools.

Pushout Estimation

Our sample for the pushout analysis consists of all voucher-using students in private schools and all voucher-eligible students in TPSs (for a sample description, please see Supplementary Appendix Table A2 in the online version of the journal). Following a similar approach to our cream skimming analysis, we assess the likelihood of voucher students moving to a TPS (in this case, only a nonstructural move) in a given year t who are classified as low-achieving, or a special education or ELL student in the previous year t − 1. We want to compare this likelihood to all other voucher-eligible TPS students with the same classifications who make a move. We estimated this using achievement as a measure of performance in Equation 2:

Y_{i t} = δ V P_{i t - 1} + θ L A_{i t - 1} + γ V P_{i t - 1} L A_{i t - 1} + λ_{t} + μ_{i t} .

(2)

Here, $Y_{i t}$ = 1 if student i makes a nonstructural move from their school in between years t − 1 and t. In the pushout model, $V P_{i t - 1}$ denotes students who are receiving a voucher and enrolled in a private school as of year t − 1. The term $L A_{i t - 1}$ indicates all students who are low-achieving at time t − 1. We estimated separate models for different indicators of low-achieving students, including being below the school average, below the state 25th percentile, and below the state 10th percentile. The interaction between the first two terms, $V P_{i t} L A_{i t - 1}$ , denotes voucher students who were low-achieving in their private school at time t − 1. We also included year fixed effects ( $λ_{t}$ ).

In the pushout analysis, the coefficient of interest (γ) on the interaction term ( $V P_{i t} L A_{i t - 1}$ ) in Equation 2 measures the added difference in the relative likelihood by which low-achieving voucher students exit a public school for a private school. To make sense of this estimate, it must be combined with other coefficients to assess patterns consistent with the claim of pushout of voucher students from private schools that is different from other relevant comparison groups.

First, we want to know whether the rate in which low-achieving, voucher-using students transition from their private school to a public school is different from the rate in which low-achieving students—who are also voucher-eligible—transition from their public school to any other schools without a voucher. Because not all voucher students change schools, we must also account for the baseline transfer rate of all voucher students (δ). A significant and positive linear combination of δ and γ compared with θ suggests evidence that lower-achieving voucher-using students are more likely to exit a private school above and beyond the baseline rate of low-achieving, voucher-eligible TPS students who make a transition (θ).

Second, we want to know whether the rate in which low-achieving, voucher-using students transition from their private school to a public school is different from the rate in which high-achieving, voucher-using students transition from their private school to a public school. Here, we must account for θ, which captures the baseline likelihood that low-achieving students are to transition schools, relative to high-achieving students. A significant and positive linear combination of θ and γ compared with δ suggests evidence that lower-achieving voucher-using students are more likely to exit a private school above and beyond the baseline rate of high-achieving voucher-using students who make the same transition (δ). Together, these estimates yield descriptive information about patterns in the data consistent with the claim of private schools pushing out lower-performing voucher students.

Using this same structural model, we also examined whether voucher-receiving ELL or special education students are more likely to transfer out of private voucher schools. In these analyses, an ELL or special education status indicator replaces the low-achieving student indicator, $L A_{i t - 1}$ in both the main effect and interaction term. Here, a positive and significant estimate of the combination of δ and γ compared with θ would be consistent with the claim that more-costly-to-educate voucher students are more likely to exit a private school relative to the exit rate of more-costly-to-educate voucher-eligible students enrolled in TPSs. Similarly, a significant and positive linear combination of θ and γ suggests evidence consistent with the claim that more-costly-to-educate voucher students are more likely to transition than their less-costly voucher peers.

Results

Cream Skimming Results

We display our cream skimming results in Table 1. The coefficient of interest is the interaction term with statistical significance for these estimates denoted with asterisks. Overall, we find little evidence consistent with the claim that private schools are cream skimming higher-performing, less disruptive, or less-costly-to-educate students from TPS. While all high-achieving students are less likely to transition schools, the differential transition rates for high-achieving voucher students are mostly null across all subjects and achievement thresholds. The other measures we use to assess cream skimming students yield no evidence consistent with the claims of private schools cream skimming less disruptive or less-costly-to-educate students.

Pushout Results

We display our pushout results in Table 2. Italicized estimates indicate a statistically significant difference in the likelihood of low-achieving/more-costly-to-educate voucher students moving from their private school (the sum of the voucher private baseline transition and the interaction term) compared with their low-achieving/more-costly-to-educate voucher-eligible peers who move from a TPS. Bold estimates indicate a statistically significant difference in the likelihood of low-achieving or more-costly-to-educate voucher students moving from their private school (the sum of the low-achieving/ELL/special education main effect and the interaction term) compared with their high-achieving or less-costly-to-educate voucher peers.

Overall, we find that lowest-achieving voucher students are exiting private schools at a modestly higher rate than their similarly low-achieving voucher-eligible TPS peers as well as their higher-achieving voucher private school peers. The transition rate for low-achieving voucher students is higher than their low-achieving, voucher-eligible TPS peers by 1 to 3 percentage points. Considering that voucher students in general are a percentage point less likely to leave private schools, a statistically significant differential transition rate of 2 percentage points only holds for the lowest-achieving students (below the 10th percentile statewide) when looking at the linear combination of these two terms. Low-achieving voucher students are also 3 to 9 percentage points more likely to move from a private school than their higher-achieving voucher peers. This evidence is consistent across all achievement thresholds.

When looking at the potential pushout of ELL and special education voucher students, the evidence is mixed. Voucher ELL students are 2 percentage points less likely to move from their private school as compared with voucher-eligible ELL peers who move from their TPS. Voucher ELL students are also 4 percentage points less likely to make a transition from their private school than their non-ELL voucher peers. Meanwhile, voucher special education students are 3 percentage points more likely to move a transition from their private school than their non-special education peers, though this rate is similar to the rate at which special education students move from a TPS.

Conclusion

While our analysis does not provide evidence consistent with the claim of voucher-participating private schools cream skimming the best students from TPSs based on ability, disciplinary background, or cost to educate, it raises concerns as to whether voucher programs are creating effective educational opportunities for the lowest-achieving students. As Indiana invests more than US$240 million of public funds annually in vouchers for students to attend private schools (Indiana Department of Education, 2022), policymakers should be wary of potential exacerbated educational inequalities and the challenges that low-performing students exiting out of private schools create for TPSs.

Supplemental Material

sj-pdf-1-epa-10.3102_01623737231183397 – Supplemental material for Cream Skimming and Pushout of Students Participating in a Statewide Private School Voucher Program

Supplemental material, sj-pdf-1-epa-10.3102_01623737231183397 for Cream Skimming and Pushout of Students Participating in a Statewide Private School Voucher Program by R. Joseph Waddington, Ron Zimmer and Mark Berends in Educational Evaluation and Policy Analysis

Footnotes

Acknowledgements

Author order determined randomly; each contributed equally to this article. This research was supported by the University of Notre Dame Center for Research on Educational Opportunity (CREO) and Institute for Educational Initiatives. We are grateful to the Indiana Department of Education for providing access to the state administrative records and for supporting independent analyses. We are also grateful for the helpful feedback we received from colleagues, including J. S. Butler, Stephen Cornman, Rajeev Darolia, Joseph Ferrare, Joshua Goodman, Alison Grantham, Adam Kho, Jodi Moon, Rob Olsen, Umet Ozek, Barbara Steel-Lowney, Marsha Silverberg, Molly Stewart, Genia Toma, and anonymous reviewers. All opinions expressed in this article represent those of the authors and not necessarily the institutions with which they are affiliated. All errors in this article are solely the responsibility of the authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a Lyle Spencer Research Award (#201600089) from the Spencer Foundation.

ORCID iDs

R. Joseph Waddington

Mark Berends

Authors

R. JOSEPH WADDINGTON, PhD, is an associate professor of educational policy studies and evaluation at the University of Kentucky College of Education and Martin School of Public Policy and Administration. His primary line of research focuses on the variation in impacts of school choice programs and policies on student outcomes.

RON ZIMMER, PhD, is a professor of public policy at the University of Kentucky, where he is the director of the Martin School of Public Policy and Administration. His research focuses on school choice and school reforms.

MARK BERENDS, PhD, is a professor of sociology at the University of Notre Dame, where he is the Hackett Family Director of the Institute for Educational Initiatives and director of the Center for Research on Educational Opportunity (CREO). His mixed-methods research focuses on how school organization and classroom instruction are related to student outcomes, with special attention to marginalized students and educational policies and reforms aimed at reducing educational inequalities. He is an elected member of the National Academy of Education and a fellow of the American Educational Research Association (AERA).

References

Berliner

D. C.

Glass

G. V.

(2014). 50 myths and lies that threaten America’s public schools: The real crisis in education. Teachers College Press.

Carlson

D. E.

Cowen

J. M.

Fleming

D. J.

(2013). Life after vouchers: What happens to students who leave private schools for the traditional public sector? Educational Evaluation and Policy Analysis, 35(2), 179–199.

Cowen

J. M.

Fleming

D. J.

Witte

J. F.

Wolf

P. J.

(2012). Going public: Who leaves a large, longstanding, and widely available urban voucher program? American Educational Research Journal, 49(2), 231–256.

Epple

D. E.

Romano

R. E.

(1998). Competition between private and public schools, vouchers, and peer-group effects. The American Economic Review, 88(1), 33–62.

Fleming

D. J.

Cowen

J. M.

Witte

J. F.

Wolf

P. J.

(2015). Similar students, different choices: Who uses a school voucher in an otherwise similar population of students? Education and Urban Society, 47(7), 785–812.

Indiana Department of Education. (2022). Choice scholarship program annual report: Participation and payment data.

Kho

Zimmer

McEachin

(2022). A descriptive analysis of cream skimming and pushout in choice versus traditional public schools. Education, Finance, and Policy, 17(1), 160–187.

Ravitch

(2010). The death and life of the great American school system. Basic Books.

Ravitch

(2013). Reign of error: The hoax of the privatization movement and the danger to America’s public schools. Random House.

10.

Winters

M. A.

(2017). New York charter schools outperform traditional selective schools: More evidence that cream-skimming is not driving charters’ success. Manhattan Institute.

11.

Zimmer

Gill

Booker

Lavertu

Witte

(2011). Charter schools: Do they cream skim, increasing student segregation? In Berends

Springer

M. G.

Ballou

Walberg

(Eds.), School choice and school improvement (pp. 215–232). Harvard Education Press.

12.

Zimmer

Guarino

(2013). Is there empirical evidence consistent with the claim that charter schools “push out” low performing students? Educational Evaluation and Policy Analysis, 35(4), 461–480.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.32 MB