Overview

While school choice policies are now ubiqitous and often presented as a strategy for addressing persistent racial disparities education, there is concern that such policies may maintain or increases such disparities due to resource hoarding by advantaged families or through increased racial segregation (Davis 2014; Roda and Wells 2013). More specifically, white families may use charters to exit from racially diversifying schools to maintain status hierarchies (Renzulli and Evans 2005). Using a group threat/status anxiety framework (Bobo and Hutchings 1996), I hypothesize that exposure to increased racial diversification may induce white families to enroll their children in charter schools. I distinguish between proximal exposure, where racial diversification occurs in neighboring school districts, and direct exposure, where racial diversification occurs within local traditional public schools.

I find evidence that:

  1. both proximal and direct exposure are associated with both increased foundings of charter schools and with increasd white enrollment in charter schools;
  2. this is concentrated in non-urban areas; and
  3. threat-induced white enrollment is associated with increased racial segregation.

Data

Panel of California school district data from 2000-2015 (n = 12,603 district-by-year observations; 990 unique districts)

Sources: The Common Core of Data; Small Area Income and Poverty Estimates (both accessed via the Urban Institutes API); NCES EDGE database

Charter schools in California:

Methods

Measures

  1. Proximal status threat (Andrews and Seguin 2015) \[ProxThreat_{ij} = \theta_{ij} * \gamma_{ij}\] where \(\theta_{ij} = \frac{White_{ij}}{Total_{ij}}\) and \(\gamma_{ij} = \frac{\sum_1^k Blk_{kj}, Hisp_{kj}}{\sum_1^k Total_{kj}}\)

  2. Direct status threat \[DirThreat_{ij} = \frac{\{BlkTPS{ij}, HispTPS{ij}\}}{\{TotalBlk_{ij},TotalHisp_{ij}\}}\]

  3. Racial segregation (white isolation) (Massey and Denton 1988) \[WhiteIsolation_{ij} = \sum_{s=1}^n[(\frac{x_{sj}}{X_{ij}})(\frac{x_{sj}}{t_{ij}})]\] where \(x_{sj}\) is the white enrollment in a school, \(X_{ij}\) is the total white enrollment in the district \(s\) is in, and \(t_{ij}\) is the total enrollment in the district.

Analytic models

  1. Does proximal and/or direct exposure increase charter enrollment for white students?
    \[Y_{ij} = \beta_1ProxThreat_{ij} + \beta_2DirThreat_{ij} + X_{ij}\beta + \alpha_i + \gamma_j + \epsilon_{ij}\]
  1. Does white enrollment induced by exposure contribute to racial segregation in schools?

First stage
\[\phi_{ij} = \pi_1ProxThreat_{ij} + \pi_1DirThreat_{ij}\]

Second stage: \[WhiteIsolation_{ij} = \beta_1\hat{\phi} + X_{ij}\beta + \alpha_i + \gamma_j + \epsilon_{ij}\]

Charter School Expansion and Enrollment

California passed its charter school law in 1992 and has seen steadily increased growth in the past two decades. Charter schools are authorized at the district-level. The median district consists of about 4 schools, with a mean of 9.21. The share of charter schools in the state has grown steadily in the state to about 12% in 2015. However, the distribution of charters across the state is uneven. About 67% of districts did not have a single charter school in 2015. Los Angeles City had 271 charter schools, representing 27% of the schools in the district. Some districts had a share of charter schools well over 50%–some up to 92%.

Descriptive Analysis

District characteristics Percent      
% increase in proximal exposure 43      
% increase in share of white students in charters 85.2    
% increase in share of charters 24.1    
Mean   SD   
% White 46.5 27.8 
% Black 3.4 5.55
% Hispanic 37.7 28   
% Poverty 18.1 10.5 
Variable Mean    SD    
%charters 5.46 13.6  
%White in charters 5.7  17.3  
Proximal exposure 17.4  12.3  
Direct exposure - %Black in TPS 3.31 5.73 
Direct exposure - %Hispanic in TPS 38.2  28.2  
%White in district 46.5  27.8  
Student-teacher ratio 22.9  266    
%SWD 9.32 4.64 
%Title I eligible schools 70.6  30.8  
%Magnet schools 1.3  6.44 
Enrollment (log) 7.37 1.89 
Local revenue (log) 15.5  1.9  
Per-student spending 9.23 0.364
%5-17 year olds in poverty 18.1  10.5  

Preliminary Results

Table 1 shows the results of the fixed effects regressions, identifying the association between within-district changes in various types of exposure and the share of charter schools in a district. I find a positive association between proximal exposure and direct exposure for Hispanic students.

Table 1: Fixed effects regression of share of charters on proximal and direct threat
(1) (2) (3) (4) (5)
Overall proximal exposure 0.220 *** 0.250 ***
(0.060) (0.061)
Proximal exposure - Black 0.347 0.356 *
(0.181) (0.180)
Proximal exposure - Hispanic 0.210 *** 0.241 ***
(0.060) (0.062)
Direct exposure - %Black(TPS) -0.124 -0.167 -0.165
(0.090) (0.091) (0.091)
Direct exposure - %Hispanic(TPS) 0.161 *** 0.174 *** 0.174 ***
(0.044) (0.045) (0.045)
District FEs X X X X X
Year FEs X X X X X
Controls X X X X X
N 14366 14366 14366 14366 14366
R-squared 0.769 0.769 0.769 0.772 0.772
*** p < 0.001; ** p < 0.01; * p < 0.05. TPS = Traditional public schools. Coefficient estimates based on OLS regression with district and year fixed effects. Standard errors clustered at the district level. Controls include the natural log of enrollment, local revenue, and per-student spending; the share of children in poverty; the share of children in poverty; the share of Title 1 eligible schools, students with disabilities, white students, and magnet schools in the district; and teacher-student ratio.

Likewise, proximal exposure and direct exposure for Hispanic students increase the share of white students enrolled in charter schools in a district (Table 2). For a 10 percentage point increase in the overall proximal exposure a district faces, the share of white students enrolled in the district increases by 4.25 percentage points.

Table 2: Fixed effects regression of share of white students enrolled in charters on proximal and direct threat
(1) (2) (3) (4) (5)
Overall proximal exposure 0.379 *** 0.425 ***         
(0.074) (0.076)         
Proximal exposure - Black 0.673 ** 0.692 ** 
(0.210) (0.215)   
Proximal exposure - Hispanic 0.356 *** 0.404 ***
(0.075) (0.078)   
Direct exposure - %Black(TPS) -0.053 -0.128 -0.122    
(0.110) (0.111) (0.112)   
Direct exposure - %Hispanic(TPS) 0.290 *** 0.313 *** 0.313 ***
(0.063) (0.064) (0.063)   
District FEs X X X X X        
Year FEs X X X X X        
Controls X X X X X        
N 14366 14366 14366 14366 14366        
R-squared 0.787 0.787 0.788 0.792 0.792    
*** p < 0.001; ** p < 0.01; * p < 0.05. TPS = Traditional public schools. Coefficient estimates based on OLS regression with district and year fixed effects. Standard errors clustered at the district level. Controls include the natural log of enrollment, local revenue, and per-student spending; the share of children in poverty; the share of children in poverty; the share of Title 1 eligible schools, students with disabilities, white students, and magnet schools in the district; and teacher-student ratio.

The results are concentrated in non-urban areas. If I run the model separately for districts in non-urban and urban areas, I find that both proximal exposure and direct Hispanic exposure in non-urban schools are associated with both charter foundings and increased white enrollment in charter schools, but this was not the case. The coefficient for proximal threat in urban district was positive for charter foundings and white enrollment and similar in magnitude, but I cannot rule out that the true value is zero.

Table 3: Results for urban and non-urban school districts
Share of Charters White Enrollment in Charters
Non-urban white enrollment Urban white enrollment Non-urban %Charter Urban %Charter
Proximal exposure 0.411 *** 0.347 0.249 *** 0.309
(0.075) (0.287) (0.061) (0.287)
Direct exposure - %Black(TPS) -0.141 -0.012 -0.109 -0.392
(0.120) (0.240) (0.096) (0.237)
Direct exposure - %Hispanic(TPS) 0.325 *** -0.132 0.191 *** -0.175
(0.067) (0.159) (0.047) (0.160)
District FEs X X X X
Year FEs X X X X
Controls X X X X
N 12608 1758 12608 1758
R-squared 0.794 0.904 0.774 0.866
*** p < 0.001; ** p < 0.01; * p < 0.05. TPS = Traditional public schools. Standard errors clustered at the district level. Controls include the natural log of enrollment, local revenue, and per-student spending; the share of 5-17 in poverty; the share of children in poverty; the share of Title 1 eligible schools, students with disabilities, and magnet schools in the district; and teacher-student ratio.

Finally, I evaluate the role of threat-induced white enrollment in charter schools on district-level segregation. I assess segregation in terms of white isolation, which reflects the probability that a white student shares a school with a student of color (Massey and Denton 1988). I use two-stage least squares approach to determine (1) the variation in share of white enrollment that is associated with the exposures measures and (2) the association between those fitted values and the level of white isolation in the district. I find that districts with increases in threat-induced enrollment had increased white isolation. A one-percentage-point increase in white enrollment in charter school that is associated with proximal exposure is associated with abou a 2.5 percentage-point increase in white isolation.

Table 4. Results of 2SLS regression of threat-induced enrollment in charters on white isolation.
Overall Proximal Exposure Proximal Exposure - Black Proximal Exposure - Hispanic Direct Exposure - Black Direct Exposure - Hispanic Proximal & Direct Exposure
Threat-induced white enrollment in charters 2.615 *** 2.307 *** 2.661 *** 0.150  0.056  0.230 ***
(0.551)    (0.676)    (0.608)    (0.087) (0.048) (0.049)   
N 14366         14366         14366         14366      14366      14366        
R-squared 0.389     0.521     0.367     0.968  0.969  0.967    
*** p < 0.001; ** p < 0.01; * p < 0.05. Threat-induced white enrollment are fitted values from first stage regression of exposure variables on share of white students enrolled in charter schools. Second stage regression includes controls for overall share of black and hispanic students, student-teacher ratio, the share of students with disabilities, the share of Title I eligible and magnet schools, the natural log of enrollment, local revenue, and per student spending, and the share of 5-17 year olds in poverty in the district. All models include district and year fixed effects in the second stage. Standard errors clustered at the district level.

Summary and implications

References

Andrews, Kenneth T, and Charles Seguin. 2015. “Group Threat and Policy Change: The Spatial Dynamics of Prohibition Politics, 1890-1919.” American Journal of Sociology 121 (2): 475–510.

Bobo, Lawrence, and Vincent L Hutchings. 1996. “Perceptions of Racial Group Competition: Extending Blumer’s Theory of Group Position to a Multiracial Social Context.” American Sociological Review 61 (6): 951. https://doi.org/10.2307/2096302.

Davis, Tomeka M. 2014. “School Choice and Segregation: ‘Tracking’ Racial Equity in Magnet Schools.” Education and Urban Society 46 (4): 399–433. https://doi.org/10.1177/0013124512448672.

Massey, Douglas S., and Nancy A. Denton. 1988. “The Dimensions of Residential Segregation.” Social Forces 67 (2): 281–315. https://doi.org/10.1093/sf/67.2.281.

Renzulli, Linda A, and Lorraine Evans. 2005. “School Choice, Charter Schools, and White Flight.” Social Problems 52 (3): 398–418.

Roda, Allison, and Amy Stuart Wells. 2013. “School Choice Policies and Racial Segregation: Where White Parents’ Good Intentions, Anxiety, and Privilege Collide.” American Journal of Education 119 (2): 261–93. https://doi.org/10.1086/668753.