How Do We Identify and Count Charter Schools?

The National Alliance for Public Charter Schools manages a database that serves as a repository for charter school data. Data are collected from a variety of sources including:

  • State data files containing enrollment, directory, and performance information
  • Federal datasets, such as Common Core of Data (CCD), the Civil Rights Data Collection (CRDC), and EdFacts
  • State and local partners, and in some cases, individual schools

In addition to quality control and verification, the data are processed and categorized based on the type of record.

How Do We Categorize Schools?

Each database record receives a Modified Count code. However, a single record—typically represented as a row or unique identification number in a dataset—is not necessarily school. For example, the record may represent a program within a school, or a campus associated with another school. These record types can vary between federal and state datasets, and appear frequently among charter school records. The Modified Count code indicates whether the school is a campus, charter holder, program, or indeed a school. Accounting for and correcting these discrepancies when possible is essential for generating accurate counts of charter schools, openings and closures, and other important metrics. The National Alliance’s Modified Count code allows researchers to consistently classify records across data sources and school years. Notably, campus records have increased over time. Between the 2005-06 and 2018-19 school years, the share of campuses relative to total records has doubled from 4% to 8%.

Charter Record Type by Year

Year Schools Campuses Holders Schools Campuses Holders
2005-06 3,643  133  15  96.1%  3.5%  0.4% 
2006-07 3,923  167  18  95.5%  4.1%  0.4% 
2007-08 4,190  202  18  95.0%  4.6%  0.4% 
2008-09 4,480  251  23  94.2%  5.3%  0.5% 
2009-10 4,735  296  31  93.5%  5.8%  0.6% 
2010-11 5,025  342  35  93.0%  6.3%  0.6% 
2011-12 5,359  400  40  92.4%  6.9%  0.7% 
2012-13 5,697  431  41  92.3%  7.0%  0.7% 
2013-14 6,027  490  47  91.8%  7.5%  0.7% 
2014-15 6,254  560  47  91.2%  8.2%  0.7% 
2015-16 6,426  580  48  91.1%  8.2%  0.7% 
2016-17 6,591  611  49  90.9%  8.4%  0.7% 
2017-18 6,683  631  41  90.9%  8.6%  0.6% 
2018-19 6,922  611  38  91.4%  8.1%  0.5% 

Some states appear disproportionately impacted by campuses and holder records (i.e. “non-school records”). States with some of the largest charter school sectors, such as Washington D.C., Texas, and Florida, also have some of the highest proportions of non-school records. For more information on this issue please see our published white paper on the matter

States With More than 5% of Records as Non-Schools

State Holders Campuses Share
OK 0 16 27.1%
DC 2 24 21.0%
NH 0 7 20.0%
AR 1 16 19.5%
TN 0 20 17.5%
MO 0 12 15.8%
MN 1 35 15.4%
TX 1 125 15.1%
FL 2 85 13.3%
CO 5 21 9.8%
NV 0 8 9.8%
RI 0 3 8.3%
CA 0 111 8.2%
AZ 1 42 7.7%
IN 1 7 7.6%
MI 0 25 6.8%
OH 1 18 5.9%
NJ 6 0 5.1%

How Do We Account For Changing School Identification Codes?

State and federal (NCES) school ID codes can sometimes change from year to year. The National Alliance Data & Research Team developed an identification system to address this, whereby data is merged together into our Database of Charter Schools. The system establishes a unique “NAPCS ID” that allows the team to identify schools across time, regardless of whether state or federal IDs have changed.

State and federal IDs are typically embedded with information, such as a code to identify a school’s district or county, for example. Therefore, if underlying information relating to the code changes, like the redistricting of a school, the ID will change as well. In this situation, the school remains operational, but the school’s original ID may be eliminated, making it hard to determine if the school is still open, or to track the school properly over time. In other words, the creation or elimination of a state or federal school ID is not necessarily connected to a school’s operational status. Accounting for these sorts of changes is important for accurately counting school openings and closings, especially for charter schools. Changes in IDs without careful attention might incorrectly look like the closures of schools and opening of new schools. Between the 2005-06 and 2018-19 school years, federal NCES IDs for charter schools changed almost 1,600 times. NCES IDs for some charter schools even changed multiple times. This means that 14% of all charter school records across the 2005-06 to 2018-19 period saw an NCES ID change, but did not have any operational changes.

Some states appear to have more NCES ID changes than others, as a proportion of the state’s total records. The reasons for changing IDs vary depending on state context. For example, in some cases an authorizer change my cause an NCES ID change. In the case of Louisiana, numerous schools received changes to their NCES ID when they moved from the Recovery School District (RSD) to the Orleans Parish School Board. Conversions, mergers, and expansions are other factors that can lead to an NCES ID change.

Top 10 States With the Most NCES Changes

States Total Unique Records Number of Changes Percent Changed
CA 1,769  985  55.7% 
LA 206  104  50.5% 
OK 70  26  37.1% 
NM 121  37  30.6% 
TN 135  30  22.2% 
MO 115  21  18.3% 
SC 98  17  17.3% 
TX 1,078  167  15.5% 
ID 72  10  13.9% 
CO 310  24  7.7% 
WI 425  27  6.4% 

How Is Demographic Data Collected?

Demographic data for student racial/ethnic backgrounds were collected using a combination of CCD and state-level datasets. Demographic data for “Other/Two or More Races”, “Native American/Alaskan Native”, and “Pacific Islander/Hawaiian Native” classifications were summed into a single category labeled “Other”, along with any missing demographic enrollment, for the purposes of this report. As individual groups, these populations constituted very low percentages of the overall population in both charter and district schools with a relatively steady trend. However, it should be noted that “Other/Two or More Races” alone reached greater than 4% of the population for the first time in 2018-19. At the state level , “Other/ Two or More Races,” “Native American/ Alaskan Native” and “Pacific Islander/ Hawaiian Native” do provide a deeper understanding of demographic context for charter schools and district schools.

Demographic data for students with disabilities (SWDs), economically disadvantaged students (EDSs), and low English proficiency students (LEPSs) were collected using EdFacts datasets from the 2009-10 through the 2017-18 school years. Using EdFacts provided a more consistent and up-to-date report of SWD, EDS, and LEPS data for the years examined, compared to other datasets available at the time of this draft. Although EdFacts reflects the population of students participating in state assessments, rather than total student enrollment, careful review by the NAPCS Data and Research team determined that EdFacts participation data was a valid and representative sample of the overall student population. EdFacts data did not significantly skew the prevalence rates for the special populations in question. The data presented in this report was found by averaging the values of the Math EdFacts and ELA EdFacts data in each appropriate year.

How Do We Distinguish School Districts?

Because charter schools are often coded as their own districts on the national level, we assign all charter schools a geographic school district in order to find school and enrollment share. We acquire addresses for every public charter and district school from CCD, as well as our own collection process. We then use Geocodio to find latitude and longitude values for each school and use ArcGIS to plot those values on public school district shapefiles. This allows the NAPCS Data and Research Team to see which geographic school districts charter schools would fall into if they were coded as part of that district. Locale data is derived from CCD.

NAPCS eliminates virtual schools* in enrollment share analyses. Most virtual schools are assigned a “virtual flag” from CCD, but the NAPCS Data and Research Team also conducts a manual check for schools with virtual identifiers in their name (i.e. “virtual,” “online,” “digital,” and “remote”). Further, the team checks for schools with student populations far exceeding what is expected based on location information, which often indicates virtual enrollment.

(*Note: Data in the 2020 edition of the Charter School Data Digest includes data from the 2005-06 through 2018-19 school years and does not cover data relating to the COVID-19 pandemic. The term “virtual schools” in this report therefore does not apply to schools that have adopted virtual, remote, hybrid, or otherwise socially distanced practices in response to the pandemic.)

How Is Poverty Data Collected?

Poverty data and census tract information is collected similarly to the methodology for identifying school districts, described above. After excluding virtual schools*, the NAPCS Data and Research Team uses addresses acquired from CCD, longitude/latitude values from Geocodio, and census tract shapefiles from the Census Bureau to plot schools using ArcGIS.

Once a school has been plotted in a census tract, we use five-year estimation data from the 2018 American Community Survey to determine if the census tract is “high-poverty”. The threshold for “high-poverty” is 20% or more of the population below the poverty line. With this information, NAPCS is able to determine and how many schools are located in a high-poverty area and their enrollment.

(*Note: Data in the 2020 edition of the Charter School Data Digest includes data from the 2005-06 through 2018-19 school years, and does not cover data relating to the COVID-19 pandemic. The term “virtual schools” in this report therefore does not apply to schools that have adopted virtual, remote, hybrid, or otherwise socially-distanced practices in response to the pandemic.)

Measuring School Finances

Data on school finances are obtained from the 2006-07 through 2016-17 National Public Education Financial Survey. The survey contains an indicator for whether the district has charter schools. To isolate the financing of each sector, we only use data from districts that are exclusively charter schools or exclusively district schools. Because of this, we do not have the complete universe of data for either charter or district schools. We also exclude states for which we do not have data on at least 50 percent of charter schools. Still, we account for approximately 60 percent of charter schools, though we notably are missing data from California, New York, and Florida, three states with some of the largest charter school sectors. We also adjust for inflation using 2016-17 constant dollars.

About the Authors

Jamison White
Jamison White

Sr. Manager, Data & Research

Before joining the National Alliance in 2017, Jamison worked as a financial and small-business consultant in Pittsburgh, Boston, and the greater New York area. Jamison studied at Carnegie Mellon University and Philipps-Universität Marburg, Germany. He is a part of a founding group for a classical charter school in Washington, DC. In his free time, Jamison researches school curricula, pedagogies, and charter school models.

Adam Gerstenfeld
Adam Gerstenfeld

Manager, Data and Research

Before joining the team, Adam was a 1st-grade teacher at Lenora B. Smith Elementary in Miami, Florida where he taught reading, writing, math, science, and social studies. Adam received his bachelor’s degree in broadcast journalism from the University of Florida, where he was a radio and television producer for the local NPR and PBS stations. He is currently pursuing his master’s degree in government analytics from Johns Hopkins University.