Education Rights Project

Building out tables & graphics for public sharing on Education Rights and Enforcement in U.S. States (Claude assisted)

US_State_Education_ProvisionsDownload

National

National Funding Gap & Outcomes, 2009–2024
National District Adequacy, 2009–2024
National Funding Gap WidgetDownload
School Finance in America (Slide Deck)Download

In progress: Global Education Rights project tied to Global Education Adequacy Estimates Project. More coming!

State-Specific Information

A few items are still being finalized and will go live shortly.

Connecticut

Connecticut Adequacy, 2009–2024: Hartford & Bristol
Connecticut School Finance: Full Overview

Florida

Florida District Outcomes Scatter Animation

New York State Small City School Districts

New York Small City Adequacy: Utica & Poughkeepsie

Kansas & Missouri

Kansas & Missouri Adequacy Outcomes, 2009–2024
Kansas City Metro Adequacy, 2009–2024: Kansas City, MO & Kansas City, KS

New Jersey

New Jersey District Adequacy vs. Massachusetts Standard, 2009–2024
New Jersey School Funding Outcome Gaps, 2019–2024

Minnesota

Minnesota Funding Gaps & Outcome Gaps, 2009–2024: National vs. Massachusetts Standards

Texas

Digging Out: Restoring Equal Educational Opportunity in Texas (November 2026). Texas ranked 6th of 49 states in school funding effort in 1993 and 35th in 2024. Its highest-poverty districts now spend about half of what national-average outcomes cost, 46th of 48 states, and 98% of Texas students attend districts spending below that cost. Appendix A re-runs the analyses of the 2012 school finance trial expert report through 2025; the inequities documented then remain, and several are larger. Stata replication packages reproduce every figure.

Texas District Spending as a Share of the Cost of National-Average Outcomes, 2009–2024
Texas Funding Gaps & Outcome Gaps, 2009–2024: Majority Black & Hispanic Districts vs. All Others

International

Mexico

Risk and Spending in Mexican Public Basic Education (October 2026). Applies the risk and spending analysis steps of the equal educational opportunity framework to more than 113,000 public preschools, primarias and secundarias, linking federal FONE payroll by school to the Formato 911 census, PLANEA results and 2020 Census community measures. Federal school payroll is regressive: comparing similar schools within the same state, schools in the highest-need communities receive about three-quarters (0.77) of the payroll per pupil of schools in low-need communities. No state is progressive once school size and remoteness are accounted for, and the highest-need state, Chiapas, has the lowest payroll per pupil in the country.

Map of Mexican states shaded by the ratio of federal payroll per pupil in high-need versus low-need schools, adjusted for school size, remoteness and labor costs. Every state is below 1.0; Baja California Sur and Nuevo León are lowest, Coahuila highest.
Within-state equity ratio of federal (FONE) payroll per pupil, high-need vs. low-need schools
Scatter plot of Mexican states: average community need on the horizontal axis and FONE payroll per pupil relative to the national average on the vertical axis. The trend slopes downward; Chiapas has the highest need and lowest payroll.
State average need vs. federal payroll per pupil, 2023-24
Dot map of public schools in Guerrero, Oaxaca and Chiapas colored by payroll per pupil relative to similar schools in the same state, from under 80 percent (brown) to over 125 percent (teal).
Guerrero, Oaxaca and Chiapas: school payroll per pupil vs. similar schools, 2023-24

Brazil

Risk and Spending in Brazilian Municipal School Networks (October 2026). Applies the risk and spending analysis steps of the equal educational opportunity framework to Brazil’s 5,560 municipal school networks, linking SAEB results, INEP’s school socioeconomic index, the Censo Escolar, the 2022 Census and Cadastro Único poverty data with SIOPE spending for 2019–2023. Nationally, municipal spending is sharply regressive: high-need networks spend about two-thirds (0.65) as much per pupil as low-need networks. Most of the gap lies between states, where need and spending correlate at −0.78. Within states, where FUNDEB pools revenue, spending is roughly need-neutral (0.96) and tracks local revenue; the 2021 FUNDEB reform moved within-state distributions toward neutrality.

Scatter plot of Brazilian states: average municipal network need on the horizontal axis and municipal spending per pupil relative to the national average on the vertical axis. The trend slopes steeply downward; São Paulo is high-spending and low-need, Maranhão and Pará high-need and low-spending.
State average need vs. municipal spending per pupil, 2023
Map of Brazilian states shaded by the ratio of municipal spending per pupil in high-need versus low-need networks within each state, adjusted for size, remoteness and labor costs.
Within-state ratio of municipal spending per pupil, high-need vs. low-need networks, 2019–2023
Map of Brazilian municipalities shaded by municipal network spending per pupil in 2023 relative to the median network in the same state.
Municipal network spending per pupil relative to the state median, 2023

France

Risk and Resources in French Public Collèges (October 2026). Applies the risk and resource analysis steps of the framework to about 5,300 public collèges, linking brevet results, grade-6 national tests and the social position index (IPS) of each collège’s intake with the teaching hours per pupil the State allocates to each collège, 2023–2025. Because teachers are paid on national scales and allocated as hours, the State layer is measured in hours rather than euros. Unlike Mexico and Brazil, the State allocation is progressive: within académies, high-need collèges receive about 15% more teaching hours per pupil, mostly as smaller groups. Most of that runs through the 2015 REP/REP+ map, which leaves many disadvantaged collèges outside it. Mayotte, the highest-need académie, receives only average hours, and département spending on collèges is unequal and unrelated to need.

Line chart of teaching hours per pupil by need index for French public collèges in 2025, with separate lines for collèges outside priority education, REP and REP+. At every level of need, REP and REP+ collèges receive more hours than collèges outside the network.
Teaching hours per pupil by need and éducation prioritaire status, 2025
Scatter plot of French académies: mean collège need on the horizontal axis and teaching hours per pupil on the vertical axis. Hours rise with need in metropolitan France; Mayotte, with the highest need, sits near the national average.
Académie mean need vs. teaching hours per pupil, 2025
Dot map of public collèges in Paris and the inner suburbs colored by teaching hours per pupil relative to collèges of the same size in the same académie. Collèges in Seine-Saint-Denis receive more hours; central and western Paris collèges fewer.
Paris and the inner suburbs: collège teaching hours vs. similar collèges, 2025

South Korea

Risk and Resources in Korean Public Schools (October 2026). Applies the risk and spending analysis steps of the framework to about 9,900 public elementary, middle and general high schools enrolling 4.2 million pupils, linking School Info (학교알리미) disclosures of classes, teachers, staff, school accounts and graduates’ destinations for 2019–2025 with district welfare, health-insurance and pension-income data. Korea does not publish achievement by school, so risk is measured by progression: in the highest-need tenth of middle schools, 21% of graduates enter vocational high schools and under 1% selective ones, against 9% and 8% in the lowest-need tenth. Raw comparisons make the system look progressive, because high-need districts have many small rural schools with generous staffing. But comparing schools of similar size and remoteness within the same office of education, classroom teachers and school budgets per pupil are essentially need-neutral (ratios of 1.01–1.02). Parent payments to school accounts rise with district income, and size-adjusted teacher supply leans slightly toward the lowest-need offices, Sejong and Seoul.

Grayscale map of South Korea's districts shaded by the need index. Need is lowest in southeastern Seoul, Sejong and Gyeonggi suburbs, and highest in rural districts of the Jeolla and Gyeongsang provinces and Gangwon.
Need index by district (pupil-weighted), 2024
Line chart: as need rises, the share of middle-school graduates entering vocational high schools climbs from about 10% to over 20% while the share entering selective high schools falls from about 11% to under 1%.
Where middle-school graduates go, by need, 2019–2025
Bar charts: ratio of classroom teachers and school-account spending per pupil at high versus low need is 1.12 to 1.35 with only need and year controlled, and about 1.0 once school size, rurality and office are held constant.
Resources at high vs. low need: the apparent advantage disappears once school size is held constant
Scatter plot of Korea's offices of education: size-adjusted classroom teachers per pupil decline as mean need rises; Sejong and Seoul are above average, Busan and Incheon below.
Offices of education: need vs. size-adjusted classroom teachers per pupil, middle schools, 2024
Grayscale map of parent-paid school revenue per pupil by district in fiscal 2023, highest in central and southeastern Seoul.
Parent-paid school revenue per pupil by district, fiscal 2023

England

Risk and Resources in English State Schools (October 2026). Applies the risk and resource analysis steps of the framework to about 15,800 primary and 3,350 secondary schools, maintained and academies, linking Key Stage 2 and 4 results and school census data with school-level spending from the Department for Education’s benchmarking tool (2021/22–2024/25), workforce and pay data, and 2026-27 national funding formula allocations. Free school meal eligibility and SEN support are the strongest predictors of results; English as an additional language barely predicts them, though the formula funds it. Comparing schools of similar size and location in the same local authority, high-need schools spend about 21% more per pupil in primary and 16% more in secondary, close to the formula allocation, and every local authority is progressive. But the extra money buys teaching assistants far more than teachers (1.37 and 1.90 against 1.14 and 1.06), and teacher pay does not rise with need. Against outcome gaps of 1.2 to 1.6 standard deviations, a 20% differential is modest. England’s open data meet every requirement for a school-level cost function, making it the strongest candidate so far for Step 2.

Grayscale dot map of England's state-funded schools shaded by need index, with a London inset. Need is concentrated in inner London, the North East, Merseyside, Greater Manchester and Birmingham.
School need index, all state-funded mainstream schools
Bar charts of resources at the 90th versus 10th percentile of need in English primary and secondary schools: NFF allocation and spending about 1.2, teachers 1.06 to 1.14, teaching assistants 1.37 to 1.90.
Resources at high vs. low need, by phase and specification
Bar charts of NFF notional allocation and actual spending per pupil by need decile, rising from about £6,800 to £8,700 in primary and £7,700 to £9,700 in secondary schools.
Formula allocation and actual spending per pupil by need decile
Line charts by need decile: teachers per 100 pupils rise modestly, teaching assistants per 100 pupils rise steeply, and mean teacher pay is flat.
Teachers, teaching assistants and teacher pay by need decile, 2024/25
Grayscale map of English local authorities shaded by the ratio of primary spending per pupil in high- versus low-need schools within each authority; all are above 1.0.
Within-authority progressivity of primary spending per pupil, 2021/22–2024/25

Australia

Risk and Resources in Australian Schools (October 2026). Applies the risk and resource analysis steps of the framework to about 9,350 government, Catholic and independent schools, 2015–2025, using ACARA’s open school profile and finance data, Victorian senior secondary results and NSW school-level formula funding. ICSEA, ACARA’s index of socio-educational advantage, alone explains 73% of the variation in Victorian schools’ median VCE study scores, and sector adds nothing once it is held constant. Government schools staff progressively: comparing similar schools in the same state, high-need schools have 1.15 times the teaching staff per student. The independent sector is regressive (0.72), and with independent schools enrolling the most advantaged students and drawing fee income on top of public funding, the system as a whole is barely progressive (1.07). NSW’s Resource Allocation Model shows how targeted funding can be steeply progressive. A cost function is not feasible with open data but would be with ACARA’s licensed school-level NAPLAN and finance data, and would allow the Schooling Resource Standard loadings to be tested.

Grayscale dot map of Australian schools shaded by ICSEA in 2025; darker dots (lower ICSEA, more need) are concentrated in remote northern and central Australia.
School ICSEA, 2025 (darker = more need)
Bar charts of teaching and non-teaching staff per student at the 90th versus 10th percentile of need, by sector: government schools are progressive, independent schools regressive.
Staffing at high vs. low need, by sector and specification
Staff per 100 students and share of students by sector, by need decile; independent schools enrol many of the most advantaged students and very few of the highest-need.
Staffing and sector of enrolment by need decile, 2025
Stacked bars of recurrent income per student by source for government, Catholic and independent schools in 2024; independent schools have the highest total because of fees and private income.
Recurrent income per student by source and sector, 2024
Bar chart of NSW Resource Allocation Model funding per student by need decile in 2025, rising from about $770 to about $5,100.
NSW Resource Allocation Model funding per student by need decile, 2025