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entitled 'School Finance: Per-Pupil Spending Differences between 
Selected Inner City and Suburban Schools Varied by Metropolitan Area' 
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Report to the Ranking Minority Member, Committee on Ways and Means, 
House of Representatives: 

United States General Accounting Office: 

GAO: 

December 2002: 

School Finance: 

Per-Pupil Spending Differences between Selected Inner City and Suburban 
Schools Varied by Metropolitan Area: 

GAO-03-234: 

GAO Highlights: 

Highlights of GAO-03-234, a report to the Committee on Ways and Means, 
House of Representatives 

SCHOOL FINANCE: 

Per-Pupil Spending Differences between Selected Inner City and Suburban 
Schools Varied by Metropolitan Area:  

Why GAO Did This Study: 

The No Child Left Behind Act of 2001 has focused national attention on 
the importance of ensuring each child’s access to equal educational 
opportunity. The law seeks to improve the performance of schools and 
the academic achievement of students, including those who are 
economically disadvantaged. 

The Congress, among others, has been concerned about the education of 
economically disadvantaged students. This study focused on per-pupil 
spending, factors influencing spending, and other similarities and 
differences between selected high-poverty inner city schools and 
selected suburban schools in seven metropolitan areas: Boston, Chicago, 
Denver, Fort Worth, New York, Oakland, and St. Louis. 

What GAO Found: 

Among the schools GAO reviewed, differences in per-pupil spending 
between inner city and suburban schools varied across metropolitan 
areas, with inner city schools spending more in some metropolitan areas 
and suburban schools spending more in other areas. The inner city 
schools that GAO examined generally spent more per pupil than suburban 
schools in Boston, Chicago, and St. Louis, while in Fort Worth and New 
York the suburban schools in GAO’s study almost always spent more per 
pupil than the inner city schools. In Denver and Oakland, spending 
differences between the selected inner city and suburban schools were 
mixed. In general, higher per-pupil expenditures at any given school 
were explained primarily by higher staff salaries regardless of whether 
the school was an inner city or suburban school. Two other explanatory 
factors were student-teacher ratios and ratios of students to student 
support staff, such as guidance counselors, nurses, and librarians. 
Federal funds are generally targeted to low-income areas to compensate 
for additional challenges faced by schools in those areas. In some 
cases, the infusion of federal funds balanced differences in per-pupil 
expenditures between the selected inner city and suburban schools. 

There is a broad consensus that poverty itself adversely affects 
academic achievement, and inner city students in the schools reviewed 
performed less well academically than students in the suburban schools. 
The disparity in achievement may also be related to several other 
differences identified in the characteristics of inner city and 
suburban schools. At the schools GAO visited, inner city schools 
generally had higher percentages of first-year teachers, higher 
enrollments, fewer library resources, and less in-school parental 
involvement--characteristics that some research has shown are related 
to school achievement. 

[See PDF for image] 

[End of figure] 

www.gao.gov/cgi-bin/getrpt?GAO-03-234 

To view the full report, including the scope and methodology, click on 
the link above. For more information, contact Marnie Shaul at (202) 512-
7215 or shaulm@gao.gov 

[End of section] 

Contents: 

Letter: 

Results in Brief: 

Background: 

Spending Differences between Selected Inner City and Suburban Schools 
Varied by Metropolitan Area: 

Inner City Schools Generally Faced Greater Challenges That May Have 
Affected Student Achievement: 

Conclusions: 

Agency Comments: 

Appendix I: Objectives, Scope, and Methodology: 

Appendix II: School Profiles: 

Appendix III: GAO Contacts and Staff Acknowledgments: 

GAO Contacts: 

Acknowledgments: 

Tables: 

Table 1: Total Enrollment and Percentages of Children in Poverty, 
Students with Disabilities, and Students with Limited English 
Proficiency for Selected Schools in the Seven Metropolitan Areas 
Reviewed: 

Table 2: Spending Per Pupil, Average Teacher Salary, Student-Teacher 
Ratio, and Student-Support Staff Ratio at the Median Spending School in 
Each Metropolitan Area: 

Table 3: Per-Pupil Spending with and without Federal Dollars for 
Selected Inner City and Suburban Schools in Seven Metropolitan Areas: 

Table 4: Metropolitan Areas Selected for Study: 

Table 5: Selected Inner City Census Tracts and Child Poverty Rates: 

Table 6: Selected Suburban School Districts’ Child Poverty Rates: 

Table 7: School Characteristics, Assessment Measure, and Measurement 
Description: 

Table 8: Regression Results for Factors Explaining Differences in Per- 
Pupil Spending at Selected Schools: 

Table 9: School-Level Student Characteristics for Selected Schools in 
Seven Metropolitan Areas: 

Table 10: Spending Per Pupil and Spending Per Pupil at Low, Medium, and 
High Weights for Selected Schools in Seven Metropolitan Areas: 

Table 11: Percent of First-Year Teachers, Federal Dollars Per Pupil, 
and Federal Dollars as a Percent of Total Spending at Selected Schools 
in Seven Metropolitan Areas: 

Figures: 

Figure 1: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in Metropolitan Areas Where Inner City Schools Spent More than Suburban 
Schools: 

Figure 2: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in Metropolitan Areas Where Suburban Schools Spent More than Inner City 
Schools: 

Figure 3: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in the Denver and Oakland Metropolitan Areas: 

Figure 4: Spending Per Pupil by the Median Inner City and Suburban 
School in Four Metropolitan Areas for Different Weight Adjustments for 
Students’ Needs: 

Figure 5: Average Student Achievement Scores for Selected Schools in 
Fort Worth, New York, Oakland, and St. Louis: 

Figure 6: Percentage of First-Year Teachers by School and Metropolitan 
Area: 

Figure 7: Student Enrollments at Selected Schools: 

Figure 8: Playgrounds of an Inner City School in St. Louis and a 
Neighboring Suburban School: 

Figure 9: Number of Library Books per 100 Students at Selected Schools: 

Abbreviations: 

Ed: TrustEducation Trust: 

NAEP: National Assessment of Educational Progress: 

SMSA: Metropolitanstatistical area: 

United States General Accounting Office: 

Washington, DC 20548: 

December 9, 2002: 

The Honorable Charles Rangel
Ranking Minority Member
Committee on Ways and Means
House of Representatives: 

Dear Mr. Rangel: 

The No Child Left Behind Act of 2001 has focused national attention on 
the importance of ensuring each child’s access to equal educational 
opportunity. The law seeks to improve the performance of schools and 
the academic achievement of students, including those who are 
economically disadvantaged. The heightened challenge of meeting the 
act’s new accountability requirements underscores the necessity of 
ensuring that all schools have the support they need to provide 
students with a quality public education. The challenge is particularly 
great for inner city schools serving low-income neighborhoods, where 
students on average continue to perform below students in suburban 
areas. The Congress and other policymakers have been concerned that 
this achievement gap may be related to possible differences in the 
amount of funding and resources available to low-income schools and 
school districts and affluent schools and school districts. Research 
has shown that such funding gaps are common at the district level; for 
example, a recent study by The Education Trust found that in 30 of the 
47 states studied, school districts with the greatest numbers of poor 
children had less money to spend per student than districts with the 
fewest poor children.[Footnote 1] However, little research has been 
done at the school level. 

To provide you with information about inner city school spending and 
other school characteristics, we determined similarities and 
differences between selected inner city and suburban schools in (1) per-
pupil spending and (2) other factors that may relate to student 
achievement, such as teacher experience, school enrollment, educational 
facilities and materials, and types of parental involvement. 

This study focuses on differences between inner city and suburban 
schools, and as such is distinct from a study of differences between 
urban and suburban schools; inner city schools, as a subset of urban 
schools, are in the central core of the city and have higher poverty 
rates. We selected 42 schools, 21 inner city and 21 suburban public 
elementary schools, to gather information on (1) school level, per- 
pupil spending, and federal revenues and (2) school, teacher, other 
staff, and student characteristics for the 2000-01 school year. We 
analyzed data from three inner city and three suburban schools from 
each of seven different Metropolitan Areas of medium, large, and very 
large population sizes: Oakland and St. Louis (medium); Boston, Denver, 
and Fort Worth (large); and New York and Chicago (very large).[Footnote 
2] In analyzing these data, we applied weights--a technique that 
allowed us to make adjustments to account for varying compositions of 
student need. We applied three different levels of weights. 

To obtain a selection of “typical” schools, we chose the inner city 
schools in each Metropolitan area that were at the median for poverty 
among the inner city schools; similarly we chose the school districts 
at the median for poverty among the suburban school districts. We 
attempted also to include one high-performing inner city school in each 
Metropolitan area we visited, but were able to identify only two high- 
performing inner city schools--1 in St. Louis and 1 in Oakland. For 
this selection, we used The Education Trust database, which includes 
high-performing schools in low-income areas. We did not include high- 
performing schools that were special schools (e.g., magnet schools, 
science academies, etc.)[Footnote 3] 

In addition, we visited 24 of the 42 selected schools in the New York, 
St. Louis, Fort Worth, and Oakland areas. We visited these schools to 
obtain supplementary information on student achievement, the condition 
of the buildings and facilities, educational materials, and parental 
involvement. We analyzed similarities and differences separately for 
each geographic area and for all seven sites collectively. Our results 
are not generalizable beyond the schools in these seven sites. We 
conducted our work from January to November 2002 in accordance with 
generally accepted government auditing standards. (A detailed 
explanation of our methodology is found in app. I.) 

Results in Brief: 

Among the schools we reviewed, differences in per-pupil spending 
between inner city and suburban schools varied by Metropolitan area, 
with inner city schools spending more in some areas and suburban 
schools spending more in others. In Boston, Chicago, and St. Louis, the 
selected inner city schools generally outspent suburban schools on a 
per-pupil basis. In Fort Worth and New York, the suburban schools in 
our study generally spent more per pupil than the selected inner city 
schools. In Denver and Oakland, spending differences between inner city 
and suburban schools were mixed. In general, higher per-pupil 
expenditures at any given school were explained primarily by higher 
staff salaries regardless of whether the school was an inner city or 
suburban schools. Two other important factors included lower student- 
teacher ratios and lower ratios of students to student support staff, 
such as guidance counselors, nurses, and librarians. While the selected 
inner city schools in Boston, Chicago, and St. Louis generally spent 
more per pupil than neighboring suburban schools, when we made 
adjustments using the highest weights the suburban schools generally 
spent more in every Metropolitan area reviewed, because inner city 
schools had higher percentages of low-income students. Some research 
has shown that children from low-income families may require extra 
resources to perform at the same levels as their nonpoor peers. To 
address the additional needs of some children in low-income areas, 
federal education programs target funds to schools in these areas. In 
some cases, the infusion of federal funds has balanced differences in 
per-pupil expenditures between selected inner city and suburban 
schools. 

Inner city students in the schools we reviewed generally performed 
poorly in comparison to students in suburban schools, a disparity that 
may be related to several differences we identified in the 
characteristics of inner city and suburban schools. Although research 
results are inconclusive on the importance of various factors, some 
studies have shown that greater teacher experience, smaller class size, 
more library and computer resources, and higher levels of parental 
involvement are positively related to student achievement. The inner 
city schools we visited generally had higher percentages of first-year 
teachers, higher enrollments, fewer library and computer resources, and 
less in-school parental involvement. For example, first-year teachers 
comprised more than 10 percent of the teaching staff in 8 of the 12 
inner city schools visited, but the same was true in just 4 of 12 
suburban schools. In New York City, the selected inner city schools had 
fewer than 1,000 library books per 100 students, whereas the selected 
suburban schools had more than 2,000 library books per 100 students. 

Background: 

The Congress, among others, has been concerned about the academic 
achievement gap between economically disadvantaged students and their 
more advantaged peers. The disparity between poor students’ performance 
on standardized tests and the performance of their nonpoor peers is 
well documented, and there is broad consensus that poverty itself 
adversely affects academic achievement. For example, on the National 
Assessment of Educational Progress (NAEP) reading assessment, 14 
percent of fourth grade students who qualified for the free and reduced 
lunch program (a measure of poverty)[Footnote 4] performed at or above 
the proficient level in comparison to 41 percent of those students who 
did not qualify for the program.[Footnote 5] Furthermore, research has 
indicated the importance of socioeconomic status as a predictor of 
student achievement.[Footnote 6] Research has shown that the 
achievement gap falls along urban and nonurban lines as well: students 
living in high-poverty, urban areas are even more likely than other 
poor students to fall below basic performance levels.[Footnote 7] 

In addition to the achievement gap between poor and nonpoor students, 
concerns exist that this gap may be related to differences between per- 
pupil spending among schools that serve poor and nonpoor communities. 
School district spending is generally related to wealth and tax levels, 
and differences in school district spending can have an impact on 
spending at the school level.[Footnote 8] Recently, efforts have been 
made to achieve greater spending equity. Using a variety of approaches, 
a number of states have targeted some additional funding to poor 
students to amend the unequal abilities of local districts to raise 
revenues for public schools.[Footnote 9] Comparing spending between 
schools in simple dollar terms provides one way to check for 
differences; however, this type of straightforward comparison may be 
insufficient to explain spending differences because it does not 
capture the higher cost of educating students with special needs. 
Schools with similar spending per pupil may actually be at a 
comparative disadvantage when adjustments are made to account for 
differing compositions of student needs. Though not definitive, some 
research shows that children with special needs--low-income students, 
students with disabilities, and students with limited English 
proficiency--may require additional educational resources to succeed at 
the level of their nondisadvantaged peers. Because these additional 
resources require higher spending, some researchers have adjusted per- 
pupil expenditures by “weighting” these students to account for the 
additional spending they may be required.[Footnote 10] 

Weighting counts each student with special needs as more than one 
student, so that the denominator in the expenditures to students ratio 
is increased, causing the weighted per-pupil expenditure figure to 
decrease accordingly. For example, a school with an enrollment of 100 
students may have 20 low-income students, 20 students with 
disabilities, and 10 students with limited English proficiency. 
Weighting these three groups of special needs students twice as heavily 
as other students causes weighted enrollment to rise to 150 students. 
If spending per-pupil is $4,000 without weighting, it drops to $2,667 
when weights are applied. The actual size of the weights assigned to 
low-income students, students with disabilities, and students with 
limited English proficiency is subject to debate and generally ranges 
between 1.2 and 2.0 for low-income students, between 1.9 and 2.3 for 
students with disabilities, and between 1.10 and 1.9 for students with 
limited English proficiency.[Footnote 11] 

The inner city schools selected for our study had high proportions of 
children in poverty in comparison to the selected suburban schools. The 
elected inner city schools also generally had more students with 
limited English proficiency than their suburban counterparts. However, 
the proportions of students with disabilities in our selected inner 
city and suburban schools differed within and among Metropolitan Areas. 
In Denver, the selected inner city schools consistently had a higher 
proportion of students with disabilities than the selected suburban 
schools while in Fort Worth, the suburban schools had a higher 
proportion of students with disabilities. (See table 1 for total 
enrollment and percentages of children in poverty, students with 
disabilities, and students with limited English proficiency for 
selected schools in the seven Metropolitan Areas reviewed in this 
study.) 

Table 1: Total Enrollment and Percentages of Children in Poverty, 
Students with Disabilities, and Students with Limited English 
Proficiency for Selected Schools in the Seven Metropolitan Areas 
Reviewed: 

Metropolitan Area: Boston; 
Inner city/suburb: Inner city 1; 
Enrollment: 712; 
Percent poor: 51; 
Percent disabled: 21; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 193; 
Percent poor: 50; 
Percent disabled: 9; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 250; 
Percent poor: 49; 
Percent disabled: 17; 
Percent LEP: 12. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 386; 
Percent poor: 7; 
Percent disabled: 12; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 979; 
Percent poor: 7; 
Percent disabled: 15; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 335; 
Percent poor: 7; 
Percent disabled: 8; 
Percent LEP: 3. 

Metropolitan Area: Chicago; 
Inner city/suburb: Inner city 1; 
Enrollment: 466; 
Percent poor: 59; 
Percent disabled: 9; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 900; 
Percent poor: 59; 
Percent disabled: 14; 
Percent LEP: 5. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 692; 
Percent poor: 59; 
Percent disabled: 12; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 503; 
Percent poor: 5; 
Percent disabled: 17; 
Percent LEP: 1. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 401; 
Percent poor: 5; 
Percent disabled: 8; 
Percent LEP: 2. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 280; 
Percent poor: 5; 
Percent disabled: 6; 
Percent LEP: 5. 

Metropolitan Area: Denver; 
Inner city/suburb: Inner city 1; 
Enrollment: 562; 
Percent poor: 52; 
Percent disabled: 12; 
Percent LEP: 52. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 372; 
Percent poor: 52; 
Percent disabled: 13; 
Percent LEP: 19. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 468; 
Percent poor: 51; 
Percent disabled: 12; 
Percent LEP: 32. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 407; 
Percent poor: 9; 
Percent disabled: 13; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 292; 
Percent poor: 10; 
Percent disabled: 8; 
Percent LEP: 11. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 623; 
Percent poor: 11; 
Percent disabled: 6; 
Percent LEP: 10. 

Metropolitan Area: Fort Worth; 
Inner city/suburb: Inner city 1; 
Enrollment: 760; 
Percent poor: 52; 
Percent disabled: 6; 
Percent LEP: 17. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 555; 
Percent poor: 51; 
Percent disabled: 3; 
Percent LEP: 10. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 937; 
Percent poor: 51; 
Percent disabled: 3; 
Percent LEP: 15. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 413; 
Percent poor: 12; 
Percent disabled: 18; 
Percent LEP: 2. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 392; 
Percent poor: 12; 
Percent disabled: 6; 
Percent LEP: 5. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 373; 
Percent poor: 14; 
Percent disabled: 17; 
Percent LEP: 13. 

Metropolitan Area: New York; 
Inner city/suburb: Inner city 1; 
Enrollment: 484; 
Percent poor: 56; 
Percent disabled: 9; 
Percent LEP: 22. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 645; 
Percent poor: 52; 
Percent disabled: 11; 
Percent LEP: 18. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 630; 
Percent poor: 43; 
Percent disabled: 6; 
Percent LEP: 3. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 457; 
Percent poor: 5; 
Percent disabled: 16; 
Percent LEP: 9. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 553; 
Percent poor: 5; 
Percent disabled: 9; 
Percent LEP: 3. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 536; 
Percent poor: 5; 
Percent disabled: 9; 
Percent LEP: 0. 

Metropolitan Area: Oakland; 
Inner city/suburb: Inner city 1; 
Enrollment: 745; 
Percent poor: 45; 
Percent disabled: 5; 
Percent LEP: 64. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 312; 
Percent poor: 50; 
Percent disabled: 9; 
Percent LEP: 73. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 1,238; 
Percent poor: 47; 
Percent disabled: 6; 
Percent LEP: 41. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 402; 
Percent poor: 8; 
Percent disabled: 8; 
Percent LEP: 15. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 877; 
Percent poor: 8; 
Percent disabled: 0; 
Percent LEP: 4. 

Metropolitan Area: Inner city/suburb: Suburban 3; 
Enrollment: 460; 
Percent poor: 8; 
Percent disabled: 8; 
Percent LEP: 3. 

Inner city/suburb: Inner city 1; 
Enrollment: 163; 
Percent poor: 85; 
Percent disabled: 12; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Inner city 2; 
Enrollment: 292; 
Percent poor: 55; 
Percent disabled: 13; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Inner city 3; 
Enrollment: 499; 
Percent poor: 55; 
Percent disabled: 8; 
Percent LEP: 0. 

Metropolitan Area: Inner city/suburb: Suburban 1; 
Enrollment: 602; 
Percent poor: 11; 
Percent disabled: 18; 
Percent LEP: 3. 

Metropolitan Area: Inner city/suburb: Suburban 2; 
Enrollment: 391; 
Percent poor: 11; 
Percent disabled: 5; 
Percent LEP: 0. 

Inner city/suburb: Metropolitan Areasuburban 3; 
Enrollment: Metropolitan area459; 
Percent poor: Metropolitan area9; 
Percent disabled: Metropolitan area11; 
Percent LEP: Metropolitan area1. 

Source: GAO’s data analysis. 

[End of table] 

Differences in school spending can affect characteristics that may be 
related to student achievement. There is a large body of research on 
factors that may directly or indirectly contribute to student 
achievement. Spending has been the factor most studied for its effect 
on student achievement. Differences in student outcomes have also been 
related to factors such as teacher quality, class size, quality of 
educational materials, and parental involvement. Our study describes 
how some of these factors may differ across selected inner city and 
suburban schools. 

Spending Differences between Selected Inner City and Suburban Schools 
Varied by Metropolitan Area: 

Differences in per-pupil spending between selected inner city and 
suburban schools varied by Metropolitan Areas in our study.[Footnote 
12] Inner city schools in Boston, Chicago, and St. Louis generally 
spent more per pupil than neighboring suburban schools, whereas 
selected suburban schools in Fort Worth and New York almost always 
spent more per pupil than the inner city schools. In Denver and 
Oakland, no clear pattern of spending emerged. Three factors generally 
explained spending differences between inner city and suburban schools: 
(1) average teacher salaries; (2) student-teacher ratios; and (3) 
ratios of students to student support staff, such as guidance 
counselors, librarians, and nurses. When we adjusted per-pupil 
expenditures to account for the extra resources students facing 
poverty, disabilities, and limited English proficiency might need, 
inner city schools almost always spent less per pupil than suburban 
schools. To compensate for additional challenges faced by schools in 
these areas, federal education dollars are generally targeted to low- 
income areas. As a result, federal funds have played an important role 
in increasing funding to inner city schools. 

Differences in Per-Pupil Spending between Selected Inner City Schools 
and Suburban Schools Varied by Metropolitan Area: 

Differences between inner city and suburban school per-pupil spending 
were related to the particular Metropolitan area studied and generally 
seemed to be most influenced by teacher salaries. The selected inner 
city schools tended to outspend the suburban schools in the Boston, 
Chicago, and St. Louis Metropolitan Areas.[Footnote 13] For example, in 
the Boston Metropolitan area, the lowest spending inner city school 
spent more per pupil than the highest spending suburban school. (See 
fig. 1 for a comparison of per-pupil spending at selected inner city 
and suburban schools in these areas.) 

Figure 1: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in Metropolitan Areas Where Inner City Schools Spent More than Suburban 
Schools: 

[See PDF for image] 

[End of figure] 

In contrast, in the Fort Worth and New York Metropolitan Areas, 
suburban schools generally outspent inner city schools. For example, 
among the selected schools in the Fort Worth Metropolitan area, the 
lowest spending suburban school had per-pupil expenditures 21 percent 
higher than the highest spending inner city school. (See fig. 2 for a 
comparison of per-pupil spending at selected inner city and suburban 
schools in these areas.) 

Figure 2: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in Metropolitan Areas Where Suburban Schools Spent More than Inner City 
Schools: 

[See PDF for image] 

[End of figure] 

In Denver and Oakland, an examination of spending differences among the 
selected suburban and inner city schools revealed mixed results. That 
is, analysis of spending differences showed no general pattern of 
spending that favored either inner city or suburban schools. (See fig. 
3 for a comparison of per-pupil spending at selected inner city and 
suburban schools in the Denver and Oakland Metropolitan Areas.) 

Figure 3: Paired Comparison (High to High, Middle to Middle, and Low to 
Low) of Per-Pupil Spending at Selected Inner City and Suburban Schools 
in the Denver and Oakland Metropolitan Areas: 

[See PDF for image] 

[End of figure] 

Average Teacher Salaries, Student-Teacher Ratios, and Ratios of 
Students to Student Support Staff Accounted for Most of the Differences 
in School Spending in Selected Schools: 

Among the schools in our study, three factors influenced per-pupil 
spending average teacher salaries, student-teacher ratios, and the 
ratio of students to student support staff.[Footnote 14] Average 
teacher salaries appeared to have the greatest impact on per-pupil 
spending, followed by lower student-teacher ratios and lower ratios of 
students to student support staff. 

Average teacher salaries influenced per-pupil spending in areas where 
inner city schools spent more per pupil (Boston and Chicago), where 
suburban schools spent more per pupil (New York), and where spending 
was mixed (Oakland). For example, in Chicago, where inner city schools 
generally outspent suburban schools, the median inner city school 
average teacher salary was $47,851, compared with $39,852 in the 
suburbs. In Oakland, where spending between suburban schools and inner 
city schools was mixed, the average teacher salary at the median 
spending school was $60,395 and per-pupil spending was $4,849, compared 
with $52,440 and $4,022 at the median spending inner city school. 

Student-teacher ratios and ratios of students to student support staff 
were factors that could offset the influence of teacher salaries in 
explaining per-pupil spending.[Footnote 15] For example, in Fort Worth, 
where the three suburban schools typically spent more per student than 
inner city schools, inner city teacher salaries were generally higher 
than suburban teacher salaries. However, ratios of students to both 
teachers and student support staff were lower in our selected suburban 
schools. For example, the median spending inner city school in Fort 
Worth had 21 students per teacher, compared with 17 students per 
teacher in the suburbs. Additionally, the median spending inner city 
school had 1 student support staff professional for every 162 students, 
whereas in the suburbs the ratio was 1 to 68. (Table 2 lists factors 
contributing to higher per-pupil spending--average teacher salaries, 
student-teacher ratios, and ratios of students to support staff--for 
the median spending school in each reviewed Metropolitan area.) 

Table 2: Spending Per Pupil, Average Teacher Salary, Student-Teacher 
Ratio, and Student-Support Staff Ratio at the Median Spending School in 
Each Metropolitan Area: 

Boston: 

Inner city; Spending per pupil: $5,770; 
Average teacher salary: $61,079; 
Student-teacher ratio: 16:1; 
Students-student support staff ratio: 119:1. 

Suburb; Spending per pupil: $4,433; 
Average teacher salary: $38,180; 
Student-teacher ratio: 17:1; 
Students-student support staff ratio: 61:1. 

Chicago: 

Inner city; Spending per pupil: $4,482; 
Average teacher salary: $46,661; 
Student-teacher ratio: 23:1; 
Students-student support staff ratio: 58:1. 

Suburb; Spending per pupil: $3,216; 
Average teacher salary: $39,852; 
Student-teacher ratio: 21:1; 
Students-student support staff ratio: 100:1. 

Denver: 

Inner city; Spending per pupil: $3,852; 
Average teacher salary: $38,044; 
Student-teacher ratio: 20:1; 
Students-student support staff ratio: 171:1. 

Suburb; Spending per pupil: $3,313; 
Average teacher salary: $32,753; 
Student-teacher ratio: 17:1; 
Students-student support staff ratio: 86:1. 

Fort Worth: 

Inner city; Spending per pupil: $3,058; 
Average teacher salary: $41,402; 
Student-teacher ratio: 21:1; 
Students-student support staff ratio: 162:1. 

Suburb; Spending per pupil: $4,246; 
Average teacher salary: $33,316; 
Student-teacher ratio: 17:1; 
Students-student support staff ratio: 68:1. 

New York: 

Inner city; Spending per pupil: $6,057; 
Average teacher salary: $42,285; 
Student-teacher ratio: a; 
Students-student support staff ratio: a. 

Suburb; Spending per pupil: $7,218; 
Average teacher salary: $72,591; 
Student-teacher ratio: 18:1; 
Students-student support staff ratio: 73:1. 

Oakland: 

Inner city; Spending per pupil: $4,022; 
Average teacher salary: $52,440; 
Student-teacher ratio: 30:1; 
Students-student support staff ratio: 233:1. 

Suburb; Spending per pupil: $4,849; 
Average teacher salary: $60,395; 
Student-teacher ratio: 20:1; 
Students-student support staff ratio: 155:1. 

St. Louis: 

Inner city; Spending per pupil: $5,337; 
Average teacher salary: $33,223; 
Student-teacher ratio: 25:1; 
Students-student support staff ratio: 28:1. 

Suburb; Spending per pupil: $3,467; 
Average teacher salary: $34,304; 
Student-teacher ratio: 13:1; 
Students-student support staff ratio: 87:1. 

Note: School districts in New York City did not provide us with 
information on student-teacher ratios and the ratio of students to 
student support staff. 

[A] Not applicable. 

Source: GAO’s data analysis. 

[End of table] 

Inner City Schools at a Disadvantage When Spending Adjusted for Student 
Needs: 

Despite higher per-pupil spending by about half of the inner city 
schools in our study, inner city schools generally spent less compared 
with neighboring suburban schools when spending was weighted to account 
for differing compositions of student needs. To account for the greater 
costs that may be associated with educating low-income students, 
students with disabilities, and students with limited English 
proficiency, some researchers have used formulas that weight these 
students more heavily than other students. In a similar fashion, we 
applied weights to our per-pupil expenditure data. 

The use of the lowest and medium weights had little impact on spending 
differences between inner city and suburban schools.[Footnote 16] Inner 
city schools in Boston, Chicago, and St. Louis continued to outspend 
neighboring suburban schools in most cases. For example, in Chicago, 
when students were weighted with the lowest weight, the median per- 
pupil spending for inner city school was $3,743 per pupil compared with 
$2,996 for the suburban school. Similarly, the use of medium weights 
generally did not result in higher per-pupil spending at suburban 
schools. For example, using medium weights, the median inner city 
school in Chicago still spent more than the median suburban school, 
although the difference was smaller--$3,089 compared with $2,858. 

However, when the highest weight was applied, inner city per-pupil 
spending fell below suburban school spending in almost all 
cases.[Footnote 17] For example, in Chicago when the highest weight was 
applied, per-pupil spending at the median inner city school was less 
than that of the suburban school, $2,629 as compared with $2,734. 
Similarly, in the New York Metropolitan area, where suburban schools we 
reviewed outspent inner city schools, the use of the highest weights to 
adjust for student needs caused the differences between inner city and 
suburban school spending to be substantially enlarged. (See fig. 4 for 
examples of how spending changes as different weights are applied for 
per-pupil spending at the median inner city and suburban schools in 
four Metropolitan Areas.) 

Figure 4: Spending Per Pupil by the Median Inner City and Suburban 
School in Four Metropolitan Areas for Different Weight Adjustments for 
Students’ Needs: 

[See PDF for image] 

[End of figure] 

Federal Funds Played Important Role in Helping Inner City Schools Meet 
Expenses: 

Because federal programs, such as Title I, specifically target funds to 
schools in low-income areas, these federal funds generally helped 
reduce or eliminate the gap between selected inner city and suburban 
schools in terms of per-pupil expenditures.[Footnote 18] In the Denver 
and St. Louis Metropolitan Areas, federal funds generally eliminated 
the gap between inner city and suburban schools’ per-pupil spending. In 
Fort Worth, without federal funds per-pupil spending at the selected 
inner city schools would have been about 63 percent of selected 
suburban schools, and in Oakland, per-pupil spending would have been 
about 78 percent of suburban schools. However, selected inner city 
schools in Boston and Chicago would have still spent more than suburban 
schools without federal funds. (See table 3 for a comparison of inner 
city and suburban per child spending with and without federal dollars.) 

Table 3: Per-Pupil Spending with and without Federal Dollars for 
Selected Inner City and Suburban Schools in Seven Metropolitan Areas: 

[See PDF for image] 

[End of table] 

Inner City Schools Generally Faced Greater Challenges That May Have 
Affected Student Achievement: 

Factors that may relate to student achievement differed between inner 
city and suburban schools in our study. Research has shown a positive 
relationship between student achievement and factors such as teacher 
experience, lower enrollment, more library books and computer 
resources, and higher levels of parental involvement. Among the 24 
schools we visited, the average student achievement scores were 
generally lower in inner city than in suburban schools. Along with 
lower achievement scores, these inner city schools were more likely to 
have a higher percentage of first-year teachers, whose lack of 
experience can be an indicator of lower teacher quality. In addition, 
in comparison to the suburban schools, inner city schools generally 
were older, had higher student enrollments, and had fewer library books 
per pupil and less technological support. Finally, the type of in- 
school parental involvement in the inner city and suburban schools 
differed. 

Inner City Students’ Achievement Scores Were Generally Lower than 
Suburban Students’ Achievement Scores: 

In general, at the schools we visited in the Metropolitan Areas of Fort 
Worth, New York, Oakland, and St. Louis, inner city students’ average 
achievement scores on state reading assessment tests were lower than 
scores at the neighboring suburban schools. Two schools were exceptions 
to this pattern. In St. Louis, we specially selected one high- 
performing inner city school; students at this school performed higher 
than students at the three suburban schools we visited. In the Fort 
Worth Metropolitan area, one inner city school performed slightly 
higher than two of the three suburban schools we visited. (See fig. 5 
for average student achievement scores for selected schools in the four 
Metropolitan Areas.) 

Figure 5: Average Student Achievement Scores for Selected Schools in 
Fort Worth, New York, Oakland, and St. Louis: 

[See PDF for image] 

[End of figure] 

Although the selected inner city schools’ student achievement scores 
were generally lower, this pattern did not appear to be related to or 
consistent with per-pupil spending. That is, higher-performing schools 
were not necessarily schools that were high in per-pupil spending. For 
example, per-pupil spending at the highest-performing inner city school 
in Fort Worth we visited was $3,058, which was higher than one selected 
inner city school, lower than the other selected inner city school, and 
lower than each of the suburban schools. 

Inner City Schools We Reviewed Had More First-Year, Thus Less 
Experienced, Teachers than Suburban Schools: 

First-year teachers in the 24 schools we visited generally constituted 
a higher percentage of the faculty in inner city schools than suburban 
schools.[Footnote 19] First-year teachers comprised more than 10 
percent of the teaching staff in 8 of the 12 inner city schools, but 
the same was true in just 4 of 12 suburban schools. However, both the 
percent of first-year teachers and differences between inner city and 
suburban schools varied among the 4 Metropolitan Areas. (See fig. 6 for 
the percentage of first-year teachers by school and Metropolitan area.) 
For example, in the New York Metropolitan area there were no first-year 
teachers at 2 of the suburban schools, but at 2 inner city schools 
first-year teachers were 24 and 13 percent of the faculty.[Footnote 20] 
In the Fort Worth Metropolitan area, 2 of the suburban schools had 
almost twice the percent of first- year teachers as the two inner city 
schools with the highest percent of first-year teachers. 

Figure 6: Percentage of First-Year Teachers by School and Metropolitan 
Area: 
Area: 

Note: One New York school did not provide data on first-year teachers. 

[See PDF for image] 

[End of figure] 

Notably, the percentage of first-year teachers was low at the two high- 
performing inner city schools. In Oakland, the percentage of first-year 
teachers at the high-performing inner city school was 6 percent, 
compared with 12 percent at the other two inner city schools. In St. 
Louis, the high-performing inner city school had no first-year 
teachers, whereas the other two inner city schools had 11 and 16 
percent. 

As noted earlier in the report, average teacher salaries in large part 
accounted for most of the differences in school spending. The fact that 
teaching staff at inner city schools were generally comprised of higher 
percentages of first-year teachers is not inconsistent with the finding 
on teacher salaries. The average teacher salary at a school includes 
the salaries of all teachers in the school, from first-year teachers to 
the most senior staff. For example, in a school with a high proportion 
of first-year teachers the average teacher salary could still be higher 
than that of another school because of higher proportions of tenured 
teachers and the district’s salary structure. 

Enrollment Was Higher in Inner City Schools than in Suburban Schools, 
and Buildings Were Older: 

The enrollment of the 12 inner city schools we visited tended to be 
higher than that of the 12 suburban schools we visited, but enrollment 
varied across and within Metropolitan Areas.[Footnote 21] The national 
average elementary school enrollment is 443, and schools with 
enrollments over 600 are considered “large,” regardless of the school’s 
capacity.[Footnote 22] In three out of the four Metropolitan Areas we 
visited, Fort Worth, New York, and Oakland, the enrollment at the inner 
city schools was consistently higher than the national average 
enrollment. In addition, 6 of the 12 inner city schools we visited had 
enrollments over 600 students. In contrast, enrollments exceeded 600 in 
only 2 of the 12 suburban schools we visited. (See fig. 7 for 
enrollments at the selected schools.) 

Figure 7: Student Enrollments at Selected Schools: 

[See PDF for image] 

[End of figure] 

Among the schools we visited, most of the inner city schools were older 
than 50 years, which is higher than the national average of 43 years. 
Furthermore, 7 of the oldest 10 buildings were inner city schools, 2 
having been built in the 19th century. In contrast, most of the 
suburban schools we visited were less than 40 years old. 

In addition to the physical condition of the buildings, playground 
facilities in the inner city schools differed greatly from facilities 
in the suburban schools. Inner city schools we visited were less likely 
to have playground equipment and expansive play areas. For example, the 
playgrounds in St. Louis suburban schools all had green fields and a 
variety of playground equipment. In this same Metropolitan area, only 
one of the inner city schools had any playground equipment and at the 
other two schools asphalt lots were the single outdoor recreational 
facility. Figure 8 shows the playgrounds of an inner city school and a 
suburban school in the St. Louis Metropolitan area. 

Figure 8: Playgrounds of an Inner City School in St. Louis and a 
Neighboring Suburban School: 

[See PDF for image] 

[End of figure] 

Inner City Schools Had Less Library and Technological Support than 
Suburban Schools: 

Overall, the inner city schools we visited had fewer library books per 
child and were less likely to have a computer laboratory than suburban 
schools. Most of the suburban schools visited were below the national 
average of 2,585 books per 100 students--7 of the 12 schools had more 
than 2,000 books per 100 students. However, only 3 of the inner city 
schools visited had more than 2,000 books per 100 students.[Footnote 
23] For example, in New York City, the 3 selected inner city schools 
had fewer than 1,000 library books per 100 students, whereas the 3 
selected suburban schools had more than 2,000 library books per 100 
students and one had more than 3,000. Notably, the high-performing 
inner city school in St. Louis had 2,813 library books per 100 
students, more than any of the suburban schools we visited in that 
area. Similarly, the high- performing inner city school in Oakland had 
2,244 books per 100 students, which was more than the other two Oakland 
inner city schools and 2 of the 3 selected suburban schools. 
Furthermore, only 7 of the 12 selected inner city schools had a full-
time librarian, whereas all but one suburban school had a full-time 
librarian. (See fig. 9 for the number of library books per 100 students 
at selected schools.) 

Figure 9: Number of Library Books per 100 Students at Selected Schools: 

[See PDF for image] 

[End of figure] 

Our site visits also revealed a difference between inner city and 
suburban schools in terms of the presence of a computer laboratory. 
Eleven of the 12 suburban schools we visited had a computer laboratory, 
whereas 8 of the inner city schools visited had such a facility. Among 
schools with computer laboratories, however, the ratio of students to 
laboratory computers was similar among inner city and suburban schools. 

In-School Parental Involvement Differed between Selected Inner City and 
Suburban Schools: 

Parents of children attending the suburban schools we visited were more 
involved in on-site school activities than parents of inner city 
children.[Footnote 24] According to the suburban school principals, 
parental involvement in their schools was typically very high and 
included participation in volunteer activities, attendance at parent- 
teacher conferences, and providing financial support to the school. 
Parent volunteerism at suburban schools could be quite substantial. For 
example, parents at one suburban school in the Oakland Metropolitan 
Area provided 24,000 hours of volunteer time during the school year. 
Inner city principals characterized parents as concerned and interested 
in their children’s education, though less likely to attend parent- 
teacher conferences and volunteer in school. A number of inner city 
principals we interviewed also noted that while parents generally 
wanted to help their children succeed in school, they often lacked the 
necessary finances, skills, or education to offer additional assistance 
beyond that offered by the school. 

Conclusions: 

Our findings suggest that spending differences between the inner city 
schools and suburban schools in our review do exist, but these 
differences for the most part depend upon the Metropolitan area. In 
some Metropolitan Areas, inner city schools spent more per pupil 
whereas in others suburban schools spent more per pupil. Spending 
differences, regardless of Metropolitan area for the most part, seemed 
to be the result of differences in salaries and student to teacher and 
staff ratios. However, the very heavy concentration of poverty in inner 
city schools may place them at a spending disadvantage, even when 
spending is equal. In addition, the suburban schools, as well as the 
high-performing inner city schools we visited, generally had more 
experienced teachers, lower enrollments, more library books per child, 
and more parental in-school volunteer activities than the other inner 
city schools in this study. These factors are important to consider in 
improving the performance of inner city schools. 

Agency Comments: 

We provided a draft of this report to the Department of Education for 
review and comment. Education’s Executive Secretariat confirmed that 
department officials had reviewed the draft and had no comments. 

We are sending a copy of this report to the Secretary of Education. We 
will make copies available to others upon request. In addition, the 
report will be available at no charge on GAO’s Web site at http:// 
www.gao.gov. 

If you or your staff have any questions about this report, please call 
me at (202) 512-7215. See appendix III for other staff acknowledgments. 

Sincerely yours, 

Marnie S. Shaul, Director
Education, Workforce, and Income Security Issues 

Signed by Marnie S. Shaul 

[End of section] 

Appendix I: Objectives, Scope, and Methodology: 

The objectives of our study were to provide information on similarities 
and differences between (1) per-pupil spending in selected inner city 
and suburban schools and (2) other characteristics that may relate to 
student achievement, such as, teacher experience, school enrollment, 
educational materials, physical facilities, and parental involvement. 
To address the first objective, we reviewed the literature on spending 
differences, interviewed experts about the issues and approaches to 
measuring spending data, and collected spending and related school data 
on 42 inner city and suburban schools. To address the second objective, 
we examined the literature, interviewed experts about relationships 
between student achievement and school characteristics, and visited 24 
inner city and suburban schools to collect information on student 
achievement, the quality and availability of educational materials, the 
condition of the buildings and facilities, and type and extent of 
parental involvement. This appendix discusses the scope of the study, 
criteria for selecting Metropolitan Areas and schools, and the methods 
employed to describe and explain observed spending differences. 

Scope: 

This study focused on similarities and differences between inner city 
schools and suburban schools. This is different and distinct from a 
study of similarities and differences between urban and suburban 
schools, or urban and suburban districts, as urban schools and 
districts generally include a wider range of poverty than inner city 
schools. This study covered selected inner city and suburban schools in 
seven Metropolitan Areas. 

Metropolitan area Selection: 

Metropolitan Areas were purposively selected to reflect diversity on 
the basis of geography and size. We used geographic areas from the 
Northeast, Midwest, South, and West. Three size categories were used: 
(1) very large, (2) large, and (3) medium. We defined these by 
population. 

* Very large: areas where the central city of a Metropolitan area had a 
population of more than 1 million residents; 

* Large: areas where the central city of a Metropolitan area had a 
population between 500,000 and 1 million residents; 

* Medium: areas where the central city of a Metropolitan area had a 
population between 250,000 and 500,000 residents. 

The Metropolitan Areas selected for inclusion in the study were Boston, 
Chicago, Denver, Fort Worth, Miami, New York, Oakland, and St. Louis. 
Inner city and suburban schools in Miami were dropped from the study 
because the district did not provide the necessary data. (See table 4 
for the selected Metropolitan Areas.) 

Table 4: Metropolitan Areas Selected for Study: 

Category: Very large; 

Metropolitan Area: Chicago; 
Geographic location: Midwest; 
City population: 2,896,016.
Metropolitan Area: New York; 
Geographic location: Northeast; 
City population: 8,008,278. 

Category: Large; 

Metropolitan Area: Boston; 
Geographic location: Northeast; 
City population: 589,141. 

Metropolitan Area: Denver; 
Geographic location: West; 
City population: 554,636. 

Metropolitan Area: Fort Worth; 
Geographic location: South; 
City population: 534,694. 

Category: Medium; 

Metropolitan Area: Miami; 
Geographic location: South; 
City population: 362,470. 

Metropolitan Area: Oakland; 
Geographic location: West; 
City population: 399,484. 

Metropolitan Area: St. Louis; 
Geographic location: Midwest; 
City population: 348,189. 

Source: GAO’s data analysis and 2000 Census. 

[End of table] 

Defining Inner City and Suburbs: 

For this study, in consultation with experts, we defined “inner city” 
as a contiguous geographic area that (1) had a poverty rate of 40 
percent or higher, (2) was located within the “central core” of a city 
with a population of at least 250,000 persons, and (3) the city is the 
central city of a Metropolitan Area: with a population of at least 1 
million persons. 

We defined suburb as the geographic area that is (1) outside the 
boundaries of a central city with a population of at least 250,000 
persons, (2) inside the boundaries of the Metropolitan Area: 
statistical area (SMSA) of the central city, as defined by the Office 
of Management and Budget and used by the census, and (3) the 
Metropolitan area has a population of at least 1 million persons. 

In total, we collected spending data on 42 schools, 21 inner city and 
21 suburban public elementary schools in seven Metropolitan Areas, and 
gathered information on (1) school-level per-pupil spending and federal 
revenues, and (2) school, teacher, other staff, and student 
characteristics for the 2000-01 school year. In addition, we conducted 
site visits at 24 of the selected schools. These schools were located 
in the New York, St. Louis, Fort Worth, and Oakland Metropolitan Areas. 
We visited them in order to obtain supplementary information on 
characteristics that might affect student achievement, such as 
facilities, educational materials, and types of parental involvement. 

School Selection: 

The study was designed to compare “typical” inner city and “typical” 
suburban schools, rather than those schools with extreme poverty or 
wealth. We consulted with experts about our design. We used the factors 
described below to select typical schools. Our goal was to make 
comparisons that would reflect likely differences, if any, between the 
inner city and suburban schools in a given Metropolitan area. 

To select the inner city schools, we (1) consulted with local experts 
in each Metropolitan area to identify the geographic area of the 
central city of the SMSA generally considered the inner city, (2) 
calculated census child poverty rates for each census tract within the 
inner city area, (3) retained identified census tracts with census 
child poverty rates higher than 40 percent, (4) ranked the census 
tracts by poverty rate, and (5) identified the three inner city census 
tracts closest to the 50th percentile, that is, the median poverty 
census tracts of the inner city.[Footnote 25] We then selected the 
public elementary school that served those census tracts, but purposely 
excluded schools that were special schools, for example, magnet 
schools, science academies, etc. 

Where possible, we attempted to include one high-performing inner city 
school in each Metropolitan area we visited. We used Dispelling the 
Myth, an Education Trust (EdTrust) database of high-poverty, high- 
performing schools, for this selection. Dispelling the Myth is an 
ongoing EdTrust project to identify high-poverty and high-minority 
schools that have high student performance or have made substantial 
improvement in student achievement. We identified schools in that 
database with a student poverty rate greater than 50 percent and an 
overall achievement score on the most recent state reading assessment 
test above the 50th percentile. Because the EdTrust database used free 
and reduced lunch eligibility as its criterion for poverty,[Footnote 
26] we further verified that the school was located in an inner city 
census tract as defined by this study serving an area with a census 
child poverty rate greater than 40 percent. We purposely excluded 
schools that were special schools, for example, magnet schools, science 
academies, etc. Inner city schools from the St. Louis and Oakland 
Metropolitan Areas met these criteria. The identified high-performing 
inner city school in St. Louis replaced a selected school. The 
identified high-performing inner city school in Oakland, however, was a 
school that would have been selected through the described census tract 
approach and was, therefore, treated similarly to the other selected 
inner city schools. (See table 5 for the selected inner city census 
tracts and child poverty rates.) 

Table 5: Selected Inner City Census Tracts and Child Poverty Rates: 

Metropolitan Area: Boston; 

Census tract: 0611; 
Child poverty rate: 48.5%. 

Census tract: 0814; 
Child poverty rate: 49.8%. 

Census tract: 0924; 
Child poverty rate: 50.9%. 

Metropolitan Area: Chicago; 

Census tract: 6106; 
Child poverty rate: 58.6%. 

Census tract: 6812; 
Child poverty rate: 58.9%. 

Census tract: 4001; 
Child poverty rate: 59.0%. 

Metropolitan Area: Denver; 

Census tract: 0011.02; 
Child poverty rate: 51.3%. 

Census tract: 0007.02; 
Child poverty rate: 52.0%. 

Census tract: 0010; 
Child poverty rate: 52.2%. 

Metropolitan Area: Fort Worth; 

Census tract: 1046.04; 
Child poverty rate: 51.0%. 

Census tract: 1050.06; 
Child poverty rate: 51.2%. 

Census tract: 1061.02; 
Child poverty rate: 52.1%. 

Metropolitan Area: New York[A]; 

Census tract: 209.01; 
Child poverty rate: 42.9%. 

Census tract: 0395; 
Child poverty rate: 52.4%. 

Census tract: 65; 
Child poverty rate: 56.5%. 

Metropolitan Area: Oakland; 

Census tract: 4054; 
Child poverty rate: 44.9%. 

Census tract: 4088; 
Child poverty rate: 46.8%. 

Census tract: 4024[B]; 
Child poverty rate: 49.6%. 

Metropolitan Area: St. Louis; 

Census tract: 1212[C]; 
Child poverty rate: 85.0%. 

Census tract: 1104; 
Child poverty rate: 54.8%. 

Census tract: 1243; 
Child poverty rate: 54.9%. 

Note: Child poverty rates were computed using 1990 census data. 

[A] Census tracts are from three separate counties: CT 209.01 (New 
York); CT 0395 (Kings); CT 65 (Bronx). 

[B] Census tract contained identified high-performing inner city 
school. 

[C] Census tract 1112 (54.6% child poverty) was replaced by identified 
high-performing inner city school in census tract 1212. 

Source: GAO’s data analysis. 

[End of table] 

To select suburban schools, we (1) collected census child poverty rates 
for all school districts in the defined suburban area outside the 
central city of the selected Metropolitan area and within the same 
state as the central city; (2) ranked by census child poverty rates in 
the suburban school districts; and (3) identified the three suburban 
school districts closest to the 50th percentile, that is, the median 
suburban school districts, based upon child poverty rates. We dropped 
districts that were contiguous or had a 5 to 17-year-old population of 
less than 500 and replaced them with the district with the next closest 
median level child poverty that did not have any of these attributes. 

For those districts, we selected the elementary school of the district. 
If more than one elementary school served the school district, we 
selected the elementary school in the district with the median child 
poverty rate (as determined by free and reduced lunch eligibility) for 
elementary schools in that district. (See table 6 for the child poverty 
rates for the selected suburban school districts.) 

Table 6: Selected Suburban School Districts’ Child Poverty Rates: 

Metropolitan Area: Boston; Metropolitan Area: District 1: 6.5%; 
Selected school district poverty rate: District 2: 6.6%; 
Selected school district poverty rate: District 3: 6.9%. 

Metropolitan Area: Chicago; Metropolitan Area: District 1: 4.5%; 
Selected school district poverty rate: District 2: 4.6%; 
Selected school district poverty rate: District 3: 4.6%. 

Metropolitan Area: Denver; Metropolitan Area: District 1: 8.8%; 
Selected school district poverty rate: District 2: 10.1%; 
Selected school district poverty rate: District 3: 11.0%. 

Metropolitan Area: Fort Worth; Metropolitan Area: District 1: 12.0%; 
Selected school district poverty rate: District 2: 12.3%; 
Selected school district poverty rate: District 3: 13.9%. 

Metropolitan Area: New York; Metropolitan Area: District 1: 4.9%; 
Selected school district poverty rate: District 2: 5.1%; 
Selected school district poverty rate: District 3: 5.2%. 

Metropolitan Area: Oakland; Metropolitan Area: District 1: 7.5%; 
Selected school district poverty rate: District 2: 7.9%; 
Selected school district poverty rate: District 3: 8.4%. 

Metropolitan Area: St. Louis; Metropolitan Area: District 1: 9.3%; 
Selected school district poverty rate: District 2: 10.5%; 
Selected school district poverty rate: District 3: 10.8%. 

Note: Child poverty rates were computed using 1995 census child poverty 
estimates for school districts. 

Source: GAO’s data analysis. 

[End of table] 

Data Collected: 

From 42 selected schools we obtained detailed information for the 2000-
01 school year on (1) school spending and federal revenues, (2) 
staffing and teacher experience, and (3) student characteristics. The 
practical difficulties of conducting any data collection effort may 
introduce errors, commonly referred to as nonsampling errors. For 
example, difficulties in how a particular question is interpreted or in 
the sources of information that are available can introduce unwanted 
variability into the results. We took steps in the development of the 
instrumentation, the data collection, and the data editing and analysis 
to minimize these errors. We pretested our data collection instrument 
with the Boston school district and called individual district 
officials to clarify answers. Completed instruments were examined for 
inconsistencies, and follow-up calls were made to districts to clarify 
imprecise responses or data that were unusually different from other 
respondent data. 

* School spending data included (1) instructional staff salaries, (2) 
certified professional staff salaries, (3) administrative staff 
salaries, (4) operations staff salaries, (5) education materials and 
supplies spending, and (6) building maintenance and repair spending. In 
addition, schools reported federal sources of revenue. 

* School, staff, and student information included numbers of (1) 
regular education teachers, special education, English as a second 
language instructional staff, and other specialized instructional 
staff, for example, art teachers, reading teachers; (2) regular 
education teacher assistants, special education teacher assistants, and 
other instructional staff teacher assistants, for example, art teacher 
assistants, reading teacher assistants; (3) student support 
professional and nonprofessional staff by job title; (4) administrators 
and administrative assistants by job title; (5) operations staff by job 
title; (6) the number of first-year teachers; (7) total enrollment; (8) 
number of students with disabilities and number of students with 
limited English proficiency; (9) race and ethnicity of students; and 
(10) the number of students eligible for free and reduced lunch. 

Data on student achievement, facilities, educational materials, and 
parental involvement that may contribute to academic achievement were 
obtained from site visits to 12 inner city and 12 suburban schools. We 
developed a site visit protocol and pretested it at site visits to 
inner city and suburban schools in the New York and Baltimore 
Metropolitan Areas. 

We obtained information on student achievement. In Fort Worth, we used 
Grade 3 reading scores on the Texas Assessment of Academic Skills. In 
New York, we used Grade 4 scores on the State English Language Arts 
Assessment. In Oakland, we used Grade 4 reading scores on the Stanford 
9 test. In St. Louis, we used Grade 3 Communication Arts scores on the 
Missouri Assessment Program. In each Metropolitan area, we contrasted 
the achievement scores of the selected schools to the state average. 

Depending upon data, information was collected as a dichotomous 
variable (yes/no), date or period of time, number, or ranked scale 
assessment. (See table 7 for school site visit information collected, 
assessment measure, and description of the measurement scale.) 

Table 7: School Characteristics, Assessment Measure, and Measurement 
Description: 

Category: Facilities. 

Category: Age of building; 
Assessment: Date; 
Measurement description: Year of construction. 

Category: Renovations; 
Assessment: Date; 
Measurement description: Year of most recent renovation. 

Category: Ancillary buildings; 
Assessment: Yes/no; 
Measurement description: Presence of auxiliary classrooms. 

Category: Classroom size; 
Assessment: Square feet; 
Measurement description: Size of 2nd Grade classrooms. 

Category: Special classrooms; 
Assessment: Yes/no; 
Measurement description: Presence of special classrooms and 
description[A]. 

Category: Playgrounds; 
Assessment: Yes/no; 
Measurement description: GAO assessment. 

Category: Condition of facilities; 
Assessment: Scale; 
Measurement description: GAO assessment (1-4 scale) and description. 

Category: Educational materials. 

Category: Age of textbooks; 
Assessment: Date; 
Measurement description: Year of purchase. 

Category: Computers; 
Assessment: Number; 
Measurement description: Total computers in building. 

Category: Modernization; 
Assessment: Scale; 
Measurement description: GAO assessment (1-3 scale) and description. 

Category: School supplies; 
Assessment: Scale; 
Measurement description: Principal assessment (1-5 scale) and 
description. 

Category: Library; 
Assessment: Number; 
Measurement description: Number of books. 

Category: Parental involvement. 

Category: School activities; 
Assessment: Scale; 
Measurement description: Principal assessment (1-5 scale) and 
description. 

Category: Donate/raise money; 
Assessment: Yes/no; 
Measurement description: Principal assessment. 

Category: Volunteer; 
Assessment: Yes/no; 
Measurement description: Principal assessment. 

Category: PTA participation; 
Assessment: Yes/no; 
Measurement description: Principal assessment. 

Note: Scale is a subjective assessment. 

[A] Special classrooms include gymnasium, auditorium, cafeteria, art 
room, music room, science room, and gardens, and were separately noted. 

Source: GAO site visit data collection protocol. 

[End of table] 

Methodology to Analyze Differences in Spending and Factors Accounting 
for Spending Differences: 

For each Metropolitan area, per-pupil spending[Footnote 27] for each of 
the three inner city schools and three suburban schools were ordered 
and paired, that is, the lowest spending inner city school was paired 
with the lowest spending suburban school, the middle spending inner 
city school was paired with the middle spending suburban school, and 
the highest spending inner city school was paired with the highest 
spending suburban school. 

To examine factors that explained differences in school spending, we 
conducted regression analysis. Regression analysis is a statistical 
methodology that measures the relationship between one variable and one 
or more other variables. 

In our regression model, we tried to determine the extent to which 
total per-pupil spending at a selected individual school could be 
explained by (1) average teacher salary at the school, (2) adjusted 
student-teacher ratio at the school,[Footnote 28] (3) the ratio of 
students to student support staff at the school, and (4) annual 
spending at the school on building maintenance and repair. 

The variables in the model were defined as follows: 

* Total per-pupil spending--total dollars spent by the school in the 
2000-01 school year divided by total enrollment.[Footnote 29] 

* Average teacher salary--total salary expenditure for teachers at the 
school divided by the number of teachers. Teacher salary was used in 
the regression to capture the salary structure at the school.[Footnote 
30] 

* Adjusted student-teacher ratio--total enrollment adjusted for 
students with special educational needs divided by the total certified 
instructional staff. Adjusted enrollment differed from total enrollment 
in that the adjusted enrollment included an additional weight of 100 
percent for each child receiving special education instruction at the 
school and 50 percent for students with limited English proficiency. 
Adjusted enrollment was used to capture the direct higher spending by 
the school for students with special needs. Teachers included: regular 
classroom teachers, special education teachers, teachers of students 
with limited English proficiency, art teachers, music teachers, 
physical education teachers, reading teachers, teachers for the gifted 
and talented, science teachers, and computer laboratory 
teachers.[Footnote 31] Teaching assistants and paraprofessionals were 
not included because their direct involvement with instruction was not 
always certain. 

* The ratio of students to student support staff at the school was 
computed by dividing the total enrollment by the total certified 
professional staff. Support staff was not adjusted for students with 
special needs because it was assumed that at the school level support 
staff to student time is less dependent upon the disability of the 
child. Total certified professional staff included: administrators, 
health providers, and certified staff providing services to 
students.[Footnote 32] 

* Spending on building maintenance and repair at the school included 
contracted maintenance and repair and salary expenditures for building 
custodians and maintenance workers for the 2000-01 school year. (See 
table 8 for the regression results for factors explaining differences 
in per-pupil spending at the selected schools.) 

Table 8: Regression Results for Factors Explaining Differences in Per- 
Pupil Spending at Selected Schools: 

Independent variable: Constant; 
Coefficient: 3024.888; 
Standard error: 678.076; 
t-score: 4.461; 
Significance: .000. 

Independent variable: Teacher salary; 
Coefficient: 7.718E-02; 
Standard error: .011; 
t-score: 7.295; 
Significance: .000[A]. 

Independent variable: Weighted student teacher ratio; 
Coefficient: -89.375; 
Standard error: 30.934; 
t-score: -2.889; 
Significance: .007[A]. 

Independent variable: Student-support staff ratio; 
Coefficient: - 5.134; 
Standard error: 2.165; 
t-score: -2.372; 
Significance: .024[A]. 

Independent variable: Maintenance and repair; 
Coefficient: 2.067E-03; 
Standard error: .001; 
t-score: 1.988; 
Significance: .055. 

Dependent variable: Per-pupil spending: 

R = 0.854: 

F = 21.536: 

sig. = 0.000: 

[A] Significant at the 0.05 level. 

Source: GAO’s data analysis. 

[End of table] 

Appendix II presents selected data on the 42 schools examined in the 
seven Metropolitan Areas, as well as additional information obtained 
from site visits at 24 schools. 

[End of section] 

Appendix II: School Profiles: 

This appendix contains three tables of school-level information 
collected from selected inner city and suburban schools in seven 
Metropolitan Areas. Table 9 contains student characteristic 
information. Student characteristic information includes enrollment, 
child poverty measured by the census, percent of students with 
disabilities, percent of students with limited English proficiency, and 
percent of children that are minority. 

Table 10 contains actual spending per child, then spending per child at 
low, medium, and high weights for selected schools in seven 
Metropolitan Areas. Table 11 includes information on the percent of 
first-year teachers, federal dollars per child, and federal dollars as 
a percent of total spending. 

Table 9: School-Level Student Characteristics for Selected Schools in 
Seven Metropolitan Areas: 

[See PDF for image] 

[End of table] 

Table 10: Spending Per Pupil and Spending Per Pupil at Low, Medium, and 
High Weights for Selected Schools in Seven Metropolitan Areas: 

[See PDF for image] 

Source: GAO’s data analysis. 

[End of table] 

Table 11: Percent of First-Year Teachers, Federal Dollars Per Pupil, 
and Federal Dollars as a Percent of Total Spending at Selected Schools 
in Seven Metropolitan Areas: 

[See PDF for image] 

Source: GAO’s data analysis. 

[End of table] 

Appendix III: GAO Contacts and Staff Acknowledgments: 

GAO Contacts: 

Harriet Ganson, (202) 512-7042
Peter Minarik, (202) 512-7230: 

Acknowledgments: 

In addition to those named above, Elisabeth Anderson, Shannon McKay, 
Eve Veliz, and Sarit Weisburd made key contributions to this report. 
Luann Moy provided important methodological contributions to the review 
of the research. Patrick DiBattista also provided key technical 
assistance. 

FOOTNOTES 

[1] The Education Trust, The Funding Gap: Low-Income and Minority 
Students Receive Fewer Dollars, August 2002. 

[2] Inner city schools and suburban schools in Miami were part of the 
original selection process but were dropped from the study because the 
district did not provide the necessary data. 

[3] The criteria for including a school from the Education Trust 
database included the following: (1) The school was located in a 
selected inner city area. (2) The census child poverty rate for the 
school exceeded 40 percent. (3) The school placed in the top 50th 
percentile among all schools on the state’s most recent reading 
assessment test. (4) The school was not a special school, for example, 
magnet school, science academy, etc. 

[4] Eligibility for free lunches is set at 130 percent of the official 
poverty line ($22,165 for a family of four during the 2000-01 school 
year), and eligibility for reduced-price lunches extends up to 185 
percent of the poverty line ($31,543 for a family of four during the 
2000-01 school year). 

[5] U.S. Department of Education, National Center for Education 
Statistics, NAEP, The Nation’s Report Card: Fourth-Grade Reading 2000, 
April 2001. 

[6] See for example, U.S. General Accounting Office, School Finance: 
State and Federal Efforts to Target Poor Students, GAO/HEHS-98-36 
(Washington, D.C.: Jan. 28, 1998). 

[7] Educational Testing Service, unpublished tabulations from 1994 NAEP 
reading test. Cited in Education Week “Quality Counts,” 1998. http:// 
www.edweek.org/sreports/qc98/challenges/achieve/ac-c1.htm. 

[8] For recent statistics of finance equity among states, see American 
Education Finance Association, Equitable School Finance Systems: 
Grading The States, American Education Finance Association meeting, 
Austin, TX, Mar. 9-11, 2000. 

[9] Spending per pupil reported in this study reflects nominal dollars 
after such adjustments have been made by the state to account for 
student needs. 

[10] See: S. Chaikind, et al., “What Do We Know About the Costs of 
Special Education? A Selected Review,” The Journal of Special 
Education, 26, no. 4 (1993): 344-370; American Institutes for Research, 
What Are We Spending on Special Education Services in the United 
States, 1999-2000?, Advance Report No. 1.( Special Education 
Expenditure Project, Mar. 2002.); GAO/HEHS-98-36; T. Parrish, “A Cost 
Analysis of Alternative Instructional Models for Limited English 
Proficient Students in California,” Journal of Education Finance 
(Winter 1994): 256-278. 

[11] Ibid. 

[12] We gathered operational school-level spending on personnel 
salaries, building maintenance and repair, and educational materials 
and supplies. Other operational expenditures, for example 
transportation and capital expenditures, are not considered spending 
for this report. Total spending, as used herein, refers to the total 
amount spent 
on salaries, building maintenance and repair, and educational materials 
and supplies. 

[13] See appendix I for technical details. 

[14] Regression analysis was employed to identify factors influencing 
per-pupil spending. The t-scores of average teacher salary, student- 
teacher ratio, and the ratio of students-to-student support staff were 
found to be significant at the 0.05 level. Maintenance and repair 
spending was found to be positively related to per-pupil spending, but 
not at the 0.05 level. See appendix I for technical details. 

[15] Student support staff was defined as including guidance 
counselors, social workers, psychologists, librarians, nurses, speech 
therapists, principals, and assistant principals. 

[16] The actual size of the weights assigned to low-income, special 
education, and limited English proficiency students is subject to 
debate and generally ranges from a 1.2 to 2.0 for low-income students, 
from 1.9 to 2.3 for special education students, and from 1.1 to 1.9 for 
students with limited English proficiency. Consequently, low-weights 
were 1.2 for low-income students, 1.9 for special education students, 
and 1.1 for students with limited English proficiency. The medium 
weights were 1.6 for low-income students, 2.1 for special education 
students, and 1.5 for students with limited English proficiency. The 
high weights were 2.0 for low-income students, 2.3 for special 
education students, and 1.9 for students with limited English 
proficiency. 

[17] The differences between inner city and suburban weighted per-pupil 
spending was most affected by differences in the proportion of low- 
income students in inner city and suburban schools. The inner city 
schools in our study served populations with very high proportions of 
low-income students. 

[18] See U.S. General Accounting Office, Title I Funding: Poor Children 
Benefit Though Funding Per Poor Child Differs, GAO-02-242 (Washington, 
D.C.: Jan. 31, 2002). 

[19] The percentage of first-year year teachers can be used as an 
indicator of lower teacher quality because of their relative 
inexperience. 

[20] Information on first-year teachers was received for only 2 of the 
3 selected New York City schools. 

[21] WestEd reports that research indicates smaller schools can reduce 
the effects of poverty on student achievement. See, WestEd, Are Small 
Schools Better? School Size Safety & Learning, November 21, 2001, San 
Francisco, CA. 

[22] National Center for Education Statistics, Condition of American 
Public School Facilities, 1999. 

[23] The number of library books reported is not precise. The reported 
number is based upon data that include both counts provided by some 
schools and best estimates provided by librarians from other schools. 

[24] Research has indicated the importance of parental involvement to 
student achievement. The National Conference of State Legislatures 
reported on a comprehensive review of 66 studies that examined the 
correlation between parent involvement and student success and 
concluded that parent involvement, not income or social status, was the 
most accurate predictor of student success. See National Conference of 
State Legislatures, Improving Student Achievement, July 2001, citing 
Anne T. Henderson and Nancy Berla, A New Generation of Evidence: The 
Family is Critical to Student Achievement (Washington, D.C., Center for 
Law and Education, 1995). 

[25] In two Metropolitan Areas, New York and Oakland, local experts 
identified three distinct inner city areas. In these two Metropolitan 
Areas, the census tract in each inner city area closest to median level 
poverty was selected. Two of the three selected inner city schools in 
New York--schools selected from Harlem and the Bronx--had poverty rates 
above 40 percent and were located in inner city areas. These schools, 
however, were selected on the basis of per capita income, which was the 
selection methodology employed during the early design phase of the 
study and subsequently replaced by the median poverty rate approach. 
The schools were retained, however, for data efficiency purposes and 
because their child poverty rates were consistent with that of schools 
that would have been selected in their stead. 

[26] Child poverty can be measured by (1) census data or (2) the number 
of children eligible for free or reduced-price lunch. The subsidized 
lunch program provides a looser definition of poverty than census 
poverty data. The number of students eligible for subsidized lunches is 
roughly double the number meeting the census poverty definition. 
Nonetheless, according to the Department of Education, the subsidized 
lunch program provides the best available source of data on low-income 
students at the school level. 

[27] Spending includes personnel salaries, building maintenance and 
repair, and educational materials and supplies. Some expenditures, such 
as transportation and district overhead, are, therefore, not included 
in spending. 

[28] Enrollment was weighted to account for students with disabilities 
and students with limited English proficiency in order to more 
accurately gauge the school’s student-teacher ratio. 

[29] Total enrollment was calculated as the enrollment of the school on 
October 1, 2000. 

[30] It was assumed that across schools the salaries of other employees 
in the school would be “structurally” related to the salaries of 
teachers. That is, if teachers at a particular school earn on average a 
higher salary, then other employees at the school, such as operations 
staff and administrators, would similarly earn higher salaries. 

[31] Classroom and instructional-service paraprofessionals were not 
included. 

[32] Operation staff and clerical staff were not included. 

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