Exploring the Quality of School-Level Expenditure Data:

Practices and Lessons Learned in Nine Sites

Exploring the Quality of School-Level Expenditure Data: 

Practices and Lessons Learned in Nine Sites

January 2017

Prepared for:

U.S. Department of Education 

Office of Planning, Evaluation and Policy Development

Policy and Program Studies Service

Prepared by: 

Drew Atchison

Bruce Baker

Andrea Boyle 

Jesse Levin 

Karen Manship 

American Institutes for Research 
This report was produced under U.S. Department of Education Contract No. EDPEP-16-O-0089/Task 

Order 16 with American Institutes for Research. Joanne Bogart and Stephanie Stullich served as the 

contracting officer’s representatives. The views expressed herein do not necessarily represent the 

positions or policies of the U.S. Department of Education. No official endorsement by the 

U.S. Department of Education of any product, commodity, service or enterprise mentioned in this 

publication is intended or should be inferred.

U.S. Department of Education

John B. King, Jr.

Secretary

Office of Planning, Evaluation and Policy Development

Amy McIntosh

Delegated Duties of Assistant Secretary

Policy and Program Studies Service

Jennifer Bell-Ellwanger 

Director

January 2017

This report is in the public domain. Authorization to reproduce it in whole or in part is granted. While 

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Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites, 

Washington, D.C., 2017. 

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Content Contact:

Stephanie Stullich

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stephanie.stullich@ed.gov
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites i 

Contents 

Contents............................................................................................................................................... i

List of Exhibits...................................................................................................................................... ii

Acknowledgments ............................................................................................................................... v

Executive Summary ........................................................................................................................... vii

Study Design and Limitations........................................................................................................ viii

Key Findings.................................................................................................................................... ix

Conclusions....................................................................................................................................xiv

I. Introduction...................................................................................................................................... 1

Challenges Involving School-Level Fiscal Data Collection................................................................ 3

Characteristics of the Case Study Sample........................................................................................ 4

Data and Methods ........................................................................................................................... 5

Study Limitations ............................................................................................................................. 8

II. Expenditure Attribution Practices .................................................................................................. 11

Types of Expenditures Attributed to Schools ................................................................................ 11

Share of Operational Expenditures Attributed to Schools ............................................................ 15

Methods for Attributing Expenditures to Schools......................................................................... 18

Summary ........................................................................................................................................ 23

III. Reliability of School-Level Expenditure Data ................................................................................. 25

Consistency of Site-Provided Data With Other Sources................................................................ 25

Accuracy of Attributing Expenditures to Schools Through Allocation Formulas...........................33

Summary ........................................................................................................................................ 40

IV. Developing and Implementing Systems to Collect School-Level Expenditure Data......................... 41

Development and Expansion of School Expenditure Data Systems..............................................41

Specific Tasks and Costs Involved .................................................................................................. 44

Challenges ...................................................................................................................................... 47

Training and Quality Assurance Practices...................................................................................... 49

Summary ........................................................................................................................................ 52

V. Conclusion ..................................................................................................................................... 53

References......................................................................................................................................... 55

Appendix A. Attribution Methods by Category................................................................................... 57

Appendix B. Detailed Spending by Study Site, Attribution Method, and Expenditure Category .......... 59

Appendix C. Distribution of Differences Between Actual Reported Expenditures and Simulated 

Allocations............................................................................................................................. 65

Appendix D. Data Collection Instruments........................................................................................... 73
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites ii

List of Exhibits

Exhibit 1. Conceptual framework........................................................................................................... 2

Exhibit 2. Characteristics of sample states and districts, 2011–12........................................................ 4

Exhibit 3. Number of study sites that reported attributing various types of expenditures to 

schools, and average percentage of operational expenditures attributed to 

schools, by expenditure type, 2011–12............................................................................... 12

Exhibit 4. Share of personnel expenditures attributed to schools, by type of expenditure and 

by site, 2011–12................................................................................................................... 13

Exhibit 5. Share of non-personnel expenditures attributed to schools, by type of expenditure 

and by site, 2011–12............................................................................................................ 14

Exhibit 6. Share of operational expenditures attributed to schools, 2011–12.................................... 15

Exhibit 7. Distribution of school- and district-level spending among personnel and nonpersonnel expenditure categories, 2011–12 ....................................................................... 16

Exhibit 8. Share of personnel and non-personnel expenditures attributed to schools, 

2011–12 ............................................................................................................................... 17

Exhibit 9. Number of study sites that reported tracking or allocating various types of 

expenditures to schools, by expenditure type, 2011–12 .................................................... 19

Exhibit 10. Distribution of school-level expenditures by attribution method, 2011–12....................... 20

Exhibit 11. Share of school-level personnel and non-personnel spending tracked by sites, 

2011–12 ............................................................................................................................... 22

Exhibit 12. Comparison of operational expenditures from site-provided data and the School 

District Finance Survey (F-33), 2011–12 .............................................................................. 25

Exhibit 13. Percentage difference between site-provided data and School District Finance 

Survey (F-33) data, for total salary expenditures and non-personnel expenditures, 

2011–12 ............................................................................................................................... 26

Exhibit 14. Average percentage differences between site-provided data and Civil Rights Data 

Collection (CRDC) data on schools’ total salary expenditures and non-personnel 

expenditures, 2011–12 ........................................................................................................ 27

Exhibit 15. Percentage of study site schools with small, medium, and large differences 

between the site-provided data and Civil Rights Data Collection (CRDC) data on 

salary spending per pupil, 2011–12..................................................................................... 29
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites iii

Exhibit 16. Percentage of study site schools with small, medium, and large differences 

between site-provided data and Civil Rights Data Collection (CRDC) data on nonpersonnel expenditures per pupil, 2011–12........................................................................ 30

Exhibit 17. Average percent difference between the site-provided data and state personnel 

database data on total salaries and instructional salaries, 2011–12................................... 31

Exhibit 18. Percentage of study site schools with small, medium, and large differences 

between the site-provided data and state personnel database data on total salary 

expenditures per pupil, 2011–12......................................................................................... 32

Exhibit 19. Percentage of study site schools with small, medium, and large differences 

between the site-provided data and state personnel database data on 

instructional salaries per pupil, 2011–12............................................................................. 33

Exhibit 20. Average difference between actual tracked expenditures and simulated 

allocations, across eight sites............................................................................................... 34

Exhibit 21. Average percent difference between actual tracked expenditures for instructional 

support and allocations that were simulated based on enrollment and full-time 

equivalent (FTE) staff, 2011–12 ........................................................................................... 35

Exhibit 22. Average difference between actual tracked expenditures for pupil support and 

allocations that were simulated based on enrollment and full-time equivalent 

(FTE) staff, 2011–12 ............................................................................................................. 36

Exhibit 23. Average difference between actual tracked expenditures for retirement benefits 

and allocations that were simulated based on full-time equivalent (FTE) staff and 

staff salaries, 2011–12 ......................................................................................................... 38

Exhibit 24. Average difference between actual tracked expenditures for health benefits and 

allocations that were simulated based on full-time equivalent (FTE) staff and staff 

salaries, 2011–12 ................................................................................................................. 39

Exhibit 25. Study sites’ development of school expenditure data systems........................................... 42

Exhibit 26. Number of study sites reporting that various issues represented a moderate or 

major challenge in implementing or changing their financial data system......................... 48

Exhibit 27. Number of study sites that reported using their school-level expenditure data in 

various ways......................................................................................................................... 51

Exhibit A1. Methods used to attribute personnel expenditures to schools, by type of 

expenditure.......................................................................................................................... 57

Exhibit A2. Methods used to attribute non-personnel expenditures to schools, by type of 

expenditure.......................................................................................................................... 58

Exhibit B1. Personnel and non-personnel expenditures at the district level and at the school 

level, 2011–12...................................................................................................................... 59
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites iv

Exhibit B2. Personnel and non-personnel expenditures at the district level and at the school 

level, 2011–12...................................................................................................................... 59

Exhibit B3. District-level expenditures and school-level expenditures by method of 

attribution, 2011–12............................................................................................................ 60

Exhibit B4. Spending by personnel category and level of attribution for state study sites, 

2011–12 ............................................................................................................................... 61

Exhibit B5. Spending by personnel category and level of attribution for district study sites, 

2011–12 ............................................................................................................................... 62

Exhibit B6. Spending by non-personnel category and level of attribution for state study sites, 

2011–12 ............................................................................................................................... 63

Exhibit B7. Spending by non-personnel category and level of attribution for district study 

sites, 2011–12 ...................................................................................................................... 64

Exhibit C1. Distribution of schools by difference in instructional support spending between 

actual (tracked) expenditures and allocations simulated based on enrollment, 

2011–12 ............................................................................................................................... 65

Exhibit C2. Distribution of schools by difference in instructional support spending between 

actual (tracked) expenditures and allocations simulated based on full-time 

equivalents (FTEs), 2011–12 ................................................................................................ 66

Exhibit C3. Distribution of schools by difference in pupil support spending between actual 

(tracked) expenditures and allocations simulated based on enrollment, 2011–12............ 67

Exhibit C4. Distribution of schools by difference in pupil support spending between actual 

(tracked) expenditures and allocations simulated based on full-time equivalents 

(FTE), 2011–12 ..................................................................................................................... 68

Exhibit C5. Distribution of schools by difference in retirement benefit spending between 

actual (tracked) expenditures and allocations simulated based on full-time 

equivalents (FTEs), 2011–12 ................................................................................................ 69

Exhibit C6. Distribution of schools by difference in retirement benefit spending between 

actual (tracked) expenditures and allocations simulated based on salaries, 

2011–12 ............................................................................................................................... 70

Exhibit C7. Distribution of schools by difference in health benefit spending between actual 

(tracked) expenditures and allocations simulated based on full-time equivalents 

(FTEs), 2011–12.................................................................................................................... 71

Exhibit C8. Distribution of schools by difference in health benefit spending between actual 

(tracked) expenditures and allocations simulated based on salaries, 2011–12.................. 72
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites v 

Acknowledgments 

We wish to thank several individuals who contributed to the completion of this report. First, we are 

deeply grateful to staff at each of the nine sites — Baltimore City Schools, Hillsborough County School 

District, Houston Independent School District, and Los Angeles Unified School District; and the states of 

Florida, Hawaii, Ohio, Rhode Island, and Texas — for their participation in data collection activities and 

willingness to provide data, answer questions, and give their perspective. We also thank the following 

members of the expert panel for their time, expertise, and meeting contributions: Cynthia Brown 

(Rhode Island Department of Education), Amanda Brownson (Texas Education Agency), Matt Cohen 

(retired, Ohio Department of Education), Stephen Frank (Education Resource Strategies), Matt Hill (Burbank 

Unified School District, California), Marguerite Roza (Georgetown University), Timothy Shrom (Solanco School 

District, Pennsylvania), Leanna Stiefel (New York University), and Lori Taylor (Texas A&M University). We are 

also grateful for the review provided by Professor Margaret Goertz (University of Pennsylvania), who 

served as an expert consultant on the study.

Although we appreciate the assistance and support of these individuals, any errors in judgment or fact

are the responsibility of the authors.

Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites vii

Executive Summary

Concerns about the equitable distribution of school funding within and across school districts have led 

to new federal data collections on school-level expenditures. The American Recovery and Reinvestment 

Act of 2009 (ARRA) required states to collect and report, for the first time, school-level data on both 

personnel and non-personnel expenditures from state and local sources for the 2008–09 school year. 

Subsequently, the Office for Civil Rights (OCR) added a similar data collection to its Civil Rights Data 

Collection (CRDC) beginning with the 2009–10 school year, and the National Center for Education 

Statistics (NCES) is currently piloting a School-Level Finance Survey (SLFS) that would extend current 

NCES fiscal data collections to include some school-level variables. In addition, the Every Student 

Succeeds Act (ESSA), enacted in December 2015, amended Title I, Part A of the Elementary and 

Secondary Education Act of 1965 (ESEA) to require states and local educational agencies (LEAs) to report 

school-level per-pupil expenditures on state and LEA report cards. These data may be useful for helping 

policymakers, educators, and others understand the extent to which resources are distributed equitably 

among schools and may also enhance transparency and efficiency in the allocation of education resources.

However, many school districts do not have experience in systematically tracking expenditures at the 

school level, and the quality of these large-scale data collections is uncertain. To better understand the 

feasibility of broadening the collection and reporting of detailed school-level expenditure data, and 

improving the quality of such data, this study examines five states and four school districts that have 

developed their own accounting systems for school-level expenditures: Florida, Hawaii, Ohio, Rhode 

Island, Texas, Baltimore City Public Schools, Hillsborough County Public Schools, Houston Independent 

School District, and Los Angeles Unified School District. Key findings from the study include the following:

• Study sites reported that they attributed most categories of spending to the school level, 

including salaries for teachers, administrators, and other support staff, as well as non-personnel 

items such as textbooks, instructional materials, furniture and equipment, and computers and 

software.

• The school districts and states in this study attributed an average of three-quarters of 

operational expenditures to individual schools, demonstrating that it is feasible to link a 

significant share of spending to the school level.

• Most of the expenditures that the study sites attributed to schools were directly tracked to 

schools (85 percent) rather than simply being allocated by formula (8 percent). 

• Comparisons between the site-reported school-level expenditures and other data sources 

showed a relatively high degree of consistency for salary expenditures, but non-personnel 

expenditure data were much less consistent.

• Allocating expenditures to schools by formula (based on total salaries or staff) appeared 

relatively accurate for health benefits and less accurate for pension benefits, pupil support staff, 

and instructional support staff.

• Instituting a system for collecting school-level expenditure data typically required new hardware and 

software (eight sites), changes to charts of accounts (six sites), and staff training (eight sites).

• Interviewees advised others aiming to implement an accounting system capable of capturing 

school-level expenditures to ensure that stakeholders are involved early, communicate clearly 

and frequently, and think about future data needs in the long term.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites viii

Study Design and Limitations

Methods

This mixed-methods study collected and analyzed school-level and districtwide expenditure data from 

each of the nine sites. It also conducted surveys and telephone interviews with the state- and districtlevel fiscal directors. The sites were asked to provide data files containing both operational expenditures 

and school-level expenditures for the 2011–12 school year for all schools in each state or district, 

organized by the accounting code structure used in their data system. The study also obtained schoollevel expenditure data from the CRDC and district-level data from the School District Finance Survey 

(F-33) for 2011–12. A short survey was administered to obtain information on the specific types of 

expenditures that each site tracked directly to individual schools or allocated to schools by formula. 

This survey was followed by telephone interviews to ask the fiscal directors about the development and 

implementation of their school-level expenditure systems, including challenges, expenses, quality 

assurance methods, and lessons that could be shared with other states and districts that might wish to 

implement similar systems to attribute spending to individual schools.

To measure variation in attribution practices (how the accounting systems determined the amounts 

being spent at individual schools), we calculated the proportion of expenditures in each site that were 

attributed to individual schools and the proportion of those school-attributed expenditures that were 

tracked rather than allocated. In this report, we use the term “tracked” to indicate that specific 

expenditures were explicitly linked to each individual school where an expense was incurred. We use 

the term “allocated” to indicate that the specific expenditures were not explicitly linked to a school; 

instead, the district calculated amounts for each school using a formula designed to approximate 

resource usage (e.g., assigning expenditures on pupil support services to individual schools based on 

each school’s share of total district enrollment).

We examined the reliability of school-level expenditure data in two ways. First, we examined the 

consistency of expenditure data provided by the study sites with those reported in other published 

sources such as the CRDC and the School District Finance Survey (F-33). Second, we examined the 

accuracy of allocating expenditures to schools using formulas by calculating simulated allocations based 

on formulas and comparing these to actual spending figures reported for expenditure categories. 1

In addition to analyzing information collected from the nine study sites, the study team convened a panel of 

education finance experts, including practitioners as well as researchers, to discuss issues and considerations 

regarding the collection and use of school-level fiscal data for the purposes of measuring equity.

Limitations

The study relied largely on expenditure data that were collected and reported in accordance with the 

individual sites’ own practices and needs, and data differences across sites resulted in some sites being 

excluded from portions of the quantitative analysis. Additionally, one site did not participate fully in the 

qualitative data collection.

 

1 Simulated allocations were conducted by distributing total school-level expenditures within a given category by an allocation 

metric, such as a school’s share of district enrollment.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites ix

Our approach to determining the method of attribution — tracking or allocating — was informed by 

district officials’ responses to the pre-interview survey. The survey provided a list of expenditure 

categories and asked whether the expenditures within each category were attributed to schools, and if 

so, whether they were tracked or allocated. In some instances, respondents indicated that both tracking 

and allocating occurred within a category. In these instances, we indicated that the method of 

attribution varied within the category.

Findings regarding practices and challenges are based largely on self-reports of staff interviewed and 

limited documents reviewed, at a small number of purposively selected sites. Therefore, findings are not 

generalizable to other districts and states. Still, administrators may use the study findings to inform their 

own efforts to implement school-level expenditure reporting systems. Specifically, the districts whose 

practices, and policies are described herein are large urban districts; these experiences may not be as 

applicable to smaller or more rural districts.

Key Findings

Expenditure Attribution Practices

With an average of three-quarters of operational expenditures attributed to individual 

schools across the district and state sites, the study results demonstrate that it is 

feasible to link a significant share of spending to the school level.

All nine study sites reported that they attributed most categories of spending to the school level, 

including salaries for teachers, administrators, and other support staff, as well as non-personnel items 

such as textbooks, other instructional materials, furniture and equipment, and computers and software. 

According to the survey responses, the expenditure categories that were least likely to be attributed to 

schools were district-level insurance (five sites), staff who provide districtwide instructional and student 

support services (four sites), centralized curriculum development (six sites), transportation (five sites), 

and district administrative staff and services (six sites).

Across eight of the study sites, the share of operational expenditures that were 

attributed to individual schools ranged from 69 to 89 percent, with an average of 

77 percent.

Fiscal data provided by the sites confirmed that expenditures attributed to schools accounted for a large 

proportion of operational expenditures, ranging from 69 percent in three sites (Hawaii, Baltimore, and 

Los Angeles) to 89 percent in two sites (Ohio and Rhode Island), with an average of 77 percent across 

eight study sites.

Study sites attributed a greater share of personnel expenditures to schools than they 

did for non-personnel expenditures (85 percent versus 53 percent, on average).

There was also a wider range in the percentage of non-personnel expenditures attributed to schools 

across study sites, compared with personnel expenditures. For non-personnel expenditures, the 

percentage attributed to schools ranged from 27 percent to 79 percent, whereas for personnel 

expenditures, all study sites attributed at least 78 percent and as much as 92 percent to schools.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites x 

Most of the expenditures that the study sites attributed to schools were directly 

tracked to schools(85 percent, on average), and 8 percent were allocated using 

formulas.

The percentage of expenditures that the sites reported directly tracking to schools ranged from 66 

percent in Rhode Island to 100 percent in Hawaii. Expenditures that were allocated to schools using 

formulas were distributed in proportion to school shares of various metrics such as total staff, salaries, 

or enrollment; this ranged from 0 percent in Hawaii and Houston to 25 percent in Ohio. Five sites 

indicated that the method for attributing expenditures to schools sometimes varied within a category 

(4 percent of all expenditures attributed to schools in the eight sites). Additionally, Rhode Island 

reported that the decision to track or allocate several categories of expenditures — representing 

25 percent of total statewide school-attributed expenditures — varied across districts in the state. At 

four sites, the attribution method was not clear for some of the expenditures (3 percent of school-level 

expenditures across the eight sites).

Study sites tracked school-attributed personnel expenditures at higher rates 

(90 percent) than non-personnel expenditures (60 percent).

Across eight sites, at least 75 percent of school-level personnel expenditures were tracked to schools, 

with four of the sites tracking 97 percent or more. In contrast, only two sites attributed more than 

75 percent of school-level non-personnel expenditures based on direct tracking, with percentages across 

sites ranging from 10 to 99 percent. 

Looking at specific expenditure categories, the method used to attribute expenditures to schools was 

less consistent across study sites for non-personnel categories than for personnel categories. The 

expenditure categories most likely to be tracked to schools were salaries for teachers and other school 

staff, professional development, and certain categories of supplies and equipment. Expenditures for 

transportation, maintenance, curriculum development, and technology were commonly pro-rated to 

school sites rather than tracked directly.

Metrics used to pro-rate district-level expenditures to the school level included staff FTEs, student 

enrollment, students eligible for free or reduced-price lunch, students being transported (for 

transportation expenditures), and square footage of buildings (for utilities and maintenance and 

operations expenditures).

Consistency of Data With Other Published Data Sources

Site-reported school-level expenditures and expenditures reported in other data 

sources were relatively consistent for salaries, but less consistent for non-personnel 

expenditures. 

Site-provided data on total districtwide salary expenditures differed from equivalent data from the 

School District Finance Survey (F-33) by only 2 percent on average across the eight sites, but nonpersonnel expenditures differed by an average of 21 percent. Total personnel and non-personnel 

expenditures based on the site-provided data differed from the F-33 data by an average of 5 percent. 

Four sites showed differences of 1 percent or less, and the other four sites had differences ranging 

between 6 and 11 percent. Similarly, comparisons with CRDC data found that school-level salary 

expenditures based on site-reported data were much closer to the CRDC data than non-personnel 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites xi 

expenditures (differing by an average of 12 percent for salary expenditures, compared to 129 percent 

for non-personnel expenditures).

The range of differences among the seven sites included in this analysis also varied considerably 

between personnel and non-personnel expenditures. For personnel expenditures, the range was fairly 

narrow (7 to 16 percent), whereas for non-personnel expenditures the differences ranged from a low of 

34 percent to a high of 261 percent. In three of the sites, the difference for non-personnel expenditures 

was greater than 190 percent. 

When comparing site-provided data with CRDC data, the average differences across schools for nonpersonnel expenditures in all study sites were at least two times larger — and in all but one study site 

were at least six times larger — than differences for salary expenditures.

Accuracy of Using Formulas to Allocate Expenditures to Schools

To examine the accuracy of using formulas to allocate expenditures to individual schools, we simulated 

allocations of spending for several expenditure categories that the majority of the study sites reported 

as being tracked to schools. The goal was to compare the expenditure simulations with actual 

expenditures in spending categories that were reported as being directly tracked to schools. We 

therefore calculated the percentage difference between the actual reported expenditures and simulated 

allocations for each school within a site. The percentage differences for individual schools were then 

averaged across sites to provide the average difference between the reported and allocation-simulated 

spending for each site.

Allocating expenditures to schools by formula appeared relatively accurate for health 

benefits and less accurate for pension benefits, pupil support staff, and instructional 

support staff.

When allocating expenditures to schools by pro-rating based on their shares of total district FTEs, the 

average differences between actual tracked expenditures and simulated allocations were 12 percent for 

health benefits, 18 percent for retirement benefits, 29 percent for pupil support, and 51 percent for 

instructional support. However, the relative accuracy of allocating expenditures using formulas varied by 

expenditure category and allocation method. Allocations of health and retirement benefits were more 

accurate when distributed according to shares of total salaries rather than shares of FTEs. Allocations of 

pupil and instructional support spending were more accurate when using school shares of FTEs rather 

than shares of student enrollment.

Developing and Implementing Systems to Collect School Expenditure Data

Study sites’ motivations for developing systems to collect school expenditure 

data typically included the goals of increasing the transparency and equity of 

school expenditures.

Interviews with officials from eight study sites revealed that they had been collecting and reporting 

school-level expenditure data for varying amounts of time, but most (seven sites) had been doing so for 

two or more decades, and most (five sites) reported making significant improvements to their school 

expenditure data system within the past 10 years. Site officials tended to report similar motivations for 

developing or expanding their school expenditure data systems. Officials from all four state sites said 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites xii

their state’s school expenditure data system was initiated or expanded in response to state laws 

intended to promote equity and transparency in school spending. Officials from three of the district sites 

indicated that district efforts to give schools more authority over spending decisions was a major reason 

for collecting school-level expenditure data. Officials from the fourth district site reported developing 

their data system in response to an equity-focused state mandate.

Instituting a system for collecting school-level expenditure data typically required new 

hardware and software (eight sites), changes to charts of accounts (six sites), and staff 

training (eight sites).

All six sites that provided information on the expenses associated with developing their system for 

collecting school-level expenditure data 2 indicated that they incurred both personnel and nonpersonnel expenses. Reported personnel expenses included staff time spent choosing and/or designing 

the data system, planning system roll-out strategies, and developing training materials. Common nonpersonnel expenses included contracts with vendors or consultants (four sites) and technology upgrades 

(four sites).

Sites reported that maintaining their school expenditure data systems required costs 

related to staff training, new central office staff, and technology improvements.

Personnel expenses associated with implementation of the accounting systems included new 

information technology and finance staff in their central offices, staff time to develop training materials 

and provide training, and time for school-level staff (in the study districts) to manage expenditure 

reporting. Non-personnel expenses often related to technology improvements (e.g., server expansions 

and upgrades) and database maintenance contracts.

Surveyed site officials most commonly identified staff capacity and training needs as a 

major challenge in implementing systems for tracking school-level expenditures. The 

most commonly identified challenge that sites continued to face was in tracking 

certain types of expenditures.

During interviews, site officials elaborated on their challenges with regard to staff capacity, emphasizing 

staff difficulty in reporting expenditures correctly and site difficulty in providing training to address staff 

capacity needs. Other reported challenges included outdated hardware and software (e.g., unsupported 

operating systems in schools) and initial resistance from schools (e.g., reluctance to take on new roles in 

managing budgets and spending, perceptions that the changes were not needed, and general opposition 

to change).

However, most sites indicated that they overcame many of these challenges as staff adjusted to new 

expectations and procedures, and received additional training. The most commonly identified challenge 

that sites continued to face was in tracking certain types of expenditures, such as staff who work at 

multiple schools and telecommunications, because it was difficult to know who used phone and Internet 

services at which schools and to what extent.

 

2 Analyses of the costs incurred and staff involved in developing systems for collecting school-level expenditure data exclude 

Hawaii and Texas because their systems were developed more than 20 years ago and state officials who were interviewed were 

unable to speak to these details.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites xiii

To promote data reliability, study sites relied most heavily on staff training (eight 

sites), data reviews (seven sites), and automated error checks in their respective 

software systems (six sites).

Although staff training was the most common method used by study sites to ensure the reliability of 

their data, the focus and intensity of the training varied across sites. At the state level, Texas and Rhode 

Island provided training to districts on the chart of accounts, and Hawaii provided training on financial 

reporting procedures overall. At the district level, training ranged from limited and sporadic training in 

Baltimore to monthly principal meetings in Hillsborough to making training a requirement to access the 

software in Houston.

Most sites also established mechanisms to locate errors in data input through data audits or automatic 

checks in their software that flag incorrect use of accounting codes or implausible values. In addition, 

officials from six sites noted that their personnel and expenditure data systems could connect to one 

another, another feature that may promote data reliability.

Site officials reported using their school-level expenditure data primarily to promote 

transparency around school expenditures (five sites) and to inform decision-making 

(six sites).

Officials from five study sites indicated that they post school expenditure data on their state or district 

websites to foster transparency with the general public. Sites that reported using school expenditure 

data to make decisions described using these data to assess compliance with federal and state laws 

(four sites), develop school budgets and monitor spending (three sites), evaluate which investments 

have been cost-effective (three sites), and/or guide strategic planning (two sites). Officials from Hawaii 

and Texas indicated that, aside from promoting transparency, their school-level expenditure data were 

not widely used.

Interviewees advised others aiming to implement such systems to involve 

stakeholders early, communicate clearly and frequently, and think about long-term 

data needs.

When asked what advice they would offer other states and districts aiming to develop or improve 

similar systems, site officials made three main recommendations:

• Include a wide range of stakeholders in the development process at the school and district levels 

and in the larger community.

• Communicate clearly and frequently with all stakeholders (both before and during changes 

to systems).

• Think about long-term needs when designing software and charts of accounts for school 

expenditure reporting.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites xiv

Conclusions

The experiences of the nine sites included in this study demonstrate that collecting and reporting highquality school-level expenditure data is feasible and has perceived benefits for transparency, equity, and 

the efficient use of resources. Each of the study sites attributed a large majority of their operational 

expenditures to individual schools, and most of these expenditures were directly tracked to school sites 

rather than merely being allocated post hoc using formulas. A key challenge in the process of collecting 

and reporting school-level expenditure data — according to the panel of experts, as well as our own 

findings — is ensuring consistency in practices surrounding the attribution of dollars to schools both 

within and across districts and states.

The states and districts in this study were most able to track personnel expenditures to the school level. 

Data on non-personnel expenditures were much less comprehensive and consistent. Our findings also 

raise concerns about the accuracy of allocating expenditures to schools using formulas, rather than 

directly tracking expenditures to schools.

In addition, differences across sites in the types and shares of expenditures attributed to schools, and 

the methods used to do so, indicate that reported school-level expenditure figures are not necessarily 

comparable across sites. Nevertheless, the experiences and data shared by these five states and four 

school districts may serve as examples for others wishing to develop or improve their own systems for 

collecting and reporting high-quality data on school-level expenditures.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 1 

I. Introduction 

Although funding inequities between more and less affluent districts have been well documented, little 

data are available to help us understand whether spending in schools with different needs within 

districts is equitable. Concerns about the equitable distribution of school funding within school districts 

have led to new federal data collections on school-level expenditures. The American Recovery and 

Reinvestment Act of 2009 (ARRA) required states to collect and report, for the first time, school-level 

data on both personnel and non-personnel expenditures from state and local sources for the 2008–09 

school year. Subsequently, the Office for Civil Rights (OCR) added a similar data collection to its Civil 

Rights Data Collection (CRDC), beginning with the 2009–10 school year, and the National Center for 

Education Statistics (NCES) is currently piloting a School-Level Finance Survey (SLFS) that would extend 

current NCES fiscal data collections to include some school-level variables. 3 In addition, the Every 

Student Succeeds Act (ESSA), enacted in December 2015, amended Title I, Part A of the Elementary and 

Secondary Education Act of 1965 (ESEA) to, among other things, require states and local educational 

agencies (LEAs) to report school-level expenditures per pupil on annual report cards, “including actual 

personnel expenditures and actual non-personnel expenditures” (ESEA section 1111(h)(1)(C)(x)). 4

 

School-level expenditure data are important in two ways. First, these data show whether resources are 

distributed equitably across schools. Previous measures used to determine the comparability of services 

between high- and low-poverty schools, such as staff-to-student ratios, have masked inequities in school 

spending. Second, detailed school-level expenditure data also help practitioners and researchers better 

understand associations between patterns of spending and student outcomes, and identify successful 

and cost-effective practices.

However, many school districts do not have experience in systematically tracking expenditures at the 

school level, so the quality of these data is uncertain. To respond to federal data collections on schoollevel expenditures, states and districts may simply pro-rate — or allocate — district expenditures to 

individual schools in proportion to student enrollment or based on other metrics or formulas. Even 

when expenditures are tracked to individual schools, school districts and states may differ in the 

definitions and procedures used and the types of expenditures that are included and excluded. 5 Nonpersonnel expenditures may be the most difficult to track and report at the individual school level, but 

understanding how expenditures on non-personnel resources such as professional development,

instructional materials, and technology are distributed among schools may also be important for 

comprehending the distribution and uses of educational resources at the school level.

 

3 The SLFS is an extension of an existing data collection conducted by NCES in collaboration with the Census Bureau: the School

District Finance Survey (F-33), an annual survey that collects district-level data on a wide range of education expenditures and 

revenues. The SLFS is essentially an expansion of the School District Finance Survey (F-33) to include some school-level 

variables.

4 All references to the ESEA in this document refer to the law as amended by the ESSA. 

5 In this report we use the word attributed to mean that expenditures were linked to individual school sites, whereas tracked

and allocated are methods by which expenditures were attributed. Tracked means expenditures were identified at the school 

where actual resource use or cost occurred. Allocated means expenditures were not identified at the school where resource 

use or cost occurred, but expenditures were instead distributed across schools according to a formula intended to approximate

resource use.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 2 

To explore the feasibility of broadening the collection and reporting of meaningful school-level fiscal 

data, this study examines the nature and quality of school-level fiscal data collection in nine case study 

sites — five states and four school districts — that developed their own systems for collecting and 

reporting school-level expenditures: Florida, Hawaii, Ohio, Rhode Island, Texas, Baltimore City Public 

Schools, Hillsborough County Public Schools, Houston Independent School District, and Los Angeles 

Unified School District. Four research questions guided this study:

1. In states and districts that have developed systems to report expenditures at the school level, 

what types of personnel and non-personnel expenditures are included in the school-level data? 

2. To what extent do the sites track actual expenditures to individual schools versus allocating or 

pro-rating expenditures to schools using formulas?

3. How consistent are school-level expenditure data obtained from these systems with similar data 

from other sources? How do the funding amounts attributed to individual schools based on 

formula allocations compare to those based on tracking actual expenditures? 

4. What lessons can other states and districts learn from these sites if they wish to implement 

systems for reporting accurate and reliable data on school-level expenditures?

Exhibit 1 shows the conceptual framework of this study. The study’s analyses focus on the contents of 

the blue box, seeking to better understand the types of expenditures that are linked — or attributed — 

to schools and the methods by which funds are attributed to these sites. In addition, the study measures 

differences between the raw data contained in the study sites’ accounting systems and the data 

reported for external federal data collections (e.g., the CRDC). Finally, the study examines the 

experiences of states and districts that have implemented such systems, including their motivations, the 

challenges and expenses they have faced, and their efforts to ensure data quality.

Exhibit 1. Conceptual framework
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 3 

Challenges Involving School-Level Fiscal Data Collection

Ideally, school-level fiscal data collection results in detailed expenditure data that are comprehensive, 

consistent, and accurate. Comprehensive spending data capture all expenditures that were made and 

attributes the correct proportion of these to the school level. Consistent spending data report 

expenditure levels that are similar across different independent data collections. Accurate data reflect 

the true amount of spending on resources actually received and used by schools and the district. 

However, there are numerous challenges to collecting school-level fiscal data, which have implications 

for the comprehensiveness, consistency, and accuracy of the resulting data.

Certain types of expenditures are more difficult to collect than others. For example, data on itinerant 

teachers — those working at more than one school — may be more challenging for districts to collect. 

State staffing databases and district expenditure databases that track staff to individual schools often do 

not track itinerant teachers to each school site (Goertz 1997). 6 Staffing databases may also only include 

information on certificated staff, such as teachers, thereby excluding uncertificated staffing positions 

such as teacher aides (Goertz 1997). 

Accurate and detailed expenditure data for non-personnel resources, such as instructional materials and 

equipment, are often difficult to collect — funds may be budgeted to schools, programs within schools, 

or even individual teachers, and spending decisions then made by those entities. Follow-up is then 

required to either catalog or qualitatively report what was ultimately purchased (Goertz 1997). 

Furthermore, schools or teachers may purchase equipment and materials using alternative funding 

sources, such as PTA funds, grants awarded to individual teachers, or private donations, which the data 

do not capture (Goertz 1997).

Another complication is that not all spending within a district is attributed to schools. In the early 2000s 

a study of 10 urban school districts reporting school-level expenditure data showed that between 38 

and 95 percent of total district expenditures were attributed to schools (Miller, Roza, and Swartz 2004). 

This wide variation was largely due to differences in decisions about whether and how to attribute nonschool-based expenditures to individual schools. Miller, Roza, and Swartz (2004) identified three aspects 

of the overall spending of school districts:

1. School spending: Expenditures that can be tracked directly to school sites, such as expenditures

on staff working at schools and certain school-based non-personnel items.

2. Shared district resources: Expenditures for staff, materials, and services that are housed in or 

supervised by district central offices but that directly benefit students and schools. These shared 

resources may include itinerant staff, expenditures on transportation of students, and programs 

for students from multiple schools (such as summer school or special music and art programs).

3. District operations and leadership: Expenditures for personnel and services not used directly by 

students or schools, such as the superintendent, other district governance, and district 

management expenses.

 

6 In the expert panel meeting, both practitioners and researchers indicated that itinerant teachers are often not tracked to 

school sites in their districts or those they have worked with.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 4 

Districts within and across states vary in how they treat the latter two categories of resources, making 

cross-district comparisons of school-level expenditures more difficult. Districts must decide whether and 

how to allocate shared district resources to schools, and whether the shared resources can be 

disentangled from the district operations and leadership expenses. For example, certain services, such 

as student busing, are provided for students of individual schools, but buses and the transportation staff 

driving and maintaining the buses often serve students going to multiple schools. Districts must decide 

whether to attribute those transportation expenses to schools or keep them at the district level. And if 

the attribution of shared resources does not reflect the actual use of those resources, the resulting 

estimates of school spending will not be accurate. 

Characteristics of the Case Study Sample

The five states and four school districts in this study were chosen largely based on the extent to which 

each had established accounting systems capable of attributing expenditures to individual schools. 

These states and districts represent a variety of geographic regions within the United States, including 

the South, Northeast, Midwest, and Pacific. Exhibit 2 shows characteristics of the nine sites, including 

numbers of districts, schools, and students; levels of operational expenditures (also referred to as 

current expenditures); and distribution of student enrollment by type of school. 7

Exhibit 2. Characteristics of sample states and districts, 2011–12

Name of 

State or 

District

# of 

Districts

# of 

Schools

# of 

Students

Operational 

Expenditures

Operational 

Expenditures 

per Pupila

% of 

Students in 

Regular 

Schools

% of 

Students 

in Charter 

Schools

% of 

Students 

in Other 

Schools

Hawaii 1 286 182,705 $2.2 billion $12,054 95 5 <1

Florida 74 3,920 2,668,113 22.2 billion 8,342 91 7 2 

Ohiob 1,003 3,585 1,722,183 17.9 billion 10,281 93 7 <1

Rhode Island 54 299 141,564 2.1 billion 14,426 95 3 2 

Texas 1,235 8,557 5,000,193 40.9 billion 8,182 96 4 1 

Baltimore 1 195 84,212 1.3 billion 15,287 76 15 9 

Hillsborough 1 286 197,041 1.6 billion 8,318 93 5 2 

Houston 1 278 203,012 1.9 billion 9,232 90 9 1 

Los Angeles 1 935 659,132 7.0 billion 10,602 80 14 5 

Exhibit reads: In the 2011–12 school year, the State of Hawaii public school system consisted of one school district, 

286 public schools, and 182,705 students. Operational expenditures in Hawaii were $2.2 billion and operational 

expenditures per pupil were $12,054. Students attending regular schools accounted for 95 percent of public school 

students; 5 percent attended charter schools, and less than 1 percent attended other types of schools (such as 

special education, vocational, or alternative schools). 

a Operational spending per pupil is defined as operational spending divided by number of students.

b Data on the number of districts, schools, and students for Ohio are from the 2013–14 school year. Data on operational expenditures and 

operational expenditures per pupil are from the 2011–12 school year (for Ohio and for all other study sites).

Source: U.S. Department of Education, National Center for Education Statistics, Common Core of Data (CCD), “Public Elementary/Secondary 

School Universe Survey,” School District Finance Survey (F-33).

 

7 The definition of operational spending used in this study is similar to that of current expenditures used in the School District 

Finance Survey (F-33), which includes spending for the daily functioning of public education systems to support elementary 

and secondary education and excludes expenditures for capital outlay (e.g., purchases of land, school construction, and 

equipment) and debt service. In addition, the definition excludes expenditures for programs outside the scope of public 

prekindergarten through grade 12 education such as adult education and community services.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 5 

The nine sites in this study, although small in number, account for a relatively large share (18 percent) of 

total operational expenditures for K–12 education in the United States. 8 The sites differ in several ways. 

Total operational expenditures for elementary and secondary education ranged from less than $2 billion 

in three of the four districts to over $40 billion in Texas. Per-pupil operational expenditures ranged from 

$8,182 in Texas to $15,287 in Baltimore. For the five state education systems, the number of students 

served in 2011–12 ranged from 142,000 to 5.0 million students. Enrollment sizes for the four school 

districts ranged from 84,000 to 659,000 students; the three largest school districts each had more 

students than two of the four states.

The sites also varied in the types of schools students attended. The share of students attending regular 

schools ranged from 76 percent in Baltimore to 96 percent in Texas. Each site contained charter schools 

with the share of students attending such schools ranging from 3 percent in Texas to 15 percent in 

Baltimore. Other schools such as vocational, alternative, and special education schools also accounted 

for a share of the student population in each site ranging from in Baltimore (9 percent) and Los Angeles 

(5 percent). The shares of the overall student population served in the study sites attending regular, 

charter and other schools were 93 percent, 6 percent, and 1 percent, respectively. 

Data and Methods

The study team collected and analyzed three types of data for this study: quantitative fiscal data from 

case studies, qualitative data from interviews and surveys of case study officials, and focus group data 

from a panel of education finance experts.

Analyses of Fiscal Data

We conducted analyses of fiscal data to address the first three research questions: examining the types 

of expenditures attributed to schools, the methods for attributing expenditures, and the consistency 

and accuracy of school-level data collections. The study team obtained fiscal data from each sample site 

containing all expenditures and revenues, organized by function, object, and location. To increase the 

consistency of these data across the sites, we used the function and object information to categorize the 

expenditures into personnel and non-personnel categories based on the categories used by the School 

District Finance Survey (F-33) — which is widely used and is familiar to education finance practitioners 

as well as researchers. We used site-provided crosswalks between their data and the F-33 to perform 

the conversion of site-provided data to the F-33 foundation. We also used documentation provided by 

each site to identify more detailed expenditures within their charts of accounts. Site-provided location 

codes, which attribute an expenditure to a specific school or department, were used to determine 

whether the expenditure was attributed to an individual school or kept at the district level.

 

8 The nation’s total K–12 current expenditures for 2011, calculated by summing district-level figures from the School District 

Finance Survey (F-33), were $521 billion.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 6 

We supplemented data from each site with demographic data from the Common Core of Data “Public 

Elementary/Secondary School Universe Survey.” This dataset contains information on enrollment and 

number of full-time equivalent (FTE) teachers at each school, as well as whether the school is classified 

as charter, magnet, vocational, special education, or alternative education. For certain analyses these 

non-regular schools were excluded; they typically have expenditure patterns that differ from more 

traditional schools, and may have less reliable or accurate reporting of data. 9

The primary purpose of the quantitative analysis was to examine differences in the types and amount of 

expenditures attributed to individual schools across the study sites; it also helped assess the general 

reliability of each site’s expenditure figures. There were three components to the quantitative analysis 

— comprehensiveness, consistency, and accuracy — each intended to examine a different aspect of the 

data. Below is a brief description of the methodology for each analysis:

• Comprehensiveness. To assess the comprehensiveness of the data we examined the variation 

across study sites in how much of operational spending (in total and by expenditure category) 

was attributed to individual schools, as well as whether school-attributed expenditures were 

tracked or allocated. 

• Consistency. To assess how consistent the expenditure data collected from the study sites were 

with other data sources, the research team compared the site-reported expenditures with data 

reported in the CRDC, the School District Finance Survey (F-33), and state personnel databases.

• Accuracy. To gauge the accuracy of allocating expenditures to individual school sites rather than 

directly tracking expenditures to schools, we used an allocation formula to recalculate 

expenditures that were reported as being tracked, and compared this “simulated” allocated 

expenditure with the actual tracked spending. For this analysis we selected a small number of 

spending categories that most sites reported as being tracked but might plausibly be allocated in 

other districts and states — health benefits, retirement benefits, pupil support, 10 and 

instructional support. 11, 12

Surveys and Interviews of Fiscal Office Staff

To understand the lessons learned from the case study sites to inform future implementation of systems 

for collection and reporting school-level data (Research Question 4), the study team relied on qualitative 

data collection. 

 

9 We explored conducting consistency analysis that compared site-reported data on charter schools to the CRDC data on 

charter schools. In some instances the results for charter schools was similar to that of regular schools; however, in other 

instances the results were quite different. Given this finding, non-regular schools were excluded from the consistency 

analysis comparing site-provided data to CRDC data and the accuracy analysis. These analyses are described further below.

10 Pupil support is defined by the National Center for Education Statistics as expenditure for attendance record keeping, social 

work, student accounting, counseling, student appraisal, record maintenance, and placement services. This category also 

includes medical, dental, nursing, psychological, and speech services.

11 Instructional support is defined by the National Center for Education Statistics as expenditure for supervision and instruction 

service improvements; curriculum development; instructional staff training; and instructional support services, such as 

libraries, multimedia centers, and computer stations for students that are outside of the classroom.

12 Almost all of the study sites reported tracking school-level expenditures for health benefits, retirement benefits, instructional 

support, and pupil support. In Baltimore, officials indicated that expenditures for health and retirement benefits were 

allocated rather than tracked; therefore, this site was excluded from the accuracy analysis for these spending categories.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 7 

We conducted surveys and interviews with fiscal office staff at eight 13 of the nine study sites to gather 

in-depth information about the following:

• Details surrounding the accounting system each site used to attribute spending to 

individual schools.

• Which types of expenditures were tracked or allocated to schools and the allocation 

methods used.

• Challenges the state or district encountered in establishing the accounting system, and 

how these challenges were addressed.

• The level of effort involved in developing and implementing the accounting system.

• How the school-level expenditure data were being used.

Prior to the interviews, the research team asked each site to complete a survey to provide descriptive 

information about the specific types of expenditures that the site tracked directly to individual schools 

or allocated to schools by formula, along with closed-ended questions about specific challenges and 

types of data systems. The survey instrument is included in Appendix D.

The research team conducted descriptive analyses of the survey responses to tabulate the number of 

sites that used particular methods for attributing various types of expenditures to schools. The research 

team also used the survey data about the study sites’ attribution methods in analyses of sites’ fiscal data 

(see above).

To collect more in-depth information about sites’ experiences collecting and reporting school-level 

expenditures, the research team conducted semi-structured interviews with financial officials at eight 

sites, using an interview protocol that asked about the processes used and any challenges that had been 

faced. Depending on the preference of the site, the number of individuals at each interview ranged from 

only one person to a group of staff with responsibilities that included working with school expenditure 

data. To ensure accurate interview data, the research team recorded each formal interview (after 

receiving consent), and a junior-level researcher took notes. The note-taker later checked the interview 

notes for accuracy and completeness using the audio for reference. In addition, immediately following 

each interview, the lead interviewer made separate notes regarding key themes and lessons learned; 

these notes served as an information source against which to check the conclusions from the coding and 

analysis of the interview data.

The study team coded the qualitative data using NVivo qualitative analysis software. The study team 

developed a list of codes aligned with the study’s research questions. We applied multiple codes to the 

interview data when appropriate, so that the study team could run queries to locate interview segments 

on particular topics. The interview transcripts were independently coded by two study team members 

to ensure reliability, and the coded data were used to create detailed case narratives for each of the 

eight sites. The narratives summarized key information related to the sites’ adoption of accounting 

systems attributing expenditures to schools; processes for collecting and reporting, and then using, 

these expenditure data; and quality assurance procedures. The synthesized narrative data were used to 

conduct cross-case comparisons to identify themes and patterns.

 

13 Ohio was unable to participate in an interview.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 8 

Convening of Expert Panel

The study team also convened a panel of experts in the field of education finance for a one-day meeting 

to discuss their perceptions of the feasibility and utility of collecting school-level finance data. The panel 

consisted of nine individuals, including three college professors, two researchers at research 

organizations, and four practitioners working in school districts or state education agencies (SEAs).

The comments of the panel members were used to refine the study design and data collection 

instruments. In addition, panel comments are also included in this report where they may help readers 

understand the issue being discussed.

Study Limitations

Limitations for this study relate largely to the uniqueness of the fiscal data collected from each site. 

Although the research team requested the same types of data from each site, the information that was 

provided varied widely in form as well as ease of use. The data preparation procedure was intended to 

group expenditures into categories based on function and object uniformly across all sites. Despite 

efforts to make the data as comparable as possible, there are likely to be differences across sites due to 

differences in how variables were defined as well as in the structure and coding of the data. 

Furthermore, decisions were made about how to categorize expenditures according to each study site’s 

chart of accounts prior to receiving the data, which in turn impacted how we categorized expenditures 

for our purposes. For example, in school districts in Texas, including Houston, all furniture, equipment, 

computers, and software were categorized as capital expenditures. Because we limited the scope of 

spending for this project to operational expenditures, which excludes capital expenditures, these were 

excluded for both Texas and Houston. In contrast, other sites categorized these expenses as operational, 

which resulted in these types of expenditure being included in the analysis. Although differences such as 

these impact the comparability of the data across the sites, one of the purposes of this study was to 

examine the difficulties associated with collecting and analyzing school-level fiscal data. Therefore, the 

process of collecting and preparing the data was an informative exercise, highlighting some of the 

challenges in using school-level fiscal data.

Differences in the data reported by the nine study sites also affected the analyses that could be 

performed. Site-specific data issues, which led to the omission of certain sites from at least some of the 

quantitative or qualitative analysis, are as follows:

• Baltimore. The analysis of the accuracy associated with allocating health and retirement benefit 

spending to schools using formulas rather than tracking could only be conducted on sites that 

actually tracked such expenditures to individual schools. Because Baltimore allocated these 

expenditures via formula, the site could not be included in the accuracy analysis of these 

particular spending categories.

• Florida. This site reported school-level expenditure data that had been aggregated into broad 

expenditure categories and did not include the types of detailed expenditure categories needed 

for most of the analyses conducted for this report. As a result, data for Florida were excluded 

from most quantitative analyses.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 9 

• Hawaii. Benefits in Hawaii are handled by the Department of Budget and Finance, and therefore 

do not appear in the school-level expenditure files provided by the Hawaii Department of 

Education. For the analysis of consistency between the Hawaii and School District Finance 

Survey (F-33) data, benefits were not included. Hawaii was also excluded from the accuracy 

analysis of the allocation of health and retirement benefit spending to schools.

• Ohio. Ohio was not able to provide data for 2011–12 and instead provided data for the 2013–14 

school year. Because 2013–14 data are not yet available for the CRDC and the School District 

Finance Survey (F-33), Ohio was excluded from all consistency analyses. In addition, Ohio did not 

participate in the pre-interview survey or formal interview and therefore was excluded from 

most of the qualitative analysis. However, representatives from the Ohio Department of 

Education participated in several phone conversations, enabling us to determine which 

expenditure categories were attributed to schools and by what method.

• States with personnel databases for consistency analysis. Only two states, Hawaii and Texas, 

had statewide databases available that included spending on school-level personnel. Therefore, 

the only sites where we could compare personnel spending between site-provided data and 

data from a state personnel database were Hawaii, Texas, and the Houston school district.

Findings regarding practices and challenges are based largely on self-reports of staff interviewed and 

limited documents reviewed, at a small number of purposively selected sites. Therefore, findings are not 

generalizable to other districts and states. Still, administrators may use the study findings to inform their 

own efforts to implement school-level expenditure reporting systems. Specifically, the districts whose 

practices and policies are described herein are large urban districts; these experiences may not be as 

applicable to smaller or more rural districts.

Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 11 

II. Expenditure Attribution Practices

This chapter examines the types of expenditures that the study sites attributed to schools, the extent to 

which various expenditure types were attributed to schools, and how this varied across sites and by type 

of expenditure. In addition, this chapter examines the methods used to attribute those expenditures to 

schools, particularly the extent to which the expenditures were directly tracked to individual schools 

rather than being allocated or pro-rated using various metrics and formulas.

Types of Expenditures Attributed to Schools

To examine the types of expenditures that were attributed to schools, we first relied on responses to 

the surveys administered to each site prior to the interviews and collection of detailed school-level 

expenditure data. During the interviews, we followed up with site staff regarding their survey responses 

to enable them to clarify and elaborate as appropriate. Finally, we examined the extent to which various 

types of expenditures were attributed to individual schools by using the location codes embedded in the 

fiscal data. 14

With an average of three-quarters of operational expenditures attributed to individual 

schools across the district and state sites, the study results demonstrate that it is 

feasible to link a significant share of spending to the school level.

All nine study sites reported that they attributed most categories of spending to the school level, 

including salaries for teachers, administrators, and other support staff, as well as non-personnel items 

such as textbooks, other instructional materials, furniture and equipment, and computers and software.

The expenditure categories that were least likely to be attributed to schools were security staff and 

services (three sites), curriculum development staff (three sites), and liability and property insurance 

(five sites). However, even among sites that reported attributing certain categories of expenditures to 

the school level, there was sometimes wide variation in the amount of such expenditures, based on the 

fiscal data. 15 For example, although eight sites reported attributing non-personnel expenditures for 

professional development services to schools, these school-level expenditures accounted for just 

33 percent of total non-personnel expenditures for professional development (Exhibit 3).

 

14 Tables showing spending amounts by expenditure category for each study site are provided in Appendix B.

15 Additionally, the fiscal data sometimes showed small amounts of money assigned to individual schools within expenditure 

categories that were reported as not attributed to schools in the pre-interview survey.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 12 

Exhibit 3. Number of study sites that reported attributing various types of expenditures to schools, 

and average percentage of operational expenditures attributed to schools, by 

expenditure type, 2011–12

Expenditure Type Number 

of Sites 

Average Percentage of Operational 

Expenditures Attributed to Schools

Salaries for School-Level Personnel

Principals and other school administrative staff 9 99%

Teachers 9 97%

Instructional support staff 9 71%

Pupil support staff 9 82%

School maintenance and operations staff 9 63%

Food services personnel 9 75%

Salaries for Other Personnel

Professional development staff 9 70%

Curriculum development staff 3 23%

Transportation staff 6 22%

Security staff 3 63%

Other district staff 9 6%

Employee Benefits

Health/group benefits 9 78%

Retirement benefits 8 67%

Other benefits 8 74%

Non-Personnel Expenditures — Services

Professional development 8 33%

Security services 3 56%

Transportation 8 28%

Insurance (e.g., liability, property) 5 21%

Utilities 7 63%

Non-Personnel Expenditures — Supplies

Textbooks and instructional materials 8 78%

Furniture and equipment 7 79%

Computers and software 7 85%

General supplies 8 75%

Food supplies 8 76%

Exhibit reads: Nine sites reported that they attributed salaries for principals and other school administrative staff 

to schools, and school-level expenditures accounted for 99 percent of total site expenditures in this category.

Note: Average percentage of total expenditures attributed to schools does not include Florida.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 13 

The percentage of specific expenditure categories that were attributed to schools often varied 

considerably across sites. For example, although all sites reported attributing salary expenditures for 

instructional support staff at the school level, the percentage of these expenditures attributed to 

schools ranged from less than 40 percent in Hawaii and Houston to over 90 percent in Ohio and 

Baltimore (Exhibit 4). Some expenditure categories showed a high degree of consistency across sites in 

either high coverage (e.g., teacher pay) or low coverage (e.g., “other” district staff). More commonly, 

however, there was relatively wide variation across sites, suggesting that the composition of schoollevel expenditures is often not consistently defined or measured across these sites.

Exhibit 4. Share of personnel expenditures attributed to schools, by type of expenditure and by 

site, 2011–12

Expenditure Type Hawaii Ohioa Rhode 

Island Texas Baltimore Hillsborough Houston Los Angeles 

School administrative 

staff pay 100% 99% 99% 99% 97% 97% 100% 98%

Teacher pay 95% 100% 99% 97% 97% 98% 99% 94%

Instructional support 

staff pay 36% 92% 87% 85% 99% 62% 32% 77%

Pupil support staff pay 55% 89% 90% 88% 99% 88% 63% 81%

Maintenance and 

operations staff pay 89% 86% 79% 35% 58% 60% 41% 54%

Security personnel pay — 85% 87% — 18% — — — 

Transportation staff 

pay 1% 73% 62% 1% 0% 0% 2% 37%

Food service 

personnel pay 96% 82% 83% 65% 89% 88% 25% 71%

Professional 

development staff pay 49% 80% 81% 69% 92% 84% 31% 75%

Curriculum 

development staff pay 0% 78% — — 6% 29% 0% — 

Other district staff 2% 13% 14% 2% 1% 1% 4% 10%

Health/group benefits 32% 79% 92% 83% 79% 86% 83% 92%

Retirement benefits — 87% 92% 82% 15% 85% 20% 91%

Other benefits — 95% 86% 80% 77% 85% 26% 71%

Exhibit reads: In Hawaii, 100 percent of spending for school administrative staff pay was attributed to schools, 

95 percent of spending for teacher pay was attributed to schools, etc.

a Data for Ohio are from the 2013–14 school year.

Note: Detail on personnel expenditure attributed to schools by type of expenditure is provided in Exhibits B4 and B5 in Appendix B.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 14 

Non-personnel expenditure categories often showed even more variation across sites (Exhibit 5), 

indicating that these expenditures are likely to be less comparable across districts and states than 

personnel spending. According to the panel of education finance experts convened early in this study, 

certain types of non-personnel spending — such as contracted services, utilities, and 

telecommunications — are often managed centrally by the district, even though they are benefiting 

individual schools. These centrally managed expenditures are often paid as a single bill for the district, 

and it is difficult to determine usage by school site and thus assign expenditures to schools accurately. 

For this reason, these types of spending are not reported at the school level.

Exhibit 5. Share of non-personnel expenditures attributed to schools, by type of expenditure and by 

site, 2011–12

Expenditure Type Hawaii Ohioa Rhode 

Island Texas Baltimore Hillsborough Houston Los Angeles 

Textbooks / 

instructional 

materials

90% 99% 99% 41% 76% 94% 56% 66%

General supplies 64% 95% 88% 68% 69% 79% 76% 58%

Food supplies 98% 87% 68% 66% 13% 91% 98% 89%

Furniture and 

equipment 75% 94% 84% — 75% 72% — 76%

Computers and 

software 81% 97% 81% — 93% 63% — 97%

Transportation 1% 78% 95% 2% 1% 13% 2% 28%

Professional 

development 6% 81% 47% 57% 19% 13% 20% 22%

Curriculum 

development 82% 77% — — 18% 2% 0% —

Other district 

services 2% 27% 17% 2% 0% 7% 1% 1%

Purchased services 

and rentals 15% 91% 77% 48% 56% 71% 69% 5%

Telecommunications 61% 87% 56% — 2% 32% — 61%

Utilities 0% 93% 92% 51% 0% 94% 76% 96%

Insurance 57% 67% 34% 8% 0% 0% 0% 0%

Security — 83% 82% — 2% — — —

Miscellaneous items 100% 89% 68% 58% 55% 83% 68% 47%

Exhibit reads: In Hawaii, 90 percent of spending for textbooks and instructional materials was attributed to 

schools, 64 percent of general supply spending was attributed to schools, and so forth.

a Data for Ohio are from the 2013–14 school year.

Note: Detail on personnel expenditure attributed to schools by type of expenditure is provided in Exhibits B6 and B7 in Appendix B.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 15 

Share of Operational Expenditures Attributed to Schools

Across eight of the study sites, the share of operational expenditures attributed to 

individual schools ranged from 69 to 89 percent, with an average of 77 percent.

Fiscal data provided by the sites confirmed that expenditures attributed to schools accounted for a large 

proportion of operational expenditures, ranging from 69 percent in three sites (Hawaii, Baltimore, and 

Los Angeles) to 89 percent in two sites (Ohio and Rhode Island), with an average of 77 percent across 

eight study sites (Exhibit 6). 

Exhibit 6. Share of operational expenditures attributed to schools, 2011–12

Exhibit reads: In Hawaii, 69 percent of operational expenditures were attributed to schools.

a Data for Ohio are from the 2013–14 school year. 

Note: Detail on operational expenditure attributed to schools is provided in Exhibit B1 in Appendix B.

Source: Site fiscal and accounting information systems.

School-level personnel expenditures accounted for over half of total spending in each study site, ranging 

from 59 to 77 percent of operational expenditures. School-level non-personnel expenditures accounted 

for an additional 7 to 17 percent of operational expenditures. District-level personnel expenditures also 

accounted for 7 to 17 percent of operational spending, depending on the site, and district-level nonpersonnel expenditures accounted for 4 to 20 percent of operational spending (Exhibit 7).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 16 

Exhibit 7. Distribution of school- and district-level spending among personnel and non-personnel 

expenditure categories, 2011–12

Exhibit reads: In Hawaii, 61 percent of operational expenditures were personnel expenditures (i.e., pay and 

benefits) that were attributed to schools; 9 percent were non-personnel expenditures that were attributed to 

schools; 10 percent were district-level personnel expenditures; and 20 percent were district non-personnel 

expenditures.

a Data for Ohio are from the 2013–14 school year. 

Note: Detail on personnel and non-personnel expenditure attributed to schools is provided in Exhibit B2 in Appendix B.

Source: Site fiscal and accounting information systems. 

In seven of the eight sites, school-level personnel expenditures were at least four times higher than 

school-level non-personnel expenditures. District-level expenditures were split more evenly between 

personnel and non-personnel categories.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 17 

Study sites attributed a greater share of personnel expenditures to schools than they 

did for non-personnel expenditures (85 percent versus 53 percent, on average).

Another way to examine the comprehensiveness of school-level expenditure reporting is to calculate the 

share of personnel expenditures and the share of non-personnel expenditures attributed to schools. 

Across eight sites, the percentage of personnel expenditures attributed to schools ranged from 

78 percent in Houston to 92 percent in Rhode Island. The percentages of non-personnel expenditures 

attributed to schools covered a much wider range, varying from 27 percent in Los Angeles to 79 percent 

in Ohio (Exhibit 8).

Exhibit 8. Share of personnel and non-personnel expenditures attributed to schools, 2011–12

Exhibit reads: In Hawaii, 85 percent of personnel expenditures (i.e., pay and benefits) and 30 percent of nonpersonnel expenditures were attributed to schools.

a Data for Ohio are from the 2013–14 school year. 

Note: Detail on personnel and non-personnel expenditure attributed to schools is provided in Exhibit B2 in Appendix B.

Source: Site fiscal and accounting information systems.

In all sites, the share of non-personnel expenditures attributed to schools was less than that for 

personnel expenditures (in some cases, less than half as much). Los Angeles had the largest difference, 

with 83 percent of personnel expenditures and 27 percent of non-personnel expenditures attributed to 

schools — a difference of 56 percentage points.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 18 

School Finance Experts on Inconsistent Attribution Practices

Members of the panel of school finance experts (convened at the beginning of the study) expressed concerns 

about the variation across districts in practices related to attributing spending to schools.

One concern was the level of consistency across districts in terms of which types of spending would be 

attributed to schools. According to one panelist, “there is a lot of district discretion about how expenditures are 

coded to the school level and how they code to the central office.” This panelist used substitute teachers and 

curriculum development staff as examples of spending that could be held centrally by some districts or 

attributed to schools in others, and it was suggested that clear guidelines should be developed about what 

kinds of expenses to attribute to schools.

Acknowledging concern about inconsistent practices regarding categorization of spending, as well as practices 

of attribution to schools, another panelist suggested collecting as much raw data as possible, and having a 

central authority, such as the National Center for Education Statistics (NCES), conduct the process of classifying 

expenditures into categories and assigning dollars to schools. This panelist’s opinion was that data quality 

would be weakened if districts were asked to make a lot of decisions about how to categorize different types of 

expenditures.

Finally, experts raised concerns about inconsistent methods of attributing spending to schools. According to one 

panelist, “you need to make a clear distinction between allocations and direct expenses.” There was concern 

that for those expenses that are allocated rather than tracked directly, allocation formulas would vary 

substantially across districts and states. The panel member’s preference was to leave any expense that was 

allocated by formula at the district level, and to allow NCES or researchers to determine and use a common 

allocation formula across districts and states.

Methods for Attributing Expenditures to Schools

For this analysis, we asked officials at each of the study sites whether they directly tracked expenditures 

to individual schools or pro-rated or allocated the expenditures to schools using formulas. We also 

estimated the percentage of school-level expenditures that were attributed to schools based on tracking 

versus allocating by combining the information on the reported method of attribution for each 

expenditure category with site-reported fiscal data on the amount of expenditures in each expenditure 

category.

Study sites reported directly tracking many categories of personnel and non-personnel 

expenditures to schools.

All study sites reported directly tracking several categories of personnel spending (e.g., salaries for 

administrative staff, teachers, and instructional and pupil support staff) and non-personnel spending 

(e.g., textbooks, instructional materials, furniture, equipment, computers, and software) to schools 

(Exhibit 9). In some cases, sites reported both tracking and allocating expenditures in the same category; 

for example, teacher salaries might be tracked to schools in most cases, but allocated in cases where a 

teacher’s time was divided between more than one school.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 19 

Exhibit 9. Number of study sites that reported tracking or allocating various types of expenditures 

to schools, by expenditure type, 2011–12

Expenditure Type

Tracked at 

School 

Level 

Allocated 

to School 

Level 

Varied 

Within 

Category

Not Reported 

at School Level

Salaries for School-Level Personnel

Principals and other school administrative 

staff 9 0 0 0 

Teachers 9 0 0 0 

Instructional support staff 8 0 1 0 

Pupil support staff 8 0 1 0 

School maintenance and operations staff 5 2 2 0 

Food services personnel 7 1 1 0 

Salaries for Other Personnel

Professional development staff 7 1 1 0 

Curriculum development staff 1 4 1 3 

Transportation staff 1 5 1 2 

Security staff 4 0 2 3 

Other district staffa 3 2 2 1 

Employee Benefits

Health/group benefits 6 1 2 0 

Retirement benefitsa 5 1 2 0 

Other benefitsa 3 1 1 0 

Non-Personnel Expenditures — Services

Professional development 7 1 1 0 

Security services 4 0 2 3 

Transportation 1 5 1 2 

Insurance (e.g., liability, property) 1 1 2 5 

Utilities 3 2 3 1 

Non-Personnel Expenditures — Supplies

Textbooks and instructional materials 7 0 2 0 

Furniture and equipment 8 0 1 0 

Computers and software 8 0 1 0 

General supplies 7 0 2 0 

Food supplies 6 1 2 0 

Exhibit reads: Nine sites reported that they directly tracked salaries for principals and other school administrative 

staff to schools, and no sites reported that such expenditures were allocated to schools or varied (sometimes 

tracked, sometimes allocated).

a One or more sites reported they did not know whether or how this type of expenditure was attributed to schools.

Note: Site-by-site supporting detail for this table is provided in Exhibits A1 and A2 in Appendix A. Although Ohio did not participate in the 

survey, the fiscal data the state provided included information identifying which expenditures were tracked and which were allocated. For all 

categories in Ohio, some expenditures were allocated and others were tracked, but the percentage that was tracked varied by expenditure type 

(e.g., 91 percent of teacher pay and 51 percent of security services). For Ohio, expenditure types for which more than 90 percent of 

expenditures were tracked were identified as tracked (this included expenditures for principals and other school administrative staff and 

teachers), and expenditure types for which more than 90 percent of expenditures were allocated were identified as allocated (this included 

expenditures for transportation staff, and transportation non-personnel expenditures).

Source: Pre-interview surveys and follow-up conversations with site officials.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 20 

Across eight sites, an estimated 85 percent of the expenditures attributed to schools 

were directly tracked to schools, and 8 percent were allocated using formulas.

The percentage of expenditures that the sites reported directly tracking to schools ranged from 

66 percent in Rhode Island to 100 percent in Hawaii. Expenditures that were allocated to schools using 

formulas were distributed in proportion to school shares of various metrics such as total staff, salaries, 

or enrollment; this ranged from 0 percent in two study sites (Hawaii and Houston) to 25 percent in Ohio 

(Exhibit 10). Study sites most commonly reported allocating transportation, maintenance, curriculum 

development, utilities, and telecommunications expenditures to schools.

Exhibit 10. Distribution of school-level expenditures by attribution method, 2011–12

Exhibit reads: Hawaii reported that 100 percent of expenditures attributed to schools were tracked. 

a Data for Ohio are from the 2013–14 school year. For Ohio, the attribution method was determined based on an indicator in the fiscal data; for 

all other states, the attribution method was determined based on the reported attribution method by spending category in the pre-interview 

survey and follow-up conversations.

Note: Detail on school-level expenditures by attribution method is provided in Exhibit B3 in Appendix B.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 21 

Two of the eight study sites included in the expenditure analysis reported that the attribution method 

varied (i.e., both tracking and allocation were used) for at least one spending category (28 percent of 

school-level expenditures in Rhode Island and 4 percent in Los Angeles). Rhode Island reported that the 

attribution method for some expenditure categories varied because school districts have discretion over 

how to attribute certain types of expenditures to schools. 16 

In two of the eight study sites, the attribution method was unknown for a substantial proportion of 

school-level spending: 14 percent of spending in Baltimore and 12 percent in Houston. 17 The attribution 

method was also unknown for small percentages of school expenditures in Hawaii (0.1 percent) and Los 

Angeles (0.3 percent).

Study sites tracked school-attributed personnel expenditures at higher rates (90 

percent) than non-personnel expenditures (60 percent).

Across all sites, at least 75 percent of school-level personnel expenditures were tracked to schools, with 

four of eight sites tracking 97 percent of these expenditures or more. Only two sites directly tracked 

more than 75 percent of school-level non-personnel expenditures. For all sites, the share of school-level 

non-personnel expenditures attributed by tracking was less than the tracked share for school-level 

personnel spending. Hawaii and Hillsborough reported that high percentages of both personnel and 

non-personnel expenditures were tracked to schools. In other sites — Rhode Island, Baltimore, and 

Houston — there was a large difference between the percentage of personnel and non-personnel 

school-level spending that was reported as tracked to schools (Exhibit 11).

 

16 Two other sites also reported that their attribution method varied within some expenditure categories (Texas and Ohio), but 

the actual amount of expenditures involved was negligible. In Texas, the amount of expenditure in categories reported as 

varied was only 0.1 percent. In Ohio, although the attribution method used varied across all expenditure categories, it was 

possible using their fiscal data to determine exactly how much was directly tracked to schools and how much was allocated.

17 The attribution method was labeled as unknown for one of three reasons: (1) pre-interview survey respondents did not 

indicate whether expenditures were tracked or varied; (2) pre-interview survey respondents indicated the expenditure 

category was held centrally at the district level, but some expenditures in that category were assigned to school locations in 

the actual fiscal data; or (3) the category was not listed in the pre-interview survey and the study site did not respond to the 

study team’s follow-up efforts.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 22 

Exhibit 11. Share of school-level personnel and non-personnel spending tracked by sites, 2011–12

Exhibit reads: In Hawaii, 100 percent of school-level personnel spending and 99 percent of school-level nonpersonnel spending was tracked to schools. 

a Data for Ohio are from the 2013–14 school year. 

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 23 

Metrics used to pro-rate district-level expenditures to the school level included staff 

FTEs, student enrollment, students eligible for free or reduced-price lunch, students 

being transported (for transportation expenses), and square footage of buildings (for 

utilities and maintenance and operations expenses). 18 

Rhode Island, for example, chose to encourage districts to allocate transportation expenditures to 

schools by formula, based on the number of students riding buses at each school site. Interviewees in 

Rhode Island indicated that the decision to promote allocation of transportation expenditures (rather 

than tracking them directly) was based on cost-efficiency concerns and the inability to track 

transportation expenses accurately. (Tracking transportation expenditures such as bus driver time and 

fuel was determined not to be cost-efficient because these expenditures are a small part of overall 

district budgets and require significant staff effort to track by school.) Allocations based on bus ridership 

were also determined to be reasonably accurate.

In Ohio, districts are asked to report expenditures at the school level to the extent possible, and the 

state’s fiscal data system then employs a set of rules directing whether and how the remaining district 

expenditures (those not assigned to specific school buildings by districts) are to be allocated. The 

metrics used to allocate expenditures to the school level include enrollment, staff salaries, lunchroom 

meals served, number of students transported, and square footage of each school building.

In Hillsborough County, interviewees reported using different metrics to pro-rate different types of 

expenditures. For example, maintenance expenditures are allocated to schools on the basis of square 

footage at each school site. Many centrally purchased technology expenditures are pro-rated to schools 

based on staff FTEs, rather than tracking them directly, because state guidance instructs them to do so. 

State guidance also instructs districts in Florida to allocate telecommunications and professional 

development to schools by staff FTEs. 

Summary 

Study findings confirm some of the concerns of the expert panelists regarding inconsistent practices for 

attributing certain types of spending to schools, how they are attributed, and how they are categorized. 

In certain categories of personnel, such as teacher and school administration salaries, almost all 

expenditures were attributed to schools in all sites. In other categories, such as district services and 

transportation, most sites accounted for the large majority of expenses at the district level. For many 

spending categories, there was large variation across districts in the amount of spending attributed to 

schools. There was also substantial variation across districts in methods of attribution to schools, 

particularly for non-personnel spending. Improving the quality and usefulness of school-level 

expenditure data to enable comparisons across districts and states would require greater consistency in 

both definitions and attribution practices.

 

18 For each of the listed factors used for determining allocations, allocations are based on a school’s share of the districtwide 

total of that factor.

Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 25 

III. Reliability of School-Level Expenditure Data

This chapter examines the reliability of school-level expenditure data in two ways. First, we examine the 

consistency of reported expenditures across different data sources. Second, we examine the relative 

accuracy of allocating expenditures to schools using formulas based on numbers of students, teachers, 

and other factors. 

Consistency of Site-Provided Data With Other Sources

For this analysis, we compared the detailed expenditure data from each site with school-level 

expenditure data from the CRDC and state personnel databases, and with district-level expenditure data 

from the School District Finance Survey (F-33). Because the F-33 data reflect districtwide expenditures 

and do not currently include school-level data, we aggregated the sites’ school-level fiscal data to the 

district level for that comparison. For the five study sites consisting of a single school district (the four 

school districts and Hawaii), the consistency measure was simply the difference between the districtaggregated expenditures per pupil from the site-provided data and from the F-33.

Comparison Between Site-Provided and School District Finance Survey (F-33) 

Expenditure Data

Site-provided data on total districtwide expenditures differed from School District 

Finance Survey (F-33) data by an average of 5 percent.

Four sites showed differences of 1 percent or less, and the other four sites had differences of 6 to 

11 percent (Exhibit 12). Although the simple average of the percentage differences for the eight sites 

was 5 percent, the weighted average was less than 1 percent because the larger sites tended to show 

smaller percentage differences.

Exhibit 12. Comparison of operational expenditures from site-provided data and the School District 

Finance Survey (F-33), 2011–12

Site Site-Provided Data F-33 Data Percent Difference

Hawaii a $1,608,900,224 $1,775,553,024 9%

Ohiob $17,827,524,608 $17,832,790,016 0%

Rhode Island $2,074,596,096 $2,057,982,976 1%

Texas $38,861,967,360 $38,503,124,992 1%

Baltimore $1,173,004,416 $1,287,342,976 9%

Hillsborough $1,643,538,944 $1,639,068,032 0%

Houston $1,676,154,240 $1,874,685,056 11%

Los Angeles $7,385,582,592 $6,993,387,008 6%

Exhibit reads: In Hawaii, operational expenditures calculated from the site-provided data were $1,608,900,224; 

this was 9 percent less than the total reported in the School District Finance Survey (F-33) ($1,775,553,024). a In Hawaii, most employee benefits were not included in the site-provided data, so benefits expenditures were subtracted from both the siteprovided and School District Finance Survey (F-33) expenditure totals.

b The site-provided data for Ohio are from the 2013–14 school year, whereas the School District Finance Survey (F-33) data are from the 2011–

12 school year. In Ohio, the site-provided data did not include Education Service Centers (ESCs), which are regional education support centers 

that often serve multiple districts. Because ESC spending was not included in the site-provided data, we also excluded it from the School District 

Finance Survey (F-33) total.

Sources: Site fiscal and accounting information systems and the School District Finance Survey (F-33).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 26 

Comparisons between the site-reported school-level expenditures and School District 

Finance Survey (F-33) data showed a relatively high degree of consistency for salary 

expenditures (2 percent different on average), but non-personnel expenditure data 

were much less consistent (21 percent).

For salary expenditures, the difference between the site-provided data and School District Finance 

Survey (F-33) data ranged from less than 1 percent in three sites to 4 percent in two sites. For nonpersonnel expenditures this difference ranged from 6 percent to 62 percent. In each of the sites, the 

magnitude of the difference between the two data sources was at least twice as large for non-personnel 

expenditures as for salary expenditures (Exhibit 13).

Exhibit 13. Percentage difference between site-provided data and School District Finance Survey

(F-33) data, for total salary expenditures and non-personnel expenditures, 2011–12

Exhibit reads: In Hawaii, the difference between the site-provided data and School District Finance Survey (F-33) 

data was 4 percent for salary expenditures and 19 percent for non-personnel expenditures.

a The site-provided data for Ohio are from the 2013–14 school year, whereas the School District Finance Survey (F-33) data are from the 2011–

12 school year. The site-provided data from Ohio did not include Education Service Centers (ESCs), which are regional education support 

centers often serving multiple districts. Because ESC spending was not included in the site-provided data, we also excluded ESC spending from 

the School District Finance Survey (F-33) total salaries and non-personnel expenditures.

Note: For state sites, district totals in the School District Finance Survey (F-33) were aggregated to the state level. 

Sources: Site fiscal and accounting information systems and the School District Finance Survey (F-33).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 27 

Comparison Between Site-Provided and Civil Rights Data Collection Expenditure Data

For this analysis, we calculated the percentage difference in spending between the site-provided data 

and CRDC data for each individual school within a study site, and averaged the magnitude of those 

differences across all of the schools for the study site. 

Comparisons with CRDC data found that school-level salary expenditures based on 

site-reported data were much closer to the CRDC data than non-personnel 

expenditures (differing by an average of 12 percent for salary expenditures, compared 

to 129 percent for non-personnel expenditures).

The differences among the seven sites included in this analysis also varied considerably between 

personnel and non-personnel expenditures. The range was narrower for personnel expenditures, from 7 

to 16 percent, than for non-personnel expenditures, which ranged from 34 to 261 percent. In three 

sites, the difference for non-personnel expenditures was more than 190 percent (Exhibit 14).

Exhibit 14. Average percentage differences between site-provided data and Civil Rights Data 

Collection (CRDC) data on schools’ total salary expenditures and non-personnel 

expenditures, 2011–12

Exhibit reads: In Hawaii, the average difference between the site-provided data and CRDC data was 9 percent for 

salary expenditures per pupil but 83 percent for non-personnel expenditures per pupil.

Sources: Site accounting and student information systems and Civil Rights Data Collection. 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 28 

School Finance Experts on Non-Personnel Spending

In the panel meeting, the education finance experts pointed out that non-personnel expenditures are often 

more difficult to track to schools, are often difficult to categorize, and are often problematic for the purposes of 

equity comparisons. The following examples from the panel show some of the difficulties associated with 

attribution of non-personnel expenditures to schools and using non-personnel expenditures in making 

determinations regarding equity:

• Telecommunications and networking are often covered by centralized districtwide contracts. It is difficult 

to determine how to allocate expenses across schools, and even more difficult to assign expenses to 

functional areas, such as administration or instruction.

• Textbooks and technology equipment are often subject to irregularity (lumpiness) due to the fact that 

books and equipment are often used for several years. Some schools might have high technology spending 

one year and low the next, making it difficult to compare across schools. Furthermore, because some nonpersonnel resources are used over multiple years, they are sometimes classified as capital expenditures 

rather than operational expenditures and therefore excluded from resource equity determinations.

• Transportation and utility expenditures are often affected by uncontrollable aspects of schools, such as the 

age of the facilities (for utilities) and the size of school enrollment catchments and whether districts have 

school choice programs (for transportation). 

• Contracted services are an important, but difficult, category to track to schools. Some schools contract out 

services that other schools provide in-house; therefore, it is important to be able to attribute contracted 

services to schools. However, it is difficult to know what is being provided by contracted services and 

categorize them accordingly. Furthermore, contracts are often brokered at the district or even state level, 

which makes it more difficult to track spending from contracts to schools.

The difficulty of tracking, lumpiness, and uncertainty regarding categorization of certain non-personnel 

spending categories potentially explains why non-personnel expenditures are less reliably reported across data 

sources than personnel expenditures. 

In five out of seven study sites, the site-provided data and CRDC data on school salary 

expenditures differed by less than 10 percent for the majority of the site’s schools.

Another way of examining consistency between site-provided and CRDC data on school-level 

expenditures is to look at how individual school differences between the two data sources are 

distributed in each site. Distributions, as opposed to averages, show how often differences within 

certain ranges occurred. For this analysis we defined small differences as less than 10 percent, medium 

differences as between 10 and 25 percent, and large differences as greater than 25 percent. 

For salary expenditures, in five of seven sites included in this analysis, there were small differences 

between the sites accounting system and CRDC data for the majority of schools; the two remaining sites 

had small differences in 35 and 47 percent of their schools. The share of schools with large differences 

between the site-provided and CRDC data on total salaries ranged from 0 percent in two study sites to 

21 percent in Texas. Despite Texas having the most schools with large differences, Houston had no 

schools with large differences, and 89 percent of Houston schools had small differences, indicating that 

there is heterogeneity across Texas districts (Exhibit 15).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 29 

Exhibit 15. Percentage of study site schools with small, medium, and large differences between the 

site-provided data and Civil Rights Data Collection (CRDC) data on salary spending per 

pupil, 2011–12

Exhibit reads: In Hawaii, the differences in school-level per-pupil total salary spending between the site accounting 

system data and CRDC were distributed as follows: 69 percent of schools had small differences, 31 percent of 

schools had medium differences, and 0 percent of schools had large differences.

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the CRDC spending figure; medium differences are between 10 

and 25 percent; and large differences are greater than 25 percent.

Sources: Site fiscal and accounting information systems and Civil Rights Data Collection.

Site-provided and CRDC data on school non-personnel expenditures differed by more 

than 25 percent for the majority of schools in each of the seven sites.

When examining the distribution of differences in non-personnel spending between site-provided and 

CRDC data across schools, in all of the sites, at least 71 percent of schools had large differences; in 

Hillsborough, all of the schools had large differences (see Exhibit 16).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 30 

Exhibit 16. Percentage of study site schools with small, medium, and large differences between siteprovided data and Civil Rights Data Collection (CRDC) data on non-personnel 

expenditures per pupil, 2011–12

Exhibit reads: In Hawaii, the differences in school-level per-pupil non-personnel spending between the site 

accounting system data and CRDC were distributed as follows: 13 percent of schools had small differences, 

6 percent of schools had medium differences, and 80 percent of schools had large differences. 

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the CRDC spending figure; medium differences are between 10 

and 25 percent; and large differences are greater than 25 percent.

Sources: Site fiscal and accounting information systems and Civil Rights Data Collection.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 31 

Comparison Between Site-Provided and State Personnel Database Salary Data

Finally, we examined consistency across data sources by comparing site-provided salary data with salary 

data collected from states’ education personnel databases, which house various data on the 

qualifications and salaries of all public school employees in the state, for the three sites that provided 

this information (Hawaii, Texas, and Houston). This comparison, like the above analyses with CRDC data, 

examines differences across schools within the study sites. While we would expect salary data from the 

personnel databases and that from the sites fiscal files to be the same, when asked whether personnel 

data and expenditure data were connected in any way, only two study sites (both districts — Baltimore 

and Hillsborough) indicated that their personnel and expenditure data systems were directly linked. All 

other sites indicated that the personnel databases did not inform expenditure databases, indicating 

there could be differences in records across the two data sets. 

The average difference in total salaries between the site-provided and state personnel 

data ranged from 5 percent in Hawaii to 16 percent in Houston.

This range is comparable to that between site-provided and CRDC data. When only instructional salaries 

were included, the average difference increased slightly in Hawaii to 6 percent, but substantially 

decreased in Texas (6 percent) and Houston (10 percent) (Exhibit 17).

Exhibit 17. Average percent difference between the site-provided data and state personnel database 

data on total salaries and instructional salaries, 2011–12

Exhibit reads: In Hawaii, the average magnitude of the difference between the site-provided data on school-level 

total salary expenditures per pupil and data from the state personnel database was 5 percent; the difference for 

instructional salaries was 6 percent.

Note: Hawaii’s data are for base salaries only and exclude any stipends or additional pay.

Sources: Site fiscal and accounting information systems and state personnel databases.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 32 

In Hawaii and Texas, there was a small difference (less than 10 percent) between siteprovided and state-personnel data on school salary expenditures for the majority of 

schools, but in Houston there was a medium-sized difference (10 to 25 percent) for 

the majority of schools.

When examining the distribution of differences in total salaries between site-provided and state 

personnel data, 91 percent of schools in Hawaii had only small differences, and no schools had large 

differences. In Texas, 72 percent of schools had small differences, and fewer than 3 percent had large 

differences. In Houston, however, only 12 percent of schools had small differences, and the vast 

majority (80 percent) of schools had medium differences. This high percentage of schools with mediumsized differences indicates that there is some systematic difference between the salaries being reported 

by Houston and those reported in the state personnel database. Additionally, the higher proportion of 

these medium-sized differences in Houston relative to Texas as a whole suggests that the discrepancy in 

Houston is uncommon in other districts in Texas. In both Houston and Texas, the higher proportion of 

schools with only small differences in the data when considering only instructional salaries indicates that 

the differences between data sources are substantially larger for salaries of non-instructional positions 

(Exhibits 18 and 19).

Exhibit 18. Percentage of study site schools with small, medium, and large differences between the 

site-provided data and state personnel database data on total salary expenditures per 

pupil, 2011–12

Exhibit reads: In Hawaii, the differences in reported school-level per-pupil total salary spending between the site 

accounting system data and the state personnel database were distributed as follows: 91 percent of schools had 

small differences, 9 percent of schools had medium differences, and 0 percent of schools had large differences. 

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the CRDC spending figure; medium differences are between 10 

and 25 percent; and large differences are greater than 25 percent.

Sources: Site fiscal and accounting information systems and state personnel databases.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 33 

Exhibit 19. Percentage of study site schools with small, medium, and large differences between the 

site-provided data and state personnel database data on instructional salaries per pupil, 

2011–12

Exhibit reads: In Hawaii, the differences in reported school-level per-pupil instructional salary spending between 

the site accounting system data and that from the state personnel database were distributed as follows: 

81 percent of schools had small differences, 18 percent of schools had medium differences, and less than 

3 percent of schools had large differences. 

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the CRDC spending figure; medium differences are between 10 

and 25 percent; and large differences are greater than 25 percent.

Sources: Site fiscal and accounting information systems and state personnel databases.

Accuracy of Attributing Expenditures to Schools Through Allocation Formulas

As discussed in Chapter II, some states and districts attribute some types of expenditures to individual 

schools through formulas that allocate expenditures post-hoc to schools in proportion to staff FTEs, 

student enrollment, or other metrics rather than track these expenditures directly to school sites. 

However, this approach may be less accurate than directly tracking spending to individual schools. The 

analysis described in this section assesses the relative accuracy of pro-rating through formulas 

compared with directly tracking expenditures at the school level. To do this, we applied commonly used 

allocation factors — school shares of districtwide enrollment, total salaries, or number of teachers — to 

certain expenditure categories (instructional support, pupil support, retirement benefits, and health 

benefits) to simulate the result of allocating these expenditure types in study sites that actually tracked 

them to individual schools. We then compared the simulated allocation amounts with the actual tracked 

expenditures for those sites. The individual school percent differences were then averaged across sites 

to provide the average difference between the actual reported spending and simulated allocated 

spending for each site.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 34 

Allocating expenditures to schools by formula appeared relatively accurate for health 

benefits and less accurate for pension benefits, pupil support staff, and instructional 

support staff.

Exhibit 20 shows the average differences across sites between actual reported expenditures and 

simulated allocations for each spending category and allocation formula. 19 Using formulas to allocate 

expenditures to the school level appeared to be more accurate for health benefits and retirement 

benefits than for instructional support and pupil support. When allocating expenditures to schools by 

pro-rating based on their shares of total district FTEs, the average differences between actual tracked 

expenditures and simulated allocations were 12 percent for health benefits, 18 percent for retirement 

benefits, 29 percent for pupil support, and 51 percent for instructional support. However, the relative 

accuracy of allocating expenditures using formulas varied by expenditure category and allocation 

method. Allocations of health and retirement benefits were more accurate when distributed according 

to shares of total salaries rather than shares of FTEs. Allocations of pupil and instructional support 

spending were more accurate when using school shares of FTEs than shares of student enrollment.

Exhibit 20. Average difference between actual tracked expenditures and simulated allocations, 

across eight sites

Full-Time Equivalent (FTE) Staff Salary Enrollment 

Health benefits 12% 10%

Retirement benefits 18% 14%

Pupil support 29% 33%

Instructional support 51% 53%

Exhibit reads: For health benefits, the average difference across sites between actual tracked expenditures and 

simulated allocations was 12 percent when using FTEs to simulate allocations and 10 percent when using salaries.

Note: Averages for health and retirement benefits are based on only six sites. Baltimore was excluded because they reported allocating health 

and retirement benefits. Hawaii was excluded because health and retirement benefits are not administered by the state’s education 

department.

Source: Site fiscal and accounting information systems.

Instructional Support Allocations

For instructional support, the average percent difference between actual reported expenditures and 

simulated allocations based on school shares of total enrollment ranged from 25 percent in Texas to 

108 percent in Houston (Exhibit 21). Across the eight sites, the average difference was 53 percent.

 

19 The average difference across sites was calculated as the simple average of the site-specific average percent difference 

between actual tracked expenditures and simulated allocations. We used a simple average rather than a weighted average in 

order to reflect tendencies across all of the study sites rather than primarily those in the largest sites.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 35 

Exhibit 21. Average percent difference between actual tracked expenditures for instructional 

support and allocations that were simulated based on enrollment and full-time 

equivalent (FTE) staff, 2011–12

Exhibit reads: In Hawaii, the average difference between tracked expenditures and simulated allocations for 

instructional support was 64 percent when allocated by enrollment and 60 percent when allocated by FTEs.

a Data for Ohio are from the 2013–14 school year.

Source: Site fiscal and accounting information systems.

Average percent differences in instructional support between tracked expenditures and simulated 

allocations were slightly smaller when using FTEs as the allocating mechanism, with an average 

difference of 51 percent across sites. However, for all sites, the simulated instructional support 

expenditures were similar to each other whether using FTEs or enrollment, with differences of no more 

than 6 percentage points between the two allocation approaches.

Looking at the distribution of school-by-school differences between actual reported expenditures and 

simulated allocations, most sites showed relatively large differences of 25 percent or more for a majority of 

their schools, for simulations based on both enrollment and FTEs (see Exhibits C1 and C2 in Appendix C). 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 36 

Pupil Support Allocations

Allocating by enrollment and averaging across sites, the difference between tracked expenditures and 

simulated allocations for pupil support was 33 percent. The range, from 23 to 44 percent, was narrower 

here for pupil support than for instructional support. As with instructional support, allocations were 

more accurate when based on FTEs versus enrollment: In all eight sites, the average difference between 

actual expenditures and simulated allocations was 1 to 6 percentage points less based on FTEs 

(Exhibit 22). 

Exhibit 22. Average difference between actual tracked expenditures for pupil support and 

allocations that were simulated based on enrollment and full-time equivalent (FTE) staff, 

2011–12

Exhibit reads: In Hawaii, the average difference between tracked expenditures and simulated allocations for pupil 

support was 32 percent of the simulated allocation figure when using enrollment as the allocating mechanism and 

29 percent when using FTEs as the allocation mechanism.

a Data for Ohio are from the 2013–14 school year.

Source: Site fiscal and accounting information systems.

Looking at the distribution of school-level differences between actual expenditures for pupil support and 

simulated allocations using enrollment, more than a third of schools at each site had large differences, and 

30 percent or fewer had small differences (see Exhibit C3 in Appendix C). Allocations using FTEs generated 

only slightly smaller differences than allocations using enrollment (see Exhibit C4 in Appendix C). 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 37 

Retirement Benefit Allocations

When using FTEs to allocate dollars and averaging across sites, the difference between actual reported 

expenditures for retirement benefits and simulated allocations was 18 percent. Across all sites, the 

difference between actual reported expenditures and simulated allocations was reduced by 2 to 

8 percentage points when allocations were based on salaries rather than FTEs, indicating that salarybased allocations offered greater accuracy (Exhibit 23).

Although simulated allocations of retirement benefits were, on average, more accurate than allocations 

for pupil support and instructional support, there was substantial variation in accuracy across sites. For 

example, in five of the six study sites included in the analysis the difference between actual reported 

retirement benefit expenditures and simulated allocations based on salaries was not more than 

14 percent, with a low of 4 percent. However, in Houston the difference was 41 percent. The difference 

between allocation-simulated spending and actual reported spending in Houston indicates the 

unreliability of allocations, suggesting that tracking may be preferable.

Differences in retirement benefit spending between tracked expenditures and simulated allocations 

strengthen the finding that allocations based on share of salaries were more accurate than those using 

FTEs, and that simulated allocations in Houston were less accurate than those in the other sites. For 

several sites, there was a marked increase in the share of schools with small differences between 

tracked expenditures and simulated allocations when salaries were the allocating factor, rather than 

FTEs. In Rhode Island, Ohio, Hillsborough, and Los Angeles, the increase in the share of schools with 

small differences where salaries were the allocating factor (rather than FTEs) was between 16 and 

27 percentage points (see Exhibits C5 and C6 in Appendix C).

When using schools’ shares of salaries to simulate allocations of expenditures on retirement benefits, 

the majority of schools in five of the six study sites (56 to 95 percent) had small differences between 

tracked expenditures and simulated allocations. For Houston, only 21 percent of schools had small 

differences and 52 percent of schools had large differences. In no other site did more than 16 percent of 

schools have large differences (see Exhibit C6 in Appendix C).
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 38 

Exhibit 23. Average difference between actual tracked expenditures for retirement benefits and 

allocations that were simulated based on full-time equivalent (FTE) staff and staff 

salaries, 2011–12

Exhibit reads: In Rhode Island, the average difference between tracked expenditures and simulated allocations for 

retirement benefits was 8 percent of the simulated allocation figure when using FTEs as the allocating mechanism 

and 5 percent when using salaries as the allocation mechanism.

a Data for Ohio are from the 2013–14 school year. 

Source: Site fiscal and accounting information systems.

Health Benefit Allocations

Using school shares of districtwide FTEs as the allocation mechanism, the average of the site-specific 

average differences between tracked expenditures and simulated allocations was 12 percent. As with 

retirement benefits, using school shares of total salaries instead to allocate health benefits reduced the 

difference between tracked expenditures and simulated allocations across all study sites by 2 to 

6 percentage points, indicating that allocations based on shares of salary expenditures were more 

accurate. For simulated allocations based on salary expenditures, the average difference between actual 

tracked expenditures and simulated allocations ranged from 7 percent to 19 percent across sites 

(Exhibit 24) — a narrower spread than for retirement benefits — with an overall average difference of 

10 percent across the six sites.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 39 

Exhibit 24. Average difference between actual tracked expenditures for health benefits and 

allocations that were simulated based on full-time equivalent (FTE) staff and staff 

salaries, 2011–12

Exhibit reads: In Rhode Island, the average difference between tracked expenditures and simulated allocations for 

health benefits was 13 percent of the allocation-simulated figure when using FTEs as the allocating mechanism and 

11 percent when using salaries as the allocation mechanism.

a Data for Ohio are from the 2013–14 school year. 

Source: Site fiscal and accounting information systems.

When examining differences in health benefit spending between tracked expenditures and simulated 

allocations, we found that there was an increase across all sites in the share of schools with small 

differences when salaries rather than FTEs were used to simulate allocations. Increases ranged from 6 to 

22 percentage points (see Exhibits C7 and C8 in Appendix C).

When using allocations that were simulated using share of salaries, the majority of schools in five of six 

sites (65 to 75 percent) had small differences between actual reported and allocation-simulated 

spending. Only Ohio did not have a majority of schools with small differences (42 percent). No study site 

had more than 21 percent of schools with large differences, and four of six study sites had less than 

3 percent of schools with large differences (see Exhibit C8 in Appendix C). 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 40 

Summary 

In this chapter, we presented analyses examining the reliability of school-level fiscal data in the state 

and district study sites that maintain systems for collecting and reporting such data. By examining the 

consistency of school-level data from sites’ accounting systems with expenditure figures reported in 

other sources (School District Finance Survey [F-33], CRDC, and state personnel systems), it was clear 

that expenditures on personnel — namely salaries — were much more consistently reported than nonpersonnel expenditures. The panel of experts provided insights as to why non-personnel expenditures 

might be reported less consistently, highlighting the complexity of tracking certain types of nonpersonnel expenditures to schools, the difficulties associated with classification decisions, and irregular 

spending patterns. The inconsistency of non-personnel spending data indicates that we should question 

the trustworthiness of such data and exercise caution when using it to make comparisons for the 

purposes of equity or for analyzing efficiency. 20 Some experts on the panel also felt that it may not be 

advisable to use all categories of non-personnel expenditures — particularly those that are noninstructional and largely uncontrollable — to evaluate equity.

We also observed that allocation-simulated expenditures can differ quite substantially from actual 

reported spending, suggesting that allocations can be quite inaccurate. There was substantial variation 

in the accuracy of allocations across categories, across study sites, and across allocation methods, with 

allocations being quite accurate in some instances and very inaccurate in others. This makes it difficult 

or impossible to determine the accuracy of allocations used to report spending. Due to uncertainty 

regarding the accuracy of allocations, states and districts should be encouraged to track as much schoolattributed spending as possible, particularly for categories accounting for large shares of overall 

spending. Furthermore, the results from analyses of school-level expenditure data where large amounts 

are allocated (rather than tracked) to schools should be interpreted cautiously, keeping in mind the 

potential unreliability of the data.

 

20 Because the CRDC and School District Finance Survey aggregate spending into broad categories, we cannot make statements 

regarding the consistency of more refined expenditure types within non-personnel or limit this analysis just to those 

categories of non-personnel that sites report as being allocated to schools.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 41 

IV. Developing and Implementing Systems to Collect 

School-Level Expenditure Data 

To generate useful information on school spending, states and districts should establish systems and 

procedures that support the collection and reporting of comprehensive and accurate expenditure data at the 

school level. This chapter describes the case study states’ and districts’ experiences establishing school-level 

expenditure reporting processes in their states or districts and using the resulting data, to provide context 

and advice for other states or districts considering establishing similar systems of their own. 

Because school-level expenditure reporting systems often rely on staff at multiple levels of the school 

system to carry out data collection procedures, the panel of experts convened for this study — particularly 

the practitioner representatives — emphasized the importance of motivating and providing support for 

district and school staff to implement and use the system as intended. The expert panel members 

recommended creating incentives for district and school staff members to become invested in collecting 

high-quality expenditure data — for example, by explaining to them how the data will be used and by 

making the data widely available. The panel also underscored the importance of providing clear guidelines, 

definitions, training, and technical assistance to system users to ensure they implement data collection 

procedures consistently. The experts noted that training system users will be an iterative process; new 

needs will be discovered, requiring revisions to the original guidelines, definitions, and support. 

Drawing on data collected from interviews and pre-interview surveys with officials from eight of the nine 

study sites, 21 this chapter explores the steps that these states and districts took to develop or enhance 

their systems for collecting and reporting school-level expenditure data. Specifically, it examines the 

history and development of these systems, tasks and costs involved, and challenges experienced. 

Development and Expansion of School Expenditure Data Systems

Most of the sites reported that they had been collecting school-level expenditure data 

for two or more decades, and most reported making significant improvements to 

these systems within the past 10 years.

Officials from the eight study sites described varied backgrounds regarding their collection and reporting 

of school-level expenditure data (Exhibit 25). Baltimore reportedly began tracking school-level 

expenditures relatively recently, in 2007, while officials from the other seven sites indicated that they 

have tracked some form of school-level expenditure data for over two decades (Exhibit 25). Five of these 

seven sites reported making significant advances in their capacity to collect school expenditure data 

within the past 10 years.

 

21 Ohio did not participate in the interviews and pre-interview survey used to collect information about sites’ experiences 

developing and implementing systems for collecting and reporting school-level expenditure data. Accordingly, Ohio is 

excluded from all of the analyses in this chapter.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 42 

Exhibit 25. Study sites’ development of school expenditure data systems

Site 

Year SchoolLevel Data 

First Collected Milestones 

Origin of Current 

Data System 

Florida 1974 2012–13: Began transferring from a mainframebased cost reporting system to a Web-based systemDeveloped internally

Hawaii More than 20 

years ago

1991: Began using integrated financial management 

system

Purchased from 

vendor

Ohio 1991 1991–92: System piloted

1994–95: First mandatory statewide data collection

Rhode Island 1990 1997: Purchased software to annually collect schoollevel expenditure data

2009: Instituted a new data system and a new 

uniform chart of accounts

Developed internally 

with support from 

vendor

Texas 1988 1991: Began collecting school expenditure data 

through statewide data system

2003: Transferred state data system from 

mainframe to Web-based platform

Developed internally

Baltimore 2007 2007: Implemented new “Principal Dashboard” 

system that facilitated collection and reporting of 

school-level fiscal data

Purchased from 

vendor

Hillsborough Mid-1970s 1994: Implemented software system that could 

track salaries to school level

Late 1990s: Replaced existing data system with new 

software due to Y2K concerns 

Purchased from 

vendor, with 

customizations 

Houston More than 20 

years ago

1999: Implemented new data system and reporting 

guide 

2016: Plans to implement add-on software module 

to support tracking of additional expenses (e.g., 

building repairs, equipment) to schools

Purchased from 

vendor

Los Angeles More than 20 

years ago

2007–08: Replaced outdated expenditure tracking 

software with a new system that provides more 

detailed tracking of expenditures

Purchased from 

vendor, with 

customizations 

Note: Ohio is included here even though the site was not able to participate in interviews. Information was obtained from Sherman, Best, and 

Luskin (1996) or provided by the state department of education official who provided data for analysis. “More than 20 years ago” indicates that 

sites have been reporting expenditures at the school level as long as anyone available for interviews could remember. 

Source: Interviews and pre-interview surveys of site officials, spring 2014.

Officials from all four state sites said their data system was initiated or expanded in 

response to state laws intended to promote equity and transparency in school spending.

Florida officials traced their state’s collection of school-level expenditure data to a 1973 law enacted by the 

state legislature (F.S., 1011.62, establishing the Florida Education Finance Program), which required the 

collection of school- and program-level expenditure data to ensure that all students in the state have 

comparable access to educational programs and services. According to one official, the law came about as 

state legislators were hearing reports from school board members that parents were concerned about their 

schools “not getting their share”; the legislation was introduced to address these concerns and improve equity. 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 43 

Texas collects and reports school expenditure data through the state’s Public Education Information 

Management System (PEIMS), which stemmed from state legislation passed in 1984 to increase 

transparency and accountability for educational programs (Texas House Bill 72). According to state 

officials, Texas Education Agency staff developed PEIMS in 1989 and further developed the system in 

1991 to collect school expenditure data, in response to the 1984 law.

In Hawaii, state officials indicated that a 1986 law granting the SEA administrative flexibility in budgeting 

and fiscal operations facilitated the state’s move to computerize its fiscal data collection. This flexibility 

allowed the SEA to manage all aspects of its fiscal operations, with the exception of payroll, which was 

outside the state's centralized accounting system. In 2004, the Hawaii State Legislature enacted a 

Weighted Student Formula policy, which established a student needs-based formula for distributing 

funds to schools and gave principals more authority over spending decisions. These policy changes 

required or facilitated school-level expenditure reporting.

More recently, in 2009, Rhode Island embarked on an initiative to improve the information it had 

available on school-level expenditures. Although the state has annually collected school expenditure 

data since 1990, prior to 2009, districts and schools varied in how they accounted for expenditures, and 

this variation precluded the SEA from answering important policy questions for the Rhode Island 

General Assembly. In 2006, the General Assembly charged the SEA and the Office of the Auditor General 

with developing and implementing a statewide uniform chart of accounts to create more consistency in 

expenditure reporting.

Officials from three of the five district sites cited giving schools more authority over 

spending decisions as a major reason for collecting school-level expenditure data.

A Houston official noted that the district’s movement toward decentralization over the past two 

decades — which had given schools more autonomy over their budgets — led to a greater need for 

district and school staff to monitor spending against those budgets. In addition, although Texas requires 

all districts to collect and report school expenditure data in the PEIMS system, the Houston Independent 

School District decided to track expenditures at an even more granular level than the state required in 

order to examine program effectiveness. In 2013–14, the district decided to add to the chart of accounts 

sub-object codes within object codes, representing each academic subject (for example, a sub-object 

code of 10 refers to math), in response to stakeholder questions about spending by academic subject 

and the expenditures associated with programs that were showing student achievement results. 

Los Angeles officials reported that the district has always reported expenditures at the school level in 

some manner, but had implemented a new expenditure tracking system in 2007–08 that facilitated 

more specific expenditure tracking. The district purchased this new system because its previous 

software system was out of date, could no longer be updated, and was prone to crashing. In 2013–14 

this school-level expenditure data was used often when Los Angeles instituted a new Budgeting for 

Student Achievement policy, which “pushed out” a greater share of funds to schools and aimed to give 

principals more flexibility and control over resource allocation decisions in exchange for stronger 

accountability for results. 

Baltimore began implementing a Fair Student Funding policy in 2008–09, which, like in Los Angeles, 

transferred significantly more spending authority to schools to enable principals to make spending 

decisions based on the specific needs of their students and community. This new autonomy at the 

school level limited district management of the system, making it important to adjust expenditure data 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 44 

systems to track funding and expenditures at the school level to ensure the intended equity. This need 

motivated the development of their school-level expenditure tracking system.

In Hillsborough, respondents did not see a connection between their school expenditure data system 

and any effort to give schools more authority over spending. Instead, Hillsborough officials attributed 

the adoption of their system to a state legislative initiative in 1973 to improve equity and create more 

transparency regarding expenditures in individual schools.

Specific Tasks and Costs Involved

Officials from six sites indicated that developing their operational expenditure 

reporting system involved changes to their charts of accounts to create or revise codes 

for capturing school-level spending.

As noted earlier, Rhode Island created a statewide uniform chart of accounts in 2009 to standardize how 

all districts in the state recorded school-level expenditures. Florida’s move to school- and program-level 

reporting in 1974 also required changes to the state’s chart of accounts. Texas officials reported that the 

state has slightly modified its chart of accounts periodically to reflect new programs and funds but that 

these changes did not represent major policy or procedural changes. 

At the district level, Baltimore changed its chart of accounts when it implemented its Fair Student 

Funding policy. Los Angeles officials reported that the district augmented its chart of accounts to take 

advantage of its new software system’s capacity to handle a greater number of sub-codes allowing 

collecting expenditure data at a more granular level. Houston officials reported that the district changed 

its chart of accounts to allow cost-effectiveness analyses based on new academic sub-object codes.

All states and districts had needed hardware and software changes to capture or 

report the new data they sought. 

Site officials described making various hardware and software changes to expand their capacity to 

attribute expenditures to schools. For example, Texas used a mainframe computer system for its PEIMS 

through the 1990s but migrated to a Web-based application in 2003 when its previous data system 

reached capacity. Officials from both Houston and Los Angeles reported purchasing new software from 

SAP to increase data storage capacity and allow more specific expenditure tracking. Houston officials 

also mentioned plans to purchase an add-on SAP module for their system to collect additional data on 

building repair and equipment expenses. In Hillsborough, the district’s shift to a new software system in 

the late 1990s also required additional hardware because the software was incompatible with their 

existing servers and computers. 

Personnel costs associated with developing new data systems included time for staff to 

select or design the new system, plan its roll-out, and provide training to system users.

Officials from all six sites that provided information on the costs associated with developing their 

current system 22 reported incurring costs for staff time spent choosing or designing the system. 

Personnel costs also included staff time devoted to planning system roll-out strategies and developing 

 

22 Analyses of the costs incurred and staff involved in developing systems for collecting school expenditure data exclude Hawaii 

and Texas because the development of their systems occurred more than 20 years ago, and the state officials who were 

interviewed for this study did not have this information.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 45 

training materials. For example, to implement the new system, SEA staff in Rhode Island relied on a 

group of district administrators who served as part of a statewide workgroup (see box, “Rhode Island’s 

Efforts to Engage Stakeholders During System Design and Implementation”). Internal SEA staff 

developed the new data system and then negotiated with vendors for district-level systems. Many 

information technology staff also were involved in setting up the new systems. 

In Los Angeles, directors from all relevant departments within the central office (including accounting, 

payroll, benefits, projects, accounts payable, procurement, and many others) were involved in the 

selection process for the new expenditure reporting system. After the district’s SAP system was 

selected, about 80 to 90 district employees — including information technology staff — helped support 

district-wide implementation.

Non-personnel costs associated with developing school expenditure data systems 

included contracts with vendors or consultants (six sites) and technology upgrades 

(four sites).

As shown above in Exhibit 25, five study sites reported purchasing their expenditure data system from 

an outside vendor, and another site (Rhode Island) collaborated with a system vendor when developing 

a data system internally. Several sites established contracts with consultants or organizations other than 

the system vendor to support the design or implementation of their new system. For example, Florida 

officials worked with representatives from Orange County Schools and later a small business to develop 

their data system internally. Los Angeles officials reported contracting with 80 to 90 consultants to 

support central office personnel in creating and rolling out the district’s new system. For example, one 

individual consultant helped develop the system, which took about a year. Officials from four sites also 

described hardware or software upgrades as key non-personnel expenditures related to developing 

their data systems.

Estimates of the total amount of non-personnel costs involved in developing new data systems varied 

across sites. Rhode Island officials reported investing approximately $2.2 million in helping districts 

replace or modify their accounting software systems. Officials in Los Angeles estimated that their SAP 

system cost more than $200 million to develop, and the district paid this amount “across five or six 

years” using mostly capital funds from the district’s general fund. Most of this cost was for the software, 

the vendor (contractor labor), licenses, and the development of training materials. At four sites, 

interviewees reported that they incurred technology costs to upgrade system hardware or software, 

such as new servers and software.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 46 

Rhode Island’s Efforts to Engage Stakeholders During System Design and Implementation

After receiving a mandate from the Rhode Island General Assembly in 2006 to develop a statewide Uniform 

Chart of Accounts, a state policy workgroup surveyed other states to find out what systems already existed and 

what their capabilities were. They looked closely into what effort it would take and what it would cost to 

develop a system for Rhode Island. The group also surveyed all stakeholders — including principals, other 

school staff, district staff, General Assembly members, and the governor’s office — to find out what they were 

looking for in a new school expenditure tracking and reporting system. The group heard from stakeholders that 

they were interested in answering questions about return on investment — how they could know if the money 

they were spending was being well spent. 

A Rhode Island official noted that a key to the successful implementation of their Uniform Chart of Accounts 

was the involvement of the school business managers from the development stage, including a pilot group of 17 

district business managers who provided input on the design, codes, and code descriptions. This involvement 

gave this set of districts ownership of the project and helped to ensure that the remainder of the districts 

followed suit knowing that it was not a “top down” approach being forced upon them. According to the state 

official, continuing to include district input through the annual meeting format “has been very powerful.” 

In addition to start-up costs, all sites reported incurring day-to-day operating costs to 

maintain their data systems, including personnel costs for information technology and 

finance staff in their central offices, school staff in the study districts, and staff time to 

develop and provide training. 

The number of staff involved in the day-to-day operations of expenditure tracking systems and processes 

varied across sites. In Florida, the staff involved in maintaining the expenditure tracking system day to day 

include at least four people in the Office of Financial Reporting, the Assistant Deputy Commissioner of 

Finance and Operations, a programmer (0.25 FTE) who receives school district data and produces reports 

for reviewers, and two full-time program analysts who provide technical assistance to school districts, 

review data for accuracy, and analyze data when requested. In Houston, somewhat more staff are involved,

including 10 budget analysts, three senior budget analysts, and two budgeting managers who provide 

support and oversee campus budgeting. A senior manager of accounting in the school-based division 

within the Department of Budget and Financial Planning, along with five other staff, ensures federal, state, 

and local grant reporting and compliance. At the school level, approximately 40 of Houston’s large high 

schools have school business managers who handle budgeting and expenditure reporting. Overall, districts

in this study employed about one FTE staff per 10,000 to 15,000 students to maintain the systems.

Six sites reported incurring ongoing non-personnel costs such as contracts with 

external vendors or consultants (four sites) and technology upgrades (four sites) to 

maintain their school expenditure data system. 

In addition to the personnel costs associated with the site employees who operate these systems, officials 

from six sites indicated that they continue to incur non-personnel costs to maintain their data system. In four 

sites, these costs included ongoing contracts with vendors or partner organizations. For instance, Florida 

officials reported using contractor staff to work at a help desk to provide technical assistance to school and 

district staff working with the expenditure software. The state also contracts with the Northwest Regional 

Data Center — which houses the data — to run overnight reports. In another four sites, maintaining their 

data system requires technology costs, such as software licenses and server upgrades. For example, 

Hillsborough officials explained that the district needs to purchase updated licenses for their software every 

two years because their expenditure data system does not support outdated licenses.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 47 

Challenges

Site officials most commonly identified staff capacity and training needs as a major 

challenge in developing and implementing new systems for tracking school-level 

expenditures. The most commonly identified challenge that sites continued to face 

was in tracking certain types of expenditures.

Officials from all eight sites indicated on the pre-interview survey that they had experienced challenges 

regarding staff capacity and training needs (Exhibit 26). During interviews, site officials elaborated on 

these challenges. With regard to staff capacity, officials at three sites emphasized their staff’s difficulty 

reporting expenditures correctly. Providing training to address these capacity needs also proved 

difficult. For example, Los Angeles officials reported that training staff was one of their greatest 

challenges in implementing their new school expenditure data system, and they emphasized importance 

of training new staff quickly and thoroughly. Texas officials mentioned challenges ensuring consistency 

in the training provided in districts across the state. In response, state staff created standardized training 

documents to reduce the variation among trainers’ presentation of material. Texas officials also 

described how the SEA tried to create online training modules, but the amount and nature of the 

content that needed to be delivered made Web-based trainings unfeasible and unmanageable. As a 

result, the state switched to delivering training in person. 

Outdated technology and resistance from schools also were reported as challenges. State and district 

officials in the case study sites described specific challenges with outdated hardware and software 

(unsupported operating systems) in schools. Among the seven study sites that identified resistance from 

schools as a challenge, officials described initial pushback due to staff reluctance to take on new roles in 

managing budgets and spending, perceptions that the changes were unnecessary, and general 

opposition to change. In all but one study site, officials reported that the initial resistance they 

encountered eventually subsided as schools and districts became more accustomed to the new system. 

Rhode Island officials emphasized that giving district and school stakeholders a voice in the change 

process helped them to overcome opposition.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 48 

Exhibit 26. Number of study sites reporting that various issues represented a moderate or major 

challenge in implementing or changing their financial data system

Exhibit reads: Five sites indicated that staff capacity or training needs represented a moderate challenge in 

implementing or changing the financial data collection and reporting system.

Note: Two sites did not respond to the survey item regarding bureaucratic or political processes.

Source: Pre-interview survey of site officials, spring 2014 (n = 8 sites).

Additional challenges that respondents mentioned during interviews included difficulty allocating 

expenditures on itinerant staff who work at multiple schools as well as particular types of non-personnel 

expenses (such as transportation and telecommunications) to schools. For states, collecting data from 

districts that used varied accounting software systems emerged as a challenge. One state also reported 

challenges in comparing school budgets to expenditures because expenditure reporting was at a fairly 

granular level, whereas districts typically budget at a higher level. In the two study districts that were 

located within study states (Houston and Hillsborough), cross-walking local and state accounting codes 

reportedly posed a challenge. 

Site officials explained that they were able to overcome many challenges with time, as staff adjusted to 

new expectations and procedures, and with additional training. However, when asked about the challenges 

they continued to face, site officials reported ongoing challenges with tracking certain types of expenditures, 

such as staff who work in more than one school, transportation costs, and utility expenses. For example, a 

Rhode Island official reported that tracking telecommunications expenses was challenging because it 

was difficult to measure the amount of Internet time or bandwidth that was used by students, teachers, 

and administrators, and for what purposes.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 49 

Training and Quality Assurance Practices

All eight sites reported providing training on their systems for collecting school 

expenditure data, although the focus and intensity of the training varied.

At the state level, Texas and Rhode Island officials reported providing training to districts on the state’s 

chart of accounts, and Hawaii provided training on overall financial reporting procedures. In Rhode 

Island, the SEA developed a comprehensive slide deck of 200 slides for districts to use to train district 

and school stakeholders, but rather than requiring district trainers to cover the entire slide deck with 

each group of trainees, the SEA instructed districts to focus on a subset of slides that were most relevant 

for the particular group being trained. In Hawaii, the SEA created online, self-paced courses using a 

software program that presents narrated PowerPoint presentations. Each course includes several 

modules, including the roles that various entities and individuals play in operating and using the data 

system. Hawaii officials also reported that new principals are trained through the state’s New Principal 

Academy, where they receive operational as well as academic leadership training.

Officials from Florida reported that the SEA provided its most intensive training on required reporting 

procedures in 2013–14 as districts began using the state’s new Web-based data system. For example, 

the SEA held several workshops throughout the state on the system’s Program Cost Report, which 

provides various analyses of school- and program-level expenditure data. The Florida officials said the 

state also provides annual updates and guidance to local staff, including updates on any changes that 

have been made to the software system, legislative changes, new rules, and other information. 

At the district level, officials described providing training that ranged in intensity from episodic trainings 

(Baltimore) to monthly principal meetings (Hillsborough). In addition to providing in-person training 

during monthly principal meetings, Hillsborough trainers also provide training on account coding and 

financials to bookkeepers, principals, and secretaries during quarterly meetings, as well as to district 

leaders and other district staff when requested. Hillsborough officials noted that small group meetings 

were common, as staff from schools located near one another often get together to discuss issues with 

reporting. To ensure that staff turnover does not negatively impact data quality, the district focuses on 

getting as much information to staff as possible so that all schools have the same information. 

Similarly, Houston officials emphasized that “training [of school staff] is the best option for data quality.” 

They explained that the district’s Budget and Financial Management Division provides mandatory 

training on data coding procedures and use of the SAP software to all newly hired employees whose job 

responsibilities require access to the expenditure data system. The district also offers three to five 

sections of a refresher course at the beginning of the school year, depending on the number of school 

staff who need to be trained. In addition, the district has periodically provided specific training for 

school business managers, who are sometimes hired by principals in large secondary schools to assist 

with budget management. Although the district has found face-to-face training to be most effective, a 

Houston official noted that the district also developed an online training course, which includes a test 

that participants must pass to demonstrate their mastery of the information. 

In Los Angeles, the district rolled out its new software system and data collection procedures over time 

through its Education Service Centers, which act as subdistricts within the district. According to Los 

Angeles officials, the central office answers school staff members’ questions and provides additional

training and technical assistance until the school staff are fluent in and comfortable with the system. 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 50 

The central office also gives school leaders a manual that outlines the district’s accounting guidelines 

and instructions for using its SAP system software.

To monitor quality, states and districts in the study relied most heavily on staff review 

of data (seven sites) or automated error checks in their respective software systems 

(six sites) to identify errors, incorrect use of accounting codes, or implausible values. 

Though staff training was a primary way sites ensured the quality of their data, they also incorporated 

staff review of data and automated checks of values in their software systems. For example, Rhode 

Island officials described how their Office of Statewide Efficiencies reviews and cleans expenditure data 

quarterly, and how their software includes automatic checks on the validity of codes and 

appropriateness of values. Rhode Island also employs staff who provide technical assistance directly to 

sites and independent auditors who use the Uniform Chart of Accounts to identify common errors 

across districts and ask districts to correct these errors. Baltimore’s system reportedly did not have any 

automatic checks built into the software; rather, staff in the budget department review expenditure 

data throughout the year to ensure that school staff are using the correct account codes.

The states in this study differed in the extent to which they gave districts discretion in 

how they report school-level expenditures. 

For instance, although Texas districts are guided by an expenditure reporting manual and chart of 

accounts distributed by the state, districts themselves determine how they track or allocate 

expenditures to schools. Although Florida allows districts some discretion in how they attribute 

expenditures (for example, using formulas driven by number of students or number of staff), the state 

centralizes and standardizes expenditure reporting by requiring districts to use one system for reporting 

expenditures. Rhode Island is even more prescriptive: The state drives districts’ reporting of expenditures 

through an extensive set of specific accounting codes and rules for allocating central office expenditures. 

Officials from six sites noted that their personnel and expenditure data systems could 

connect to one another, a feature that may promote data reliability. 

Several sites also have system design elements in place to ensure consistency of data. For example, two 

district sites — Hillsborough and Baltimore — have personnel and expenditure tracking systems that are 

directly linked, meaning personnel records — including individuals’ salaries — directly inform the 

reporting of school-level expenditures. In four other sites, officials reported that their personnel and 

fiscal data systems do not “talk” directly to each other, but that common sets of codes for identifying 

objects and locations are used, so that data from the two systems can be merged if needed. Two other 

sites — Florida and Texas — reported that their state-level personnel and expenditure tracking systems 

are completely separate and do not link to each other at all. In theory, having data systems that are 

interconnected — or having one combined fiscal and personnel data system — should result in more 

reliably reported figures as there are fewer data points in isolation.

Use of School Expenditure Data

Study sites most commonly reported using their school-level expenditure data to 

promote transparency with the general public.

When asked about the primary ways in which their school-level expenditure data were used, officials 

from five sites highlighted how the data served to promote transparency regarding school spending 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 51 

(Exhibit 27). For example, a Los Angeles official noted how the district, which has been criticized for not 

being sufficiently transparent, has published individual school expenditure reports online so that 

parents, teachers, and the community can see how much money their school has to spend, how much 

their school is spending, and what the school is purchasing. The Los Angeles official also mentioned that, 

at the time of the interview in spring 2014, discussions were under way to make district expenditure 

information available online as well.

Exhibit 27. Number of study sites that reported using their school-level expenditure data in various 

ways

Exhibit reads: Officials from five sites indicated that their site used its school-level expenditure data to promote 

transparency with the general public.

Source: Interviews with site officials, spring 2014 (n = 8 sites).

In total, officials from five study sites reported that they shared school expenditure data on their state 

or district website to foster transparency. In Florida, where state law requires the state to provide 

parents with information on how money is spent at their child’s school, the SEA posts a series of nine 

reports on the state department website that include detailed information on the amount and types of 

expenses that each school in the state has incurred. 

Officials from six study sites described their use of school-level expenditure data to 

inform decision-making, including decisions regarding compliance with federal and 

state laws (four sites), school budgeting and spending monitoring (three sites), costeffective investments (three sites), and strategic planning (two sites).

Officials from four study sites reported using school expenditure data to determine compliance with 

federal or state laws (Exhibit 27). For instance, Florida and Rhode Island officials indicated that the state 

used school expenditure data to assess whether districts receiving Title I funds satisfied federal 

maintenance of effort requirements.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 52 

Three sites reportedly used their school expenditure data to monitor how schools are spending their funding 

and/or to plan school budgets. Officials in Los Angeles, for example, explained that expenditure data played a 

prominent role in informing school budget decisions. According to these officials, the district tracks 

expenditures to monitor progress on the budget plan or assess whether instructional goals are being met; 

this enables program staff to make decisions about any budget revisions or corrective actions needed.

Officials from three sites — Houston, Los Angeles, and Rhode Island — reported using school 

expenditure data to analyze the cost-effectiveness of investments in particular areas. Baltimore and 

Hillsborough officials noted how their school expenditure data were used to guide strategic planning 

decisions. For instance, Baltimore officials discussed how a former district leader wanted to conduct a 

“deep analysis” of spending in the district’s schools, and a Hillsborough official mentioned that the local 

school board used school expenditure data to review the district’s strategic plan.

A Hawaii official explained that SEA staff tend to focus more attention on school funding data than 

expenditure data because they were more concerned that schools were receiving adequate funding and 

less concerned with how the funds are spent. SEA staff reported that they primarily use school 

expenditure data to respond to questions from the public about what particular schools are spending. 

Texas officials reported that the primary purpose of collecting school-level expenditure data is public 

transparency. One state official noted that SEA staff did not find the school-level expenditure data to be 

particularly useful because many types of expenditures were allocated to schools by formula and 

therefore do not necessarily reflect what is actually going on at individual schools. Moreover, the official 

said the SEA does not scrutinize school-level expenditure reports because the reports are not used to 

make policy decisions. The official explained that the SEA obtained more accurate expenditure data at 

the district level, and, unlike the district reports, school-level reports did not have a wide audience.

Officials in the study sites encouraged other states and districts that wish to 

implement school expenditure data systems to get stakeholders involved, remember 

that communication is critical to success, and be mindful of long-term issues and 

needs when designing such systems. 

When asked what advice they would offer for other states and districts aiming to develop or improve 

systems for tracking expenditures at the school level, site officials recommend including a wide range of 

stakeholders at the school and district levels, and in the larger community; communicating clearly and 

frequently with all stakeholders, both in advance of and during changes to systems; and thinking about longterm needs for the future when designing software and charts of accounts for school expenditure reporting. 

For instance, Los Angeles officials advised districts to select a software system carefully to ensure it included 

the functionality and specific features the district would need to do business and answer anticipated questions.

Summary 

Study sites described varied motivations for developing systems for collecting and reporting expenditure data — 

including state legislative mandates and efforts to transfer more spending authority to schools — but the 

motivations typically represented steps to increase transparency and equity of spending. Site officials 

described the development and implementation of their systems as a multi-faceted process that required 

thoughtful approaches to designing system functionality, promoting stakeholder awareness, and providing 

sufficient personnel and resources to ensure the systems functioned as intended. The change process in each 

study site involved challenges — particularly related to staff capacity and training, technology issues, and staff 

resistance — but most sites indicated that they were able to overcome these challenges over time.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 53 

V. Conclusion

The states and districts in this study have developed accounting systems that report large shares of their 

operational expenditures at the individual school level, ranging from 69 to 89 percent, with an average 

of 77 percent. In most cases these systems have been operating for a considerable number of years, 

often two decades or more, but most sites reported efforts to continue to improve their systems over 

time. Most of the expenditures that the study sites attributed to schools were directly tracked, rather 

than allocated to schools by formula.

Comparisons between the site-reported school-level expenditures and other data sources showed a 

relatively high degree of consistency for salary expenditures. However, non-personnel expenditure 

reporting varied considerably, both within specific expenditure categories and across the sites. The 

analyses conducted for this study confirm concerns of the study’s expert panel about the 

comprehensiveness, consistency, and reliability of existing school-level data on non-personnel expenditures, 

particularly those that are not tracked directly to schools. 

Study sites reported using their school-level expenditure data to inform decision-making and promote 

transparency concerning school spending. Site officials reported experiencing a variety of challenges in 

developing and implementing these systems, particularly related to staff capacity and training needs, 

but most sites indicated that they overcame many of these challenges as staff adjusted to new 

expectations and procedures and received additional training. All sites reported working to monitor and 

improve data quality, including by providing ongoing training for staff, establishing procedures for 

reviewing the data, and using automated error checks in their software to identify implausible values 

and other errors.

When asked what advice they would offer for other states and districts aiming to develop or improve 

systems for tracking expenditures at the school level, site officials recommend involving multiple 

stakeholders; communicating clearly and frequently with those stakeholders, both in advance of and 

during changes to systems; and thinking about long-term needs for the future when designing software 

and charts of accounts for school expenditure reporting. 

The nine sites participating in this study may serve as helpful models for other states and districts 

interested in establishing systems to collect and report school-level expenditure data. 

Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 55 

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Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 57

Appendix A. Attribution Methods by Category

Exhibit A1. Methods used to attribute personnel expenditures to schools, by type of expenditure

Expenditure Type Florida Hawaii Ohioa Rhode

Island Texas Baltimore Hillsborough Houston LosAngeles

School administrative staff pay T T T T T T T T T

Teacher pay T T T T T T T T T

Instructional support pay T T V T T T T T T

Pupil support pay T T V T T T T T T

Maintenance and operations

staff pay

T T V T A T A T V

Security personnel pay T T V V T D* D T D

Transportation staff pay A D* A V T A A D A

Food service personnel pay A T V T T T T T T

Professional development pay A T V T T T T T T

Curriculum development pay A D V T A A A D D

Other district staff A T V T V T A D* U

Health/group benefits T T V V T A T T T

Retirement benefits T - V V T A T T T

Other benefits - - V T A U T U T

a For Ohio, expenditure types for which more than 90 percent was tracked were identified as tracked (this included expenditures for principals

and other school administrative staff and teachers) and expenditure types for which more than 90 percent was allocated were identified as

allocated (this included expenditures for transportation staff).

Note:

T = Expenditures were tracked.

A = Expenditures were allocated using a formula.

V = Method of attribution varied (some tracked, some allocated).

U = Method of attribution was not determined.

D = Expenditure category was not reported at the school level.

D* = Officials indicated the category was not reported at the school level, but we found small amounts of expenditures attributed to schools and the

attribution method was not determined. There were also cases where the respondent initially indicated the category was not reported at the school

level, but then later indicated an attribution method in response to follow-up questions; in those cases, the attribution method is listed.

Source: Pre-interview surveys and follow-up conversations with site officials.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 58

Exhibit A2. Methods used to attribute non-personnel expenditures to schools, by type of

expenditure

Expenditure Type Florida Hawaii Ohioa Rhode

Island Texas Baltimore Hillsborough Houston LosAngeles

Textbooks and 

instructional materials T T V V T T T T T

General supplies T T V V T T T T T

Food supplies A T V V T T T T T

Furniture and 

equipment T T V T T T T T T

Computers and software T T V T T T T T T

Transportation A D* A V T A A D* A

Professional

development A T V T T T T T T

Curriculum development A D* V T A A A D D

Other district services A T V T V T A D* D*

Purchased services and 

rentals U T V A A U T U T

Telecommunications T T V V T T A T A

Utilities V D* V V T A T T A

Insurance A D* V V T D D D D

Security T T V V T D* D T D

Miscellaneous items - T V T A U A U A

a For Ohio, expenditure types for which more than 90 percent of expenditures were allocated were identified as allocated (this included

expenditures for transportation).

Note:

T = Expenditures were tracked.

A = Expenditures were allocated using a formula.

V = Method of attribution varied (some tracked, some allocated).

U = Method of attribution was not determined.

D = Expenditure category was not reported at the school level.

D* = Officials indicated the category was not reported at the school level, but we found small amounts of expenditures attributed to schools

and the attribution method was not determined.

Source: Pre-interview surveys and follow-up conversations with site officials.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 59

Appendix B. Detailed Spending by Study Site,

Attribution Method, and Expenditure Category

Exhibit B1. Personnel and non-personnel expenditures at the district level and at the school level,

2011–12

Site District Level School Level Total

Hawaii $503,338,576 $1,140,759,120 $1,644,097,696

Ohioa $2,136,743,680 $16,455,908,608 $18,592,652,288

Rhode Island $216,822,960 $1,800,087,568 $2,016,910,528

Texas $8,856,920,064 $30,007,776,512 $38,864,696,576

Baltimore $362,664,176 $810,340,264 $1,173,004,440

Hillsborough $292,596,904 $1,350,942,112 $1,643,539,016

Houston $433,618,480 $1,242,535,680 $1,676,154,160

Los Angeles $2,312,587,648 $5,072,994,912 $7,385,582,560

a Data for Ohio are from the 2013–14 school year. 

Source: Site fiscal and accounting information systems.

Exhibit B2. Personnel and non-personnel expenditures at the district level and at the school level,

2011–12

Site

Personnel Non-Personnel

Total

District-Level School-Level District-Level School-Level

Hawaii $171,071,568 $995,411,072 $332,267,008 $145,348,048 $1,644,097,696

Ohioa $1,268,543,104 $13,205,345,280 $868,200,576 $3,250,563,328 $18,592,652,288

Rhode Island $141,159,248 $1,543,163,136 $75,663,712 $256,924,432 $2,016,910,528

Texas $4,574,798,848 $26,033,225,728 $4,282,121,216 $3,974,550,784 $38,864,696,576

Baltimore $184,866,848 $729,488,448 $177,797,328 $80,851,816 $1,173,004,440

Hillsborough $178,440,832 $1,120,185,344 $114,156,072 $230,756,768 $1,643,539,016

Houston $285,020,992 $990,327,552 $148,597,488 $252,208,128 $1,676,154,160

Los Angeles $918,051,328 $4,569,957,376 $1,394,536,320 $503,037,536 $7,385,582,560

a Data for Ohio are from the 2013–14 school year. 

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 60

Exhibit B3. District-level expenditures and school-level expenditures by method of attribution, 2011–12

Site District-Level

School-Level

Total

Tracked Allocated Varied Unknown

Hawaii $503,338,592 $1,139,324,288 — — $1,434,889 $1,644,097,769

Ohioa $2,136,743,680 $12,347,590,656 $4,108,317,440 — — $18,592,651,776

Rhode Island $216,822,960 $1,186,700,032 $104,722,032 $508,665,600 — $2,016,910,624

Texas $8,856,920,064 $26,552,360,960 $3,426,736,384 $28,678,946 — $38,864,696,354

Baltimore $362,664,160 $593,727,872 $99,724,280 — $116,888,096 $1,173,004,408

Hillsborough $292,596,896 $1,294,354,816 $56,587,192 — — $1,643,538,904

Houston $433,618,496 $1,098,814,976 — — $143,720,688 $1,676,154,160

Los Angeles $2,312,587,776 $4,657,463,296 $186,825,824 $214,501,280 $14,204,332 $7,385,582,508

a Data for Ohio are from the 2013–14 school year.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 61

Exhibit B4. Spending by personnel category and level of attribution for state study sites, 2011–12

Expenditure

Type

Hawaii Ohioa Rhode Island Texas

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

School admin 

staff pay -$1,702,803b $89,015,944 $9,774,264 $653,811,136 $491,542 $67,859,488 $26,657,960 $1,778,064,128

Teacher pay $40,048,144 $732,669,184 $2,738,952 $6,992,621,056 $11,732,411 $819,652,288 $518,624,832 $17,269,655,552

Instructional

support pay $24,052,632 $13,419,239 $13,857,842 $155,460,976 $3,051,334 $21,276,726 $114,404,528 $667,414,656

Pupil support pay $48,670,588 $58,933,812 $65,280,040 $543,242,496 $13,185,354 $118,553,464 $174,405,232 $1,300,534,144

Maintenance and

operations pay $6,020,603 $49,429,596 $79,675,808 $496,315,968 $10,199,472 $38,218,896 $1,012,484,352 $557,240,704

Security personnel

pay — — $4,296,732 $23,974,950 $421,121 $2,836,819 — —

Transportation

staff pay $657,367 $6,028 $104,564,392 $288,133,728 $4,737,472 $7,801,430 $598,940,928 $7,950,089

Food service

personnel pay $1,464,494 $39,320,316 $37,951,696 $174,759,952 $414,428 $2,012,279 $238,859,712 $451,094,752

Professional

development pay $945,495 $898,512 $24,253,906 $98,141,048 $4,018,431 $17,429,118 $131,361,768 $297,106,624

Curriculum

development pay $9,696 — $12,275,080 $44,190,952 — — — —

Other district staff $26,919,400 $506,839 $468,386,112 $68,778,784 $49,222,088 $7,773,508 $919,420,032 $14,527,727

Health/group

benefits $23,985,952 $11,211,633 $142,303,632 $543,874,112 $20,112,032 $227,865,488 $292,859,776 $1,477,384,576

Retirement

benefits — — $221,719,584 $1,477,825,408 $12,193,975 $143,340,368 $98,799,968 $447,041,632

Other benefits — — $81,464,992 $1,644,214,784 $11,379,581 $68,543,344 $447,979,776 $1,765,209,984

a Data for Ohio are from the 2013–14 school year.

b The negative value for school administrative staff pay in Hawaii is likely due to an accounting adjustment. This negative amount accounts for a very small percentage of total expenditures in Hawaii 

(approximately one third of 1 percent (0.0034) of total reported district-level expenditures and one tenth of 1 percent (0.0010) of total reported site-level (district and school) expenditures).

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 62

Exhibit B5. Spending by personnel category and level of attribution for district study sites, 2011–12

Expenditure Type

Baltimore Hillsborough Houston Los Angeles

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

School admin staff pay $2,090,536 $67,704,824 $2,055,317 $67,066,672 -$51,551 $96,997,552 $3,761,483 $243,740,496

Teacher pay $14,686,195 $432,665,696 $16,912,844 $672,576,000 $5,506,726 $718,368,384 $148,929,904 $2,412,985,856

Instructional support pay $85,772 $6,644,501 $9,902,761 $16,428,071 $16,250,993 $7,490,789 $5,229,371 $17,676,438

Pupil support pay $444,767 $34,280,524 $7,338,275 $51,375,260 $15,034,154 $25,093,328 $43,079,448 $178,763,696

Maintenance and

operations staff pay $8,295,212 $11,316,179 $18,841,348 $28,613,886 $44,245,092 $30,126,710 $180,304,288 $214,501,280

Security personnel pay $9,344,335 $2,070,929 — — — — — —

Transportation staff pay $3,487,181 — $33,099,034 $89,934 $25,712,416 $576,849 $90,192,288 $53,856,492

Food service personnel 

pay $1,361,719 $10,731,282 $2,961,061 $22,343,432 $23,040,288 $7,770,509 $27,498,136 $67,804,656

Professional development

pay $1,030,086 $11,232,299 $4,931,163 $25,853,716 $17,966,680 $8,106,753 $60,825,404 $177,799,632

Curriculum development

pay $18,657,364 $1,146,482 $23,534,616 $9,439,271 $3,259,458 — — —

Other district staff $36,657,100 $362,760 $20,631,820 $223,448 $29,837,278 $1,122,220 $111,452,280 $12,755,270

Health/group benefits $23,287,820 $88,104,552 $17,348,002 $107,522,544 $13,221,224 $64,378,612 $44,968,728 $514,831,712

Retirement benefits $49,195,764 $8,559,433 $7,034,508 $41,398,732 $21,684,862 $5,328,036 $25,768,564 $252,474,816

Other benefits $16,243,000 $54,668,964 $13,850,084 $77,254,320 $69,313,376 $24,967,858 $176,041,456 $422,766,816

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 63

Exhibit B6. Spending by non-personnel category and level of attribution for state study sites, 2011–12

Expenditure Type

Hawaii Ohioa Rhode Island Texas

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

Textbooks and

instructional materials $469,391 $4,460,508 $253,524 $18,941,356 $35,257 $5,230,991 $246,591,840 $167,939,744

General supplies $23,838,440 $42,455,420 $27,053,252 $472,977,600 $7,214,573 $51,901,516 $550,210,048 $1,192,542,464

Food supplies $985,885 $38,583,364 $34,133,488 $222,758,912 $847,832 $1,800,542 $345,115,264 $662,312,896

Furniture and

equipment $3,519,114 $10,455,142 $13,282,028 $192,516,944 $821,434 $4,301,294 — —

Computers and 

software $5,577,958 $23,212,870 $1,382,284 $38,774,384 $2,500,381 $10,696,272 — —

Transportation $72,603,800 $982,772 $84,250,336 $295,339,200 $3,240,317 $58,740,328 $475,483,904 $11,452,494

Professional

development $9,380,185 $551,936 $16,957,042 $71,144,280 $6,059,625 $5,285,662 $109,266,112 $146,296,128

Curriculum

development $72,240 $321,524 $8,937,770 $30,379,130 — — — —

Other district services $23,084,910 $433,328 $507,462,016 $190,437,440 $17,871,534 $3,718,302 $640,545,792 $14,151,219

Purchased services

and rentals $114,311,016 $19,927,674 $123,295,392 $1,189,691,008 $30,747,268 $104,722,032 $814,346,752 $745,071,744

Telecommunications $1,243,853 $1,948,147 $5,890,732 $40,294,880 $1,307,934 $1,664,550 — —

Utilities $62,391,556 $108,573 $28,937,398 $378,243,648 $278,016 $3,370,953 $639,066,112 $658,706,880

Insurance $12,152 $15,992 $1,445,546 $2,919,148 $3,624,248 $1,856,214 $196,694,816 $16,863,104

Security — — $3,621,222 $17,441,278 $479,825 $2,256,402 — —

Miscellaneous items — $1,890,795 $11,298,546 $88,704,168 $635,470 $1,379,376 $264,800,608 $359,214,112

a Data for Ohio are from the 2013–14 school year.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 64

Exhibit B7. Spending by non-personnel category and level of attribution for district study sites, 2011–12

Expenditure Type

Baltimore Hillsborough Houston Los Angeles

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

DistrictLevel

SchoolLevel

Textbooks and

instructional materials $2,993,891 $9,569,451 $1,107,780 $18,942,266 $5,280,418 $6,796,325 $21,677,282 $41,371,856

General supplies $2,331,581 $5,133,756 $7,475,423 $27,520,376 $10,549,670 $33,582,644 $76,160,040 $104,471,216

Food supplies $17,371,570 $2,640,059 $3,360,717 $36,036,972 $864,306 $39,739,580 $15,814,547 $128,448,376

Furniture and

equipment $88,929 $264,240 $2,177,803 $5,738,537 — — $8,327,008 $26,132,854

Computers and 

software $66,072 $883,045 $4,981,936 $8,364,617 — — $4,676 $152,348

Transportation $36,548,328 $262,205 $19,212,854 $2,899,126 $12,671,044 $290,114 $30,627,264 $11,638,437

Professional

development $2,198,287 $531,959 $8,766,564 $1,310,210 $24,520,186 $6,185,422 $31,511,520 $8,743,940

Curriculum

development $5,777,860 $1,288,848 $8,722,964 $193,401 $514,838 — — —

Other district services $26,163,962 — $14,511,758 $1,034,699 $24,354,706 $137,015 $112,071,040 $1,449,063

Purchased services

and rentals $46,851,248 $59,678,812 $28,471,688 $69,518,216 $44,591,360 $99,721,344 $1,079,668,736 $59,298,548

Telecommunications $5,511,901 $130,051 $2,752,748 $1,322,421 — — $6,579,612 $10,122,064

Utilities $28,420,200 — $2,722,843 $45,104,920 $15,597,265 $48,850,404 $4,370,256 $106,864,600

Insurance $10,000 — $7,294,807 — $1,572,539 — $2,746,674 —

Security $3,147,141 $80,616 — — — — — —

Miscellaneous items $316,351 $388,778 $2,596,183 $12,771,003 $8,081,165 $16,905,288 $4,977,676 $4,344,240

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 65 

Appendix C. Distribution of Differences Between Actual 

Reported Expenditures and Simulated Allocations

 Exhibit C1. Distribution of schools by difference in instructional support spending between actual 

(tracked) expenditures and allocations simulated based on enrollment, 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Small differences are those where the magnitude of the difference was less 

than 10 percent of the allocation simulated figure; medium differences are between 10 and 25 percent; and large differences are greater than 

25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 66 

Exhibit C2. Distribution of schools by difference in instructional support spending between actual 

(tracked) expenditures and allocations simulated based on full-time equivalents (FTEs), 

2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the allocation simulated figure; medium differences are between 

10 and 25 percent; and large differences are greater than 25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 67 

Exhibit C3. Distribution of schools by difference in pupil support spending between actual (tracked) 

expenditures and allocations simulated based on enrollment, 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Small differences are those where the magnitude of the difference was less 

than 10 percent of the allocation simulated figure; medium differences are between 10 and 25 percent; and large differences are greater than 

25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 68 

Exhibit C4. Distribution of schools by difference in pupil support spending between actual (tracked) 

expenditures and allocations simulated based on full-time equivalents (FTE), 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Small differences are those where the magnitude of the difference was less 

than 10 percent of the allocation simulated figure; medium differences are between 10 and 25 percent; and large differences are greater than 

25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 69 

Exhibit C5. Distribution of schools by difference in retirement benefit spending between actual 

(tracked) expenditures and allocations simulated based on full-time equivalents (FTEs), 

2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Small differences are those where the magnitude of the difference was less 

than 10 percent of the allocation simulated figure; medium differences are between 10 and 25 percent; and large differences are greater than 

25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 70 

Exhibit C6. Distribution of schools by difference in retirement benefit spending between actual 

(tracked) expenditures and allocations simulated based on salaries, 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the allocation simulated figure; medium differences are between 

10 and 25 percent; and large differences are greater than 25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 71 

Exhibit C7. Distribution of schools by difference in health benefit spending between actual (tracked) 

expenditures and allocations simulated based on full-time equivalents (FTEs), 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Small differences are those where the magnitude of the difference was less 

than 10 percent of the allocation simulated figure; medium differences are between 10 and 25 percent; and large differences are greater than 

25 percent.

Source: Site fiscal and accounting information systems. 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 72 

Exhibit C8. Distribution of schools by difference in health benefit spending between actual (tracked) 

expenditures and allocations simulated based on salaries, 2011–12

a Data for Ohio are from the 2013–14 school year.

Note: Sum of percentages may not add to 100 due to rounding. Bar segments are left unlabeled for values less than 3 percent. Small differences 

are those where the magnitude of the difference was less than 10 percent of the allocation simulated figure; medium differences are between 

10 and 25 percent; and large differences are greater than 25 percent.

Source: Site fiscal and accounting information systems.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 73

Appendix D. Data Collection Instruments

Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 75

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Request for Documents and Files (RFD)

for the 2011–12 and 2012–13 School Years

(for State Sites)

Data Collection Procedures

Background

You have received this Request for Documents because your state was selected to participate

in the U.S. Department of Education’s Feasibility Study on Improving the Quality of SchoolLevel Expenditure Data. The purpose of the study is to examine how district and state fiscal

accounting systems currently track expenditures at the school level and explore possible

strategies for improving the quality of school-level expenditure data that are being collected 

by the U.S. Department of Education.

In this request we are asking participating states and districts to provide school-level

expenditure and student count data for the 2011–12 and 2012–13 school years. In order to

minimize your burden in providing these data as much as possible, we ask that, before you 

start to gather these data, please carefully read through this document and participate in a

short telephone conversation with us so we can clarify any questions you might have. We will

contact you shortly to arrange this discussion at your convenience.

Description of Requested Data Files and Documents

1. Detailed school-level expenditure data reports for the 2011–12 and 2012–13 school years. We

would like to obtain electronic file(s) that provide a detailed breakdown of site-level

expenditures for all schools in your state. This information may reside in a single fiscal file that

includes entries for both revenues and expenditures, or in separate files depending on your

accounting and reporting methods. Important: Please send us the data in its original structure

(i.e., do not burden yourself with isolating expenditures if it is housed in a dataset that also

includes revenues).

We need to obtain school-level expenditures for each specific combination of codes in

your chart of account coding structure. Your codes are likely to include some or all of the

following:

• Year (school year for which data was collected)

• Location (code for each school site or the central district office)

• Source (e.g., general unrestricted, federal, state and local restricted categorical

programs, private grants, etc.)

• Program(e.g., regular education, special education, extended school day, etc.)

• Function (e.g., instruction, administration, instructional support, etc.)

• Object (e.g., salaries of classified and certified staff, benefits, instructional

materials, purchased services, capital equipment, etc.)

• Job Code (e.g., teaches, aide, principal, program director, administrator)

• Amount (total dollars associated with entry)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 76

To further clarify this request, Attachment A provides an excerpt from a data file as an

example of a format that would be acceptable. This example may not resemble the exact

reporting structure used in your state, but illustrates the type of information needed. That

is, although you do not need to provide a file in precisely this format, we ask that the files

you provide do contain the requisite information listed above.

2. Chart of accounts and descriptions. To interpret and analyze the school-level expenditures

provided we will also need to understand the account code definitions used in your state.

To this end, we request that you provide electronic versions of your state accounting

manual, and lists of the various chart of account codes and their descriptions. Attachment

B provides an example list of chart of account codes.

3. Crosswalk to F-33 Data. In order to accurately explore the types of school-level

expenditures tracked across the study sites, we will need a crosswalk of the (set of)

account codes used in your state that match to each object in the Expenditures sections of

the F-33 survey (Sections II through IV only).This crosswalk will also help us compare the

consistency of data across schools and with the information from alternative federal data

collections. The attached excel file in the email is example table which details the

information will we need. However, please do not burden yourself with filling out the table

if you can send your crosswalk in its current form; we will gladly accept the crosswalk in its

original structure.

4. Any existing documentation regarding how expenditures are tracked to schools. To help

us understand how expenditures are tracked to individual school sites, please provide any

existing technical documentation that describes which expenditures are tracked to the

school level and how this is done.

5. Student counts. In order to calculate spending on per pupil basis and to perform analyses

that take into account school characteristics such as size and student needs we will need

school-level student count data for the following: total enrollment by grade level; students

eligible for the federal free/reduced price lunch program; students with an Individualized

Education Plan (IEP); and students designated as English learners. In addition, please

provide a site identifier that is the same as the location code provided with the

expenditure data described under item 1 above, or if a different location identifier is used

for the expenditure and student count data, then we will require a crosswalk between

these two codes.

6. State Personnel Database. Many states maintain statewide personnel data systems that

include information about individual employees of local education agencies (LEAs). If such

a personnel database exists for your state, please provide it to us or direct us to where it is

publically accessible. We would like to compare the salary information in your state’s

personnel database to the corresponding expenditures data from item 1, above.

Sometimes these data use the same coding structure as the system that tracks

expenditures (e.g., function, program, object and/or other codes), and while that structure

would be beneficial, it is not absolutely necessary for our planned analysis. No personal

information will be needed; we will only need personnel expenditures amounts with a

common school identifier code which can link the personnel data to the expenditures data

(or a crosswalk between the system-specific codes), for our study purposes.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 77

Data Format

We would prefer the fiscal (expenditure) data files to be in Excel format. However, if these are not

available in Excel, we are happy to accommodate. We can accept files in at least the following 

formats:

• Dbase (.dbf)

• SAS (.sas)

• Stata (.dta)

• Access (.mdb)

• Tab-Delimited (.tab)

• Comma Separated Values (.csv)

Submission of Files

Once you have gathered the requested information, we ask that you please forward them using 

one of the following methods:

• Email. Relatively small files may be sent to us as attachments to an e-mail. Note that fiscal

files in particular can be too large for many e-mail servers to handle (e.g., attachments that

are over 20 MB). Some large files may still be sent via email if they are compressed to a

zipped volume(s).

• FTP. Larger files may be sent through a secure AIR Extranet or File Transfer Protocol (FTP)

site. Should you choose this option we will send you login instructions and credentials to

access this site, directions on how to upload files, and work with you to ensure smooth

transmission of these data.

• CD or DVD. Files that are too large for e-mailing can also be burned to CD or DVD and sent

via mail. Please let us know if you prefer to submit the files in this manner, and we will

send a pre-paid, pre-addressed FedEx mailer, with instructions, that you may use.

Next Steps

A representative from the study team will be contacting you shortly to schedule a meeting to 

review this data request and answer any questions you may have. However, should you have any

immediate concerns, please feel free to contact Antonia Wang at (650) 843-8225 or

awang@air.org.

Thank you in advance for your time and cooperation.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 78

Attachment A

Sample Expenditure Report Data

YEAR FUND RESOURCE OBJECT SITE GOAL FUNCTION LO1 LO2 EXPENDITURE

1112 01 0000 3501 677 1740 2700 000 051 5.40

1112 01 3010 3101 730 1110 1000 000 303 52.89

1112 01 6500 3502 660 5770 1110 999 891 17.05

1112 01 0000 5800 040 0000 8300 000 604 2,086.88

1112 01 0000 3602 920 0000 3140 000 050 2.17

1112 01 0000 5742 080 1525 1000 000 608 2,987.50

1112 01 0000 3501 657 0000 2700 999 671 321.36

1112 01 0000 4200 651 0000 2700 111 111 265.64

1112 01 6405 3602 910 0000 8300 000 433 5.35

1112 01 0000 3601 607 1110 1000 003 050 8.06

1112 01 0000 3961 040 0000 7180 000 604 3,000.00

1112 01 6500 3101 643 5770 1120 999 201 4,044.37

1112 01 0000 3202 940 0000 8200 000 675 215.82

1112 01 0000 3281 920 0000 2420 999 670 0.00

1112 01 7091 3802 602 4760 1000 999 401 1,637.25

1112 01 0000 3202 602 0000 2420 999 670 1,539.30

1112 01 4035 3101 730 4760 1000 999 335 6,172.44

1112 01 0000 3602 604 0000 2700 999 671 1,232.61

1112 01 4203 5800 100 1110 1000 000 357 103,483.58

1112 01 0000 3202 750 0000 8200 000 629 176.58

1112 01 7090 3501 080 1695 2140 000 401 1.50

1112 01 3010 4300 677 1110 1000 000 303 6,772.00

1112 01 0000 3301 810 1240 4200 000 050 18.91

1112 01 3310 3932 646 5770 1110 999 307 1,079.84

1112 01 0000 3202 691 0000 8200 000 629 79.55

1112 01 0000 3312 750 0000 3140 999 674 514.86

1112 01 0000 3801 060 0000 3120 999 614 1,107.26

1112 01 3710 4300 280 0000 2495 000 302 3,024.41

1112 01 0000 3311 612 1110 1000 012 050 42.92

1112 01 6500 5800 656 5770 1110 000 250 168.50

1112 01 0000 3602 660 0000 3140 000 050 60.38

1112 01 3010 3311 614 1695 1000 000 313 15.68

1112 01 6500 3311 669 5770 1120 999 201 1,221.69

1112 01 3010 3302 627 0000 2700 999 303 2,077.25

1112 01 3010 3311 995 1110 1000 999 303 570.73

1112 01 7395 3312 090 0000 8200 000 454 1.63

1112 01 6275 3311 030 9570 7400 000 457 14.50

1112 01 0000 3302 689 0000 2700 999 671 3,168.18

1112 01 6010 4300 621 1110 1000 000 448 3,083.22

1112 01 3315 3302 690 5730 2700 999 342 448.13

1112 01 0000 3602 649 0000 2700 999 671 1,295.58

1112 01 3310 3602 691 5770 1120 000 307 3.41
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 79

Attachment B

Sample Chart of Accounts Codes

...
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 80

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Request for Documents and Files (RFD)

for the 2011–12 and 2012–13 School Years

(for District Sites)

Data Collection Procedures

Background

You have received this Request for Documents because your district was selected to 

participate in the U.S. Department of Education’s Feasibility Study on Improving the Quality of 

School-Level Expenditure Data. The purpose of the study is to examine how district and state 

fiscal accounting systems currently track expenditures at the school level and explore possible 

strategies for improving the quality of school-level expenditure data that are being collected 

by the U.S. Department of Education.

In this request we are asking participating states and districts to provide school-level 

expenditure and student count data for the 2011–12 and 2012–13 school years. In order to 

minimize your burden in providing these data as much as possible, we ask that, before you 

start to gather these data, please carefully read through this document and participate in a 

short telephone conversation with us so we can clarify any questions you might have. We will 

contact you shortly to arrange this discussion at your convenience.

Description of Requested Data Files and Documents

1. Detailed school-level expenditure data reports for the 2011–12 and 2012–13 school years. We

would like to obtain electronic file(s) that provide a detailed breakdown of site-level

expenditures for all schools in your district. This information may reside in a single fiscal file that

includes entries for both revenues and expenditures, or in separate files depending on your

accounting and reporting methods. Important: Please send us the data in its original structure

(i.e., do not burden yourself with isolating expenditures if it is housed in a dataset that also

includes revenues).

We need to obtain school-level expenditures for each specific combination of codes in

your chart of account coding structure. Your codes are likely to include some or all of the

following:

• Year (school year for which data was collected)

• Location22

22 If your district’s location codes to identify school sites differ from your state’s school site codes, please provide us a crosswalk 

so we can match locations in the expenditure data.

(code for each school site or the central district office)

• Source (e.g., general unrestricted, federal, state and local restricted categorical

programs, private grants, etc.)

• Program(e.g., regular education, special education, extended school day, etc.)

• Function (e.g., instruction, administration, instructional support, etc.)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 81

• Object (e.g., salaries of classified and certified staff, benefits, instructional

materials, purchased services, capital equipment, etc.)

• Job Code (e.g., teaches, aide, principal, program director, administrator)

• Amount (total dollars associated with entry)

To further clarify this request, Attachment A provides an excerpt from a data file as an 

example of a format that would be acceptable. This example may not resemble the exact 

reporting structure used in your district, but illustrates the type of information needed. 

That is, although you do not need to provide a file in precisely this format, we ask that the 

files you provide do contain the requisite information listed above.

2. Chart of accounts and descriptions. To interpret and analyze the school-level

expenditures provided we will also need to understand the account code definitions used

in your state and any additional custom codes your district may use. To this end, we

request that you provide electronic versions of your state accounting manual, lists of the

various chart of account codes and their descriptions, and additional documentation that

describes any additional codes used by your district. Attachment B provides an example

list of chart of account codes.

3. Any existing documentation regarding how expenditures are tracked to schools. To help

us understand how expenditures are tracked to individual school sites, please provide any

existing technical documentation that describes which expenditures are tracked to the

school level and how this is done.

4. Student counts. In order to calculate spending on per pupil basis and to perform analyses

that take into account school characteristics such as size and student needs we will need

school-level student count data for the following: total enrollment by grade level; students

eligible for the federal free/reduced price lunch program; students with an Individualized

Education Plan (IEP); and students designated as English learners. In addition, please

provide, for each school, either a site identifier that is the same as the location code

provided with the expenditure data described under item 1 above, or if different location

identifiers are used for the expenditure and student count data, a crosswalk between

these two codes.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 82

Data Format

We would prefer the fiscal (expenditure) data files to be in Excel format. However, if these are not

available in Excel, we are happy to accommodate. We can accept files in at least the following formats:

• Dbase (.dbf)

• SAS (.sas)

• Stata (.dta)

• Access (.mdb)

• Tab-Delimited (.tab)

• Comma Separated Values (.csv)

Submission of Files

Once you have gathered the requested information, we ask that you please forward them using 

one of the following methods:

• Email. Relatively small files may be sent to us as attachments to an e-mail. Note that fiscal

files in particular can be too large for many e-mail servers to handle (e.g., attachments that

are over 20 MB). Some large files may still be sent via email if they are compressed to a

zipped volume(s).

• FTP. Larger files may be sent through a secure AIR Extranet or File Transfer Protocol (FTP)

site. Should you choose this option we will send you login instructions and credentials to

access this site, directions on how to upload files, and work with you to ensure smooth

transmission of these data.

• CD or DVD. Files that are too large for e-mailing can also be burned to CD or DVD and sent

via mail. Please let us know if you prefer to submit the files in this manner, and we will

send a pre-paid, pre-addressed FedEx mailer, with instructions, that you may use.

Next Steps

A representative from the study team will be contacting you shortly to schedule a meeting to review this

data request and answer any questions you may have. However, should you have any immediate

concerns, please feel free to contact Antonia Wang at (650) 843-8225 or awang@air.org.

Thank you in advance for your time and cooperation.
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 83

Attachment A

Sample Expenditure Report Data

YEAR FUND RESOURCE OBJECT SITE GOAL FUNCTION LO1 LO2 EXPENDITURE

1112 01 0000 3501 677 1740 2700 000 051 5.40

1112 01 3010 3101 730 1110 1000 000 303 52.89

1112 01 6500 3502 660 5770 1110 999 891 17.05

1112 01 0000 5800 040 0000 8300 000 604 2,086.88

1112 01 0000 3602 920 0000 3140 000 050 2.17

1112 01 0000 5742 080 1525 1000 000 608 2,987.50

1112 01 0000 3501 657 0000 2700 999 671 321.36

1112 01 0000 4200 651 0000 2700 111 111 265.64

1112 01 6405 3602 910 0000 8300 000 433 5.35

1112 01 0000 3601 607 1110 1000 003 050 8.06

1112 01 0000 3961 040 0000 7180 000 604 3,000.00

1112 01 6500 3101 643 5770 1120 999 201 4,044.37

1112 01 0000 3202 940 0000 8200 000 675 215.82

1112 01 0000 3281 920 0000 2420 999 670 0.00

1112 01 7091 3802 602 4760 1000 999 401 1,637.25

1112 01 0000 3202 602 0000 2420 999 670 1,539.30

1112 01 4035 3101 730 4760 1000 999 335 6,172.44

1112 01 0000 3602 604 0000 2700 999 671 1,232.61

1112 01 4203 5800 100 1110 1000 000 357 103,483.58

1112 01 0000 3202 750 0000 8200 000 629 176.58

1112 01 7090 3501 080 1695 2140 000 401 1.50

1112 01 3010 4300 677 1110 1000 000 303 6,772.00

1112 01 0000 3301 810 1240 4200 000 050 18.91

1112 01 3310 3932 646 5770 1110 999 307 1,079.84

1112 01 0000 3202 691 0000 8200 000 629 79.55

1112 01 0000 3312 750 0000 3140 999 674 514.86

1112 01 0000 3801 060 0000 3120 999 614 1,107.26

1112 01 3710 4300 280 0000 2495 000 302 3,024.41

1112 01 0000 3311 612 1110 1000 012 050 42.92

1112 01 6500 5800 656 5770 1110 000 250 168.50

1112 01 0000 3602 660 0000 3140 000 050 60.38

1112 01 3010 3311 614 1695 1000 000 313 15.68

1112 01 6500 3311 669 5770 1120 999 201 1,221.69

1112 01 3010 3302 627 0000 2700 999 303 2,077.25

1112 01 3010 3311 995 1110 1000 999 303 570.73

1112 01 7395 3312 090 0000 8200 000 454 1.63

1112 01 6275 3311 030 9570 7400 000 457 14.50

1112 01 0000 3302 689 0000 2700 999 671 3,168.18

1112 01 6010 4300 621 1110 1000 000 448 3,083.22

1112 01 3315 3302 690 5730 2700 999 342 448.13

1112 01 0000 3602 649 0000 2700 999 671 1,295.58

1112 01 3310 3602 691 5770 1120 000 307 3.41
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 84

Attachment B

Sample Chart of Accounts Codes

...
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 85

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Pre-Interview Survey for States

American Institutes for Research is conducting a study for the U.S. Department of Education to 

examine how some states and districts are currently tracking expenditures to the school level in 

an effort to improve the quality of these types of data. You are one of five states participating in 

this study.

We want to learn more about the structure of your fiscal system, what types of expenditures 

you currently track to the school level and the methods used to do so, and how the data system 

that allows you to do this was implemented in your state. This brief survey aims to gather some 

basic information in advance of our interview. Please complete the questions below and return 

the survey to Karen Manship at kmanship@air.org. Thank you!

Name:

State: [pre-filled]

Date:

1. In what year did your state begin tracking expenditures at the school level? _____________

2. We are interested in how your systems were developed or modified to track or collect schoollevel expenditures. Was your current school-level accounting system purchased from a vendor

or developed internally? Please check the appropriate box below.

PURCHASED FROM A VENDOR

DEVELOPED INTERNALLY
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 86

3. How many SEA staff are currently involved in implementing your school-level expenditure

data system? Please complete the table below with the titles of staff members involved,

an estimate of the numbers of Full-Time-Equivalent (FTEs) involved for each type of staff,

and a brief description of their primary responsibilities (for example, providing technical

assistance to LEA staff, cleaning or reviewing data).

JOB TITLE ESTIMATED FTE PRIMARY RESPONSIBILITIES

4. Does your state have a personnel data system that includes information about individual

employees of the local education agencies (LEAs) in your state?

YES NO

If so, which of the following types of information does the data system contain? (Check all that 

apply.)

EDUCATIONAL ATTAINMENT/DEGREE

JOB TITLE

CERTIFICATIONS

FULL-TIME EQUIVALENCY OR HOURS /WEEK

SUBJECT/GRADE ASSIGNMENT

POSITION LOCATION(S)

SALARY

YEARS EMPLOYED/EXPERIENCE

What types of staff are included in the data system? (Check one box per row.)

Certificated and

Classified Staff Certificated Staff Only Teachers Only

State Education Agency (SEA)

Local Education Agency (LEA)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 87

5. To what extent were the following represent a challenge in implementing or changing the

financial data collection and reporting system? Which were overcome, and which still remain

challenges?

Issue Not a

challenge

Moderate 

challenge

Major 

challenge

Was this

overcome?

Still a 

challenge?

IT/computer hardware problems

Bureaucratic or political processes

Staff capacity/training needs

Resistance from schools

Accounting structures or funding

distribution procedures

Other challenge (please describe: 

_______________________________

_______________________________)

6. How challenging are the following types of spending for districts in your state to report at the

school level?

Type of Expenditure Not a

challenge

Moderate 

challenge

Major 

challenge

Teachers that work at multiple schools or sites

Other staff that worked at multiple schools or sites

Central office staff

Supplies and materials

Computer equipment

Other capital expenditures

Utilities

District insurance

Staff benefits

Other costs? 

(Describe:__________________________________

_________________________________________)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 88

7. Please complete the table below to indicate the types of expenditure that are currently reported

at the individual school-site level in your state? For each expenditure type, indicate whether

school-level expenditure reporting is based on tracking actual expenditures at each site or

allocating (estimating) expenditures to each site using a formula or another method?

Reported at the 

school level? Method?

Expenditure Type Yes No Tracked Allocated

EXAMPLE: Salaries for teachers x x 

EXAMPLE: Utilities x x 

Administrator salaries

Salaries for teachers (including special education and other 

specialist teachers) who work at one school

Salaries for other instructional support staff (including media 

specialists, special education aides, and others)who work at one 

school

Salaries for pupil support staff (including guidance counselors, 

speech/language pathologists, and others) who work at one 

school

Salaries for teachers who work at more than one school

Salaries for instructional support staff who work at more than one 

school

Salaries for pupil support staff who work at more than one school

Staff health insurance benefits

Retirement and pensions (if applicable)

Other staff benefits (Specify 

Professional development

Textbooks and other instructional materials

Office/general supplies

Furniture

Computers and equipment

Telecommunications

School maintenance and operations staff

Utilities

Insurance

Security services

Transportation

Food services personnel

Food services supplies

Centralized curriculum development

Staff who provide districtwide instructional and student support 

services

District administrative services 

Other central office staff (please list)

 

Other expenditures not included above (please list)

 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 89

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Pre-Interview Survey for Districts

American Institutes for Research is conducting a study for the U.S. Department of Education to 

examine how some states and districts are currently tracking expenditures to the school level in 

an effort to improve the quality of these types of data. You are one of four districts participating 

in this study.

We want to learn more about the structure of your fiscal system, what types of expenditures 

you currently track to the school level and the methods used to do so, and how the data system 

that allows you to do this was implemented in your district. This brief survey aims to gather 

some basic information in advance of our interview. Please complete the questions below and 

return the survey to Karen Manship at kmanship@air.org. Thank you!

Name:

District: [pre-filled]

Date:

1. In what year did your district begin tracking expenditures at the school level? _____________

2. We are interested in how your systems were developed or modified to track or collect schoollevel expenditures. Was your current school-level accounting system purchased from a vendor

or developed internally? Please check the appropriate box below.

PURCHASED FROM A VENDOR

DEVELOPED INTERNALLY
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 90

3. How many district staff are currently involved in implementing your school-level expenditure

data system? Please complete the table below with the titles of staff members involved, an

estimate of the numbers of Full-Time-Equivalent (FTEs) involved for each type of staff, and a

brief description of their primary responsibilities (for example, providing technical assistance to

LEA staff, cleaning or reviewing data).

JOB TITLE ESTIMATED FTE PRIMARY RESPONSIBILITIES

4. To what extent did the following represent a challenge in implementing or changing the

financial data collection and reporting system? Which were overcome, and which still remain

challenges?

Issue Not a

challenge

Moderate 

challenge

Major 

challenge

Was this

overcome?

Still a 

challenge?

IT/computer hardware problems

Bureaucratic or political processes

Staff capacity/training needs

Resistance from schools

Accounting structures or funding

distribution procedures

Other challenge (please describe:

_____________________________

_____________________________)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 91

5. How challenging are the following types of spending for your district to report at the school

level?

Type of Expenditure Not a

challenge

Moderate 

challenge

Major 

challenge

Teachers that work at multiple schools or sites

Other staff that worked at multiple schools or sites

Central office staff

Supplies and materials

Computer equipment

Other capital expenditures

Utilities

District insurance

Staff benefits

Other costs? 

(Describe:__________________________________

_________________________________________)
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 92

6. Please complete the table below to indicate the types of expenditure that are currently reported

at the individual school-site level in your district? For each expenditure type, indicate whether

school-level expenditure reporting is based on tracking actual expenditures at each site or

allocating (estimating) expenditures to each site using a formula or another method?

Reported at the 

school level? Method?

Expenditure Type Yes No Tracked Allocated

EXAMPLE: Salaries for teachers x x 

EXAMPLE: Utilities x x 

Administrator salaries

Salaries for teachers (including special education and other 

specialist teachers) who work at one school

Salaries for other instructional support staff (including media 

specialists, special education aides, and others) who work at one 

school

Salaries for pupil support staff (including guidance counselors, 

speech/language pathologists, and others) who work at one school

Salaries for teachers who work at more than one school

Salaries for instructional support staff who work at more than one 

school

Salaries for pupil support staff who work at more than one school

Staff health insurance benefits

Retirement and pensions (if applicable)

Other staff benefits (Specify 

Professional development

Textbooks and other instructional materials

Office/general supplies

Furniture

Computers and equipment

Telecommunications

School maintenance and operations staff

Utilities

Insurance

Security services

Transportation

Food services personnel

Food services supplies

Centralized curriculum development

Staff who provide districtwide instructional and student support 

services

District administrative services 

Other central office staff (please list)

 

Other expenditures not included above (please list)

 
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 93

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Interview Protocol for State Budget Officers

State: Interviewer:

Interviewee Name: Date/Time:

Interviewee Title: Interviewee’s Role:

Introduction

Thanks again for taking the time to speak with me this morning/afternoon. Before we start, I’d like to

provide a little background on our work, and answer any questions you might have for me.

I work for an independent not-for-profit research organization called the American Institutes for

Research and we are conducting a study for the U.S. Department of Education to investigate how state

and district data systems are currently tracking expenditures to schools in an effort to improve the

quality of these types of data. You are one of five states participating in this study.

We want to learn more about the structure of your school-level expenditure reporting system, what

types of expenditures you track or allocate to the school level and how, and how the process of creating 

a system that allows you to do that was implemented in your state.

Everything that we discuss here will be used for research purposes only. It would help us with note

taking if we could record this conversation - would that be ok with you? I can turn the recorder off at

any point by your request.

If I ask you a question for which you think you are not the best respondent, it would be helpful if you 

could refer me to one of your colleagues who would be able to provide the answer.

Do you have any questions for me before we start?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 94

1. First, can you tell me a little about your position and role? What are your responsibilities? How long

have you been working in [state], and how long have you been working with district and school

expenditure data? In what capacity do you work with these data?

2. We’d like to learn about the history of how your state initiated the collection of school-level

expenditure data. You mentioned on the pre-interview survey that you began tracking expenditures

to the school level in [YEAR].

a. How did this policy originate? Were any specific individuals or organizations active in proposing

or advocating for this policy? If so, who? Was there any opposition? What were the reasons

for supporting or opposing this policy? How did any disagreements over the new policy get

resolved?

b. Were specific legislative or regulatory changes enacted to establish the collection of school-level

expenditure data? If so, please describe these changes.

c. What first motivated your state to create a system to track or collect expenditures at the school

level? Are there specific goals or objectives that the state hopes to accomplish through use of

these data?

3. You indicated in the pre-interview survey that were challenges for your state in

implementing or changing the financial data collection and reporting system. Which was the

greatest challenge and why? How did you go about addressing the challenges you were able to

overcome? For the challenges that are still being faced, why is this the case?

4. You mentioned that are the most challenging types of spending for districts in your state

to report at the school level. Why is this? How did you go about addressing the challenges you

were able to overcome? For the challenges that still exist, why is this the case?

5. We’d like to know more about how your systems were developed or modified to track or collect

school-level expenditures. You told us on the pre-interview survey that you [developed the schoollevel accounting system in house/purchased the system from a vendor]. How did you make that

decision, and who was involved?

a. Do you use contractor support to implement the data collection and reporting system(s)? If so,

what are the contractor’s specific responsibilities?

b. [If system was purchased from a vendor:] How long did it take to acquire the system from the

vendor? How did that process work?

c. [If system was developed in house:]Tell us about the process of developing the accounting

system in house. What was required? How long did it take?

6. Thank you for the information you provided regarding the staff who are involved in collecting and

cleaning data for the school level expenditure data systems. Were these staff from different

agencies or departments, or the same one? Which would you say are the essential staff roles

involved in developing and administering a school-level expenditure system?

a. Thinking back, what staff were involved in acquiring or developing the new system? Do you

have any documentation of the process your state went through in acquiring and developing

your fiscal system that permits you to track school level expenditures? Can we obtain this

documentation for our records and review?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 95

7. We are also interested in non-staff costs of administering your school-level tracking system. What

were the approximate monetary costs involved in developing your school expenditures data

system? And what are the monetary costs now, of implementing it day-to-day? Do you have any

documentation or analysis of these costs you could share with us?

Probes:

a. Contract for IT system development?

b. Software?

c. Hardware?

d. Contractor to provide training to staff?

e. Training materials/documentation?

f. Contractor support for collecting, compiling, cleaning, and reporting the data?

g. Other types of expenses?

8. Thank you for the information you provided on the survey regarding your state personnel data

system.

a. [If applicable- if state data system contains salary information:] Do the fiscal data on salaries

represent an aggregation of the data in the payroll or personnel systems?

b. Are the two systems (the fiscal and personnel systems) linked in any way? If so, please describe

how they are linked. Does the personnel data system use the same coding structure as the

system that tracks expenditures (e.g., function, program, object and/or other codes)? Can

schools be linked across these systems using a common school identifier code or a crosswalk

between system-specific codes?

9. Is there a single centralized system used by districts to input/upload school-level expenditures that

consolidates this information into a database accessed by the state?

a. If so, does the system also include automated procedures for districts to calculate spending at

individual school sites?

b. If not, is there a common application used by districts to do this or are different solutions or

systems used?

c. If districts use their own applications to calculate school-level expenditures, are there standard

guidelines developed and issued by the state? May we get a copy of these guidelines?

d. Are different types of districts or schools required to report school-level expenditures in the

same way, or do requirements vary by type of district or school? If they vary, how and why?

10. Does the state have any policies or procedures to ensure the quality of school-level expenditure

data? By quality, we mean consistency (i.e., that expenditures are tracked or allocated in the same

way in different schools and districts, and that information gathered from different systems--e.g.,

fiscal and payroll systems--would produce the same figures), completeness (i.e., that all expenditure

categories included in your school-level expenditure system are fully reported for all sites), and

accuracy? Are quality checks performed, and what do these consist of?

Probes:

a. Guidelines or instructions for schools/districts to use in entering data?

b. Training for districts? (through in-person meetings, webinars, conference calls?)

c. Technical assistance for districts?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 96

d. Automatic checks in data entry system (such as maximum values allowed, or preventing a user

from continuing if a cell is left blank)?

e. Staff assigned to review incoming data?

f. Standard analyses/data checks done after all data are entered?

11. What challenges have you encountered in ensuring data accuracy, consistency (across data sources

and schools/districts), and completeness at the school level?

12. How does your state uses school-level expenditure data? For what purposes?

Probes:

a. Do you do any kind of analysis of school-level spending? Do you report any analysis back to

districts?

b. Could you share an example report with us?

13. Does your state make information on school-level expenditures available to the general public?

Probes:

a. What kind of information is made available to the public? How is this made available (online

and downloadable, or some other method)?

Available

online

Available

upon 

request

Made available

through other

means (specify)

Not publicly

available

Data files with total expenditures

by school

Data files with detailed

expenditures by school and by

expenditure category

Reports containing analysis of

school-level expenditure data

Description of the accounting

procedures used for tracking

and/or allocating school-level 

expenditures

b. [If publicly available:] Is this information frequently accessed or requested? Who uses these

data, and how? Who uses the reports, and for what purposes?

14. We understand you have to report information each year to the federal government for the U.S.

Census Annual Survey of Local Government Finances-School Systems (the F–33 Form) and the Civil

Rights Data Collection (CRDC), and that you recently also had to report information through the

ARRA data collection. We understand that the information requested for both of these data

collections included school-level personnel salaries for instructional staff and school-level nonpersonnel expenditures. For each of those data collections, where did the data reported come

from? Which system (e.g., payroll, expenditures) is this information pulled from? Who is

responsible for generating these data?

15. Is there anything we haven’t talked about that would help us to better understand your state’s

school-level expenditure reporting system? Do you have any advice for other states considering

the development of such a system?

Thank you for your time!
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 97

Feasibility Study on Improving the Quality of School-Level Expenditure Data

Interview Protocol for District Budget Officers

District: Interviewer:

Interviewee Name: Date/Time:

Interviewee Title: Interviewee’s Role:

Introduction

Thanks again for taking the time to speak with me this morning/afternoon. Before we start, I’d like to

provide a little background on our work, and answer any questions you might have for me.

I work for an independent not-for-profit research organization called the American Institutes for

Research and we are conducting a study for the U.S. Department of Education to investigate how state

and district data systems are currently tracking expenditures to schools in an effort to improve the

quality of these types of data. You are one of four districts participating in this study.

We want to learn more about the structure of your school-level expenditure reporting system, what

types of expenditures you track or allocate to the school level and how, and how the process of creating 

a system that allows you to do that was implemented in your district.

Everything that we discuss here will be used for research purposes only. It would help us with note

taking if we could record this conversation - would that be ok with you? I can turn the recorder off at

any point by your request.

If I ask you a question for which you think you are not the best respondent, it would be helpful if you 

could refer me to one of your colleagues who would be able to provide the answer.

Do you have any questions for me before we start?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 98

1. First, can you tell me a little about your position and role? What are your responsibilities? How long

have you been working in [district], and how long have you been working with school expenditure

data? In what capacity do you work with these data?

2. We’d like to learn about the history of how your district initiated the collection of school-level

expenditure data. You mentioned on the pre-interview survey that you began tracking expenditures

to the school level in [YEAR].

a. How did this policy originate? Were any specific individuals or organizations active in proposing

or advocating for this policy? If so, who? Was there any opposition? What were the reasons

for supporting or opposing this policy? How did any disagreements over the new policy get

resolved?

b. Were specific legislative or regulatory changes enacted to establish the collection of school-level

expenditure data? If so, please describe these changes.

c. What first motivated your district to create a system to track expenditures at the school level?

Are there specific goals or objectives that the district hopes to accomplish through use of these

data?

3. You indicated in the pre-interview survey that [______________] were challenges for your district in

implementing or changing the financial data collection and reporting system. Which was the

greatest challenge and why? How did you go about addressing the challenges you were able to

overcome? For the challenges that are still being faced, why is this the case?

4. You mentioned that [_______] are the most challenging types of spending for you to report at the

school level. Why is this? How did you go about addressing the challenges you were able to

overcome? For the challenges that are still being faced, why is this the case?

5. We’d like to know more about how your systems were developed or modified to track or collect

school-level expenditures. You told us on the pre-interview survey that you [developed the schoollevel accounting system in house/purchased the system from a vendor]. How did you make that

decision, and who was involved?

a. Do you use contractor support to implement the data collection and reporting system(s)? If so,

what are the contractor’s specific responsibilities?

b. [If system was purchased from a vendor:] How long did it take to acquire the system from the

vendor? How did that process work?

c. [If system was developed in house:]Tell us about the process of developing the accounting

system in house. What was required? How long did it take?

6. Thank you for the information you provided regarding the staff who are involved in collecting and

cleaning data for the school level expenditure data systems. Were these staff from different sites or

departments, or the same one? Which would you say are the essential staff roles involved in

developing and administering a school-level expenditure system?

a. Thinking back, what staff were involved in acquiring or developing the new system? Do you

have any documentation of the process your district went through in acquiring and developing

your fiscal system that permits you to track school level expenditures? Can we obtain this

documentation for our records and review?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 99

7. We are also interested in non-staff costs of administering your school-level tracking system. What

were the approximate monetary costs involved in developing your school expenditures data

system? And what are the monetary costs now, of implementing it day-to-day? Do you have any

documentation or analysis of these costs you could share with us?

Probes:

a. Contract for IT system development?

b. Software?

c. Hardware?

d. Contractor to provide training to staff?

e. Training materials/documentation?

f. Contractor support for collecting, compiling, cleaning, and reporting the data?

g. Other types of expenses?

8. We are interested in how your expenditure data systems link to other systems.

a. Do the fiscal data on salaries in your school expenditure data system represent an aggregation

of the data in the district’s payroll or personnel systems?

b. Are the two systems (the fiscal and personnel systems) linked in any way? If so, please describe

how they are linked. Does the personnel data system use the same coding structure as the

system that tracks expenditures (e.g., function, program, object and/or other codes)? Can

schools be linked across these systems using a common school identifier code or a crosswalk

between system-specific codes?

9. Does the district have any policies or procedures to ensure the quality of school-level expenditure

data? By quality, we mean consistency (i.e., that expenditures are tracked or allocated in the same

way in different schools, and that information gathered from different systems--e.g., fiscal and

payroll systems--would produce the same figures), completeness (i.e., that all expenditure

categories included in your school-level expenditure system are fully reported for all sites), and

accuracy? Are quality checks performed, and what do these consist of?

Probes:

a. Guidelines or instructions for schools/districts to use in entering data?

b. Training for districts? (through in-person meetings, webinars, conference calls?)

c. Technical assistance for districts?

d. Automatic checks in data entry system (such as maximum values allowed, or preventing a user

from continuing if a cell is left blank)?

e. Staff assigned to review incoming data?

f. Standard analyses/data checks done after all data are entered?

10. What challenges have you encountered in ensuring data accuracy, consistency (across data sources

and schools), and completeness at the school level?

11. How does your district use school-level expenditure data? For what purposes?

Probes:

a. Do you do any kind of analysis of school-level spending? Do you report any analysis back to

schools?

b. Could you share an example report with us?

12. Does your district make information on school-level expenditures available to the general public?
Exploring the Quality of School-Level Expenditure Data: Practices and Lessons Learned in Nine Sites 100

Probes:

a. What kind of information is made available to the public? How is this made available (online

and downloadable, or some other method)?

Available

online

Available

upon 

request

Made available

through other

means (specify)

Not publicly

available

Data files with total expenditures

by school

Data files with detailed

expenditures by school and by

expenditure category

Reports containing analysis of

school-level expenditure data

Description of the accounting

procedures used for tracking

and/or allocating school-level 

expenditures

b. [If publicly available:] Is this information frequently accessed or requested? Who uses these

data, and how? Who uses the reports, and for what purposes?

13. We understand you have to report information each year to the federal government for the U.S.

Census Annual Survey of Local Government Finances-School Systems (the F–33 Form) and the Civil

Rights Data Collection (CRDC), and that you recently also had to report information through the

ARRA data collection. We understand that the information requested for both of these data

collections included school-level personnel salaries for instructional staff and school-level nonpersonnel expenditures. For each of those data collections, where did the data reported come

from? Which system (e.g., payroll, expenditures) is this information pulled from? Who is

responsible for generating these data?

14. Is there anything we haven’t talked about that would help us to better understand your district’s

school-level expenditure reporting system? Do you have any advice for other districts considering

the development of such a system?

Thank you for your time!

The Department of Education’s mission is to promote student achievement and preparation 

for global competitiveness by fostering educational excellence and ensuring equal access.

www.ed.gov