The Pennsylvania State University

The Graduate School

College of Education

OBJECT LESSONS: EXAMINATION OF SPENDING

AND REVENUE PATTERNS IN PENNSYLVANIA

K-12 PUBLIC SCHOOL DISTRICTS:

1998 THROUGH 2008

A Dissertation in

Educational Leadership

by

Timothy James Shrom

© 2013 Timothy J. Shrom

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2013

i

The dissertation of Timothy J. Shrom was reviewed and approved* by the following:

William T. Hartman Professor of Education Dissertation Advisor Chair of Committee

Preston C. Green III Professor of Education and Law

Roger C. Shouse Associate Professor of Education

Edgar P. Yoder Professor Extension Education

Gerald LeTendre Department Head

*Signatures are on file in the Graduate School.

ii

Abstract

This study analyzed changes in school district expenditure spending patterns to observe how major educational policies, initiatives, and mandates translated or were reflected by school district expenditure accounting. The target population was all 501

Pennsylvania school districts from 1998 through 2008. Within that time period, district expenditure patterns were examined focused at the major Object and Function series of accounts. Object codes by design capture strong indicators of expenditure traits.

Expenditures changes over time observed in each object’s share of total expenditures, the share of new funds it commanded, and the direction of share growth or decline. Mandates and state policy may have expenditure impact on districts by design, default, or by unintended consequence. Analyses revealing policy lead and lag times, as well as trend direction and strength may provide valuable insight for understanding component flows of school district expenditures. The confluence of a decade of increasing mandates, major policy implementations, and student outcome accountability measures met headlong into the great 2008 recession. There remains a tremendous amount of legacy costs facing school finance funding systems, including multi-year labor contracts, pension liabilities, contractual health care obligations, post-retirement benefit packages, debt service and capital requirements for infrastructure and major equipment needs.

There is little indication that districts or states have taken time to fully analyze impact and adjust to post recession spending patterns for school funding policy. To that end, this study observed and analyzed educational expenditure and revenue trends over a decade and investigated linkages to public policy initiatives that were occurring over the time period. Understanding these past trends, inclusive of trend strength and direction, serve to provide well-grounded perspective to vision post-recession policy issues. iii

Table of Contents

Chapter 1. INTRODUCTION 1

A Decade past 4 Purpose of the Study 6 Research Questions 7 Logic and Rationale for Approach 9 Focus on the Object Dimension 10 Policy Impact 13 Pennsylvania Background 15 The Study Years 17

Chapter 2. REVIEW OF LITERATURE 18

Bounding 18 Expenditures 19 Major Object Dimension 19 Function Dimension 21 Account code Strings / Linkages 22 Revenues 24 Local Revenues 24 State Revenues 24 Federal Revenues 25 Other Revenues 25 General Background 25 The Governors 25

Legislation 26 Property Tax / School Reform Legislation 26 Pension Legislation 27 Charter School Legislation 31 Electric Deregulation Legislation 33 Fund Balance Legislation 34 Gambling Legislation 36 No Child Left Behind Legislation 37 Enrollment: Average Daily Membership 38 Spending Patterns and Changes 39 Policy Linkages 45 Policy overview: Expert Opinions 47 Collection and analysis of Expert Opinion 47

iv

Chapter 3. RESEARCH DESIGN AND METHODOLOGY 50

Introduction 50 Approach to Research 50 Complex Systems 50 Data Specification 52 Expenditures 53 Object Dimension: Data Collected 53 Function Dimension: Data Collected 54 Revenues 56 Revenues Dimensions: Data Collected 56 District Characteristics 57 Market Value / Personal Income Aid Ratio (MVPI) 57 Calculations 59 Other Data 60 Pension 60 Major Policy / Legislative Implementation 61 Analysis of Spending Patterns 62 District Characteristics 63 MVPI 64 ADM 65 Policy Context 70 Policy Timelines 70 Interviews 71 Education Policy Makers: Review and Interpretation 71 Selection 71 Scheduling 72 Prior Provision of Data Trends 72 Method of Collection of Interview Answers 74 Organization of Interview Information 75 Analysis of the Interview Data 76

Chapter 4. DATA ANALYSIS 77

Data Analyses 77 Introduction 77 Summary of Findings 78 Object Share Trends 79 Analysis by Object 82 Salaries: 100 Object Series 83 Benefits: 200 Object Series 86 Professional Services: 300 Object Series 88 Purchased Services: 400 Object Series 90 Other Purchased Services: 500 Object Series 92 Supplies: 600 Object Series 94 Equipment: 700 Object Series 96

v

Other Objects: 800 Object Series 98 Other financing Uses: 900 Object Series 100 Statewide Object Summary 102 Analysis by Function 104 Major Function Analysis 104 Sub-Function Analysis 109 Function Share Trends 113 Instruction 1000 113 Regular Instruction 1100 114 Special Education 1200 114 Vocational Education 1300 115 Support Services 2000 117 Non-Instructional & Facilities Improvement 3000 120 Facilities Acquisition, Construction & Improvement 4000 121 Other Financing Uses 5000 122 Statewide Function Summary 123 Revenues 124 Revenue Share Trends 126 School Characteristic: Object analysis 132 Average Daily Membership 132 ADM Sub-Group Year-over-year Trend 134 Market Value / Personal Income Aid Ratio 139 MVPI Sub-Group Year-over-year Trend 144 Interviews 146 Summary 149

Chapter 5. PATTERNS REVEALED, TRENDS EMERGED,

AND CHANGES OBSERVED OVER TIME 152

Object Expenditures 155 100’s Salary 155 100’s Summary and Linkages 159 200’s Benefits 162 200’s Summary and Linkages 166 500’s Other Purchased Services 168 500’s Summary and Linkages 170 900’s Other Financing Uses 173 District Characteristics 176 Size of District 176 Wealth of District 179 Characteristic Summary 180 Key Events and Policy Impacting Trends 181 Pension Policy 182 Fund Balance Policy 184 Debt and Borrowing 185

vi

Charter Policy 186 Tax Reform 188 Administrations 189 Other Events / Non-Events 192 Revenue 193 Implications 195 Lack of Policy 196 The Next Five Years 198 Future Studies 199 Summary 200

Appendix A: Figure Listing by Page 204 Appendix B: Table Listing by Page 208 Appendix C: Miscellaneous 210 Appendix D: Object / Function Definitions and Policy Diagram 213

References 218

vii

Chapter 1

Introduction

In the post-recession era, Pennsylvania school finance structures for K-12 public

education remain unsettled and without clear policy direction. Pennsylvania is not

unusual as across the United States daily reports abound of school funding cuts, state

deficits, staffing furloughs, growth in court and legislatively mandated programs, and

losses in revenues attributed to the recession. (e.g. Aaron 2008, districts cut back on

busing and seek ways to save energy; and Lewin and Dillon 2010, districts warn of deep

teacher cuts; and McNichol, Oliff & Johnson 2011, states continue to feel recession’s impact). Meanwhile, Pennsylvania school districts and states increasingly face growing costs from a battery of areas including underfunded pension funds, unemployment deficits, infrastructure replacements, Medicaid and health care costs, special education

placements, energy, supply, and equipment costs (Davare 2008).

Federal allocations for school entitlement programs as well as accountability

measures of No Child Left Behind (NCLB) remain at the center of major national debates

for reauthorization of federal education funds. A significant force behind school districts’

efforts the past decade remains the drive to meet requirements of NCLB and associated

state standards for student outcomes. Concurrently, over the last decade, public K-12

education faced many challenges, including increased federal and state mandates, policy directives, and increased service demands for accountability from constituency. While it is the most important aspect of education, instruction is not the only area in which

schools must function. Non-instructional areas, such as instructional support functions,

1

facility operations, administration, transportation, and capital improvements are

important areas to address in achieving the educational goals of the entire enterprise.

A build-up of a decade of increasing mandates, major policy implementations, and student outcome accountability measures met headlong into the great 2008 recession.

There remains a tremendous tail of legacy costs facing school finance funding systems, including multi-year labor contracts, pension liabilities, contractual health care obligations, postretirement benefit packages, debt service and capital requirements for infrastructure and major equipment needs. There is little indication that districts, or state for that matter, have taken the time to fully analyze impact and needs of school funding policy as we move forward in the post-recession era (Hess and Downs, (2010).

Public education finance takes place within increasingly complex systems and ever changing policy directions. Complexity in this educational environment has grown in areas such as funding allocation formulas, school choice/competition initiatives,

political priorities, legal and labor arenas, school governance and referenda, student

testing requirements, staff certification criteria, and a host of mandated special education

requirements. Along with the increasing quantity and costs of dealing with these complex

systems, revenues and expenditures to support these challenges and requirements also

increased substantially. Now well into post-recession and ARRA funding, K-12 education in Pennsylvania struggles to deal revenue losses and expenditure pressures.

It is difficult to predict where all this is going as complex systems will interact in unexpected ways (Sargut & McGrath, 2011). Therefore, it is valuable to look back at the past to better understand school finance trends, to investigate where we were, and evaluate patterns and direction over time. More specifically, the focus in this study is to

2

look back at selected expenditure dimensions to see where school districts spent their

money, improve understanding as to why it was spent there, and understand from where that money came.

Why would this matter? Simply put, the answer is inertia and directional

steerage. School district major spending patterns are much like a large ship at sea: they are hard to turn. District spending occurs within many complex and changing environments. Additionally, the impact on any given district (and by extension schools) from internal or external forces (e.g. legislative, educational programs, Federal, State and

Local boards of education, etc.) may be different based on spending structure variances between districts. Sargut and McGrath (2011) state that “in a complex system, the same

starting conditions can produce different outcomes, depending on the interactions of the

elements in the system.” p.70

As the 2008-09 school year progressed, recessionary impact on Pennsylvania district revenues (mainly State) was mitigated in public education by $735 million

federal ARRA funds which were applied to fiscal years 2009-10 and 2010-11

respectively (ending June 30, 2011). At the end of the 2010-11 fiscal year, the

Commonwealth of Pennsylvania faced a $4 billion deficit as a new governor (Governor

Corbett) took over the statehouse. As federal ARRA funds disappeared, public education

funding was reduced by just short of a $1.0 billion for the 2011-12 fiscal year. Facing increased costs well beyond available revenue sources, school districts have implemented severe expenditure curtailments inclusive of staff furloughs, various spending or program cuts, and hiring freezes across the state (PASBO Study Report September 2011).

3

Program reduction and staff furloughs may quickly reveal certain impacts such as

class size increases as staffing is visibly reduced. However, it may take many years to

fully surface all implications and unintended consequences of major policy mandates,

deep funding cuts and the corresponding impact on school district spending reductions.

Given the decade prior to the 2008 recession, it is possible to look back and search for

significant trends, patterns, and perhaps unintended / unanticipated consequences of past

actions which lead us to where we are. Improved analysis of where we were and where

we are may provide significant insight for policy formulation and school finance moving

forward.

A decade past

For over a decade, the increasing magnitude of expenditures in education has

been supported by significant increases in funding from local, state, and federal sources.

This raises two basic questions for study in educational finance and policy: 1

1. What resources have been acquired with the funds?

2. For what purposes has the money been expended?

These general questions require more detailed analyses that are the focus of this study.

The analyses will take advantage of several different types of expenditure data to examine issues from several directions.

Generally there are two major approaches to measuring resources used in K-12 education. While there is variation within each, one relies more on detailed information about the jobs and assignments of individual school personnel, and the other relies

1 The most important question, which is beyond the scope of this study, is what student outcomes have been achieved as a result of resources allocated. Another important issue, also not a part of this study, is the cost-effectiveness of these expenditures.

4

heavily on the traditional accounting data (Chambers 1999). This study approach utilizes the latter, with the goal to provide information from existing accounting data on trends and spending movements at a more global perspective. State accounting systems, as well as district accounting systems collect and maintain expenditure data along multiple dimensions (PDE Manual for School Accounting, 2007). Two of the most important dimensions are the Object and the Function.

The Object is the service or commodity obtained as the result of a specific expenditure; examples of objects would include salaries, benefits, professional services, supplies, etc. The Function generally relates directly to the purpose for the expenditure.

Examples would include expenditures for instruction, non-instruction, administration, or ancillary support function costs.

When used in combination, the function and object account code structure will identify instructional salaries from non-instructional salaries, or expenditures for instructional supplies from those for administration. School districts code expenditures along these dimensions to maintain a record with diverse spending information. The

Commonwealth requires various minimal requirements of standardized educational fiscal reporting along these dimensions. Consequently, with the coding system it is possible to determine total district salaries by summing all expenditures that have an object code which specifically represents salaries, regardless in which function they occur.

Additionally, since we can look at these data over time, it is possible to observe how and where spending patterns have changed. At any given time, expenditures and revenues within a district are trending in observable ways (e.g. up, down. level, erratic).

Trend patterns and directional strength of that pattern may impact effectiveness of new

5

policy mandates or initiative implementation in a district. These structures in spending patterns will also affect validity and reliability issues when comparing financial data across schools or districts, as district spending patterns are often so unique they will create specific comparability issues. Finally, a district’s spending patterns may well serve to impede or enhance new program and policy implementations based on resource needs for the mandates. If an implementation requirement is contrary to existing district fiscal patterns and direction, it may provide an indication as to district ability to implement as much inertia may have to be overcome.

An example here is a state-wide mandate to implement a major technology infusion into the high school instructional environment. Districts that spend heavily in contracted services for technology support and training may have an advantage over districts that have an extensive internal technology support staff on payroll. A contracting service model may allow the district to quickly alter approach and adapt to the new mandate, while a district with robust in-house technology staff may find it difficult to unwind vested interests, re-train staff, and implement change.

Purpose of the Study

The focus of this study is to analyze school district spending patterns across the state, and to study how major educational policies, initiatives, and mandates translated into or were reflected by school district expenditure choices. The target population was all 501 Pennsylvania school districts from 1998 through 2008. Within that time period, district expenditure patterns were examined using the major object and function series of accounts. District revenue patterns were also reviewed according to their major source— local, state, federal, and other. Historical trends of both expenditures and revenues were

6

studied to determine if there are discernible relationships among spending trends and revenues. Overall, the research questions were divided between those which deal with school district data directly and those which address policy and policy implications.

Research Questions:

1. What spending patterns were observed?

a. For objects of expenditures

b. For functions of expenditures

c. What did the mean district look like?

d. What was the range of those patterns and who were the outliers?

2. How did those spending patterns change over time?

a. From 1998 to 2008 what trends were be observed?

b. For the first half of those years

c. For the half years of those years

d. Which objects changed the most (in terms of spending allocations) and in

which direction?

i. By dollars

ii. By percent share and share of change

e. Which functions changed the most (in terms of spending allocations), and

in which direction?

i. By dollars

ii. By percent share and share of change

3. Where have “new” (local and state) revenue come from to support increased

spending and changed spending patterns?

7

a. What changes in revenue streams were observed?

i. By dollars

ii. By percent share and share of change

b. From 1998 to 2008

c. For the first portion of those years

d. For the most recent years

4. Did school district characteristics appear to impact spending or revenue patterns?

Characteristics such as:

a. Wealth

b. Size

5. What events or policies offered key explanations of or appeared to impact

expenditure trends?

a. What were the major polices and events that occurred over the time

period covered by the study?

b. Did significant events in educational policies and revenues appear to

translate into changes in spending patterns?

c. Did school district spending patterns indicate different responses to

varying policy and school funding approaches?

6. What influence did major educational policies appear to have on school district

revenue flows from state and local levels?

7. What are the implications for school finance / funding policy?

8

Logic and Rationale for Approach

Certain costs are routinely driven by external factors beyond the control of most

employers and school districts. However, literature review suggests that costs are not the

only issues. Literature trends point not only to the issue of cost, but also to significant efforts to know for what funds are spent. Data collection/availability is lacking on many of these issues. Fuhrman (1994), in a study on issues and data needs in school finance,

said the following with regard to accountability implications for school finance: “But if,

as this author argues, policy makers are interested not only in performance, but also in

how other policies and resources link to performance, the data they will need should be

much broader”(p.42). These many years later, researchers have a lot of data on costs;

specifically how much is spent, but still struggle for reliable data on what expenditures

pay for.

School finance research takes on many facets in topical areas such as resource

allocation, equity, adequacy, funding methodologies, and linkages to student outcomes.

Little research is available on how the structural spending patterns within object series

may be impacted over time. While the object level focus serves to narrow the study,

Pennsylvania school district expenditure data available for analysis over time is robust.

Economies of scale do matter on certain issues and within certain given

frameworks; however, more of the same piled onto something smaller just makes it

bigger, and it is just as likely to amplify problems of the current system. Expenditures

and budgets have grown, but there is little to inform on where growth occurred and why.

Monk and Hussain (2000) conducted a study that examined the potential for inconsistent

9

resource allocation decisions to be made at different administrative levels of school districts and schools. They stated “Results indicate that there is more internal variation across districts in how teacher resources are distributed than is commonly supposed”

[Abstract]. While this study looks across the state for variation and spending pattern changes within the data, individual district spending pattern changes are also discernible

The object level dimension presents a valuable tool to expose variations of spending patterns across different organizational levels, as well as trends over time.

Focus on the Object Dimension

The accounting data focus of this study was on Pennsylvania district-level expenditures at the object dimension level. This provided a different and little used perspective to understand variations in spending structures. Additional analyses of expenditures by function and sub-function were conducted for comparison and for additional insight into spending changes. Understanding how resources are employed and in what mix often remains a missing link in evaluations of district or school spending trends. For example, changes in functional spending areas may indicate resource shifts from instruction to support services or from regular education to special education. In essence, this study and approach probes one level deeper below more common functional dimension reviews. By focusing on the object dimension to further inform, it can provide insight on what services and commodities were bought, indicate major policy implementations and reveal trend direction of impact. Delving one layer “deeper” into the accounting system utilizing existing expenditures records provides an alternative dimension beyond functional spending to follow the money. While this study did not examine the lower levels of detail of sub-objects and corresponding sub-functions, the

10

methodology can be generalized for subsequent studies at selected narrower focus,

including school-level finance research.

An example regarding an increase in the level of special education expenditures

illustrates how the object level expenditure analysis may provide program specific

information. Salary (100) and benefit (200) objects indicate educational services and

programs which are provided by the district itself (paid to district employees). Purchased

Professional Services (300) object indicate contracting out programs/services to outside

providers, such as intermediate service units, other school districts, private organizations,

or even independent contractors. Increases in special education expenditures that occurred in salary and benefit objects may indicate expansion of district-operated programs, while an increase in purchased services for special education may indicate

expansion of contracting special education programs and services. Such increases may be

indicated by actual dollar growth, by percentage growth, or by growth in the percentage

share of a total spending metric. Looking only at functional spending for special

education, we could analyze the gain or loss for that particular program and compare it

with regular education over the same time period. However, it would not be possible

without the object analysis to gain a better understanding of how the district or districts

may have used varied approaches to accomplish programmatic needs.

Improved understanding of these distinctions may help explain basic tenants of

districts’ program delivery and operating structures for which resources are utilized to

deliver requisite educational services, and, more importantly in this study, how the mix

among different objects (types of resources used) may have changed over time.

11

Object codes by design capture strong indicators of certain traits of district

expenditure decisions each fiscal year and over time will reveal directional trends.

Decisions to add staff versus utilizing contract services can reveal themselves in several

manners of change. Changes over time may be revealed in the weight of each object’s spending share of total expenditures, the share of new funds it commands, or by

percentage growth or decline. Mandated programs and state policy can have greater

expenditure impact on certain types of districts by design, default, or by unintended consequence. Further, when programs are operated in-house, expenses for supplies,

equipment, administrative support, salaries and benefits will be reflected not only in

different object dimensions, but programmatic fiscal reporting will be spread across

several different functional dimensions as well.

Continuing the special education example, an in-district special education

program requires additional support from administration, guidance, psychological and

social work departments, transportation, actual classroom space, and perhaps even

additional resources from the custodial and maintenance operations. Those costs are

recorded in different functional levels, and will be spread over instructional, instructional

support, and non-instructional cost functions. Conversely, similar expenditures reported

under contracted services for instructional programs contain many non-instructional

items within the service providers’ organization, but that single contract amount will be

recorded by the district as an instructional professional service. As a result, heavier

reliance on contracting services will tend to minimize the function and object accounting

allocation (e.g. a single coded source for an entire contracted program in instructional

services). This aspect embedded in the accounting procedures make comparing districts’

12

costs, and by extension school level costs, more difficult. This study approach mitigates

that particular problem by staying at the district level statewide.

Policy Impact

This study approaches school district-level analysis of major object spending dimensions over time, which not only can reveal trends of resource flows and movements, but also provide significant indicators for policy impact. Sub-object

(meaning cost allocations below the major object level) and school level trends are not targeted here. An example of a sub-object within the major object of salaries would be code identifiers at level to identify various sub-groups within salary expenditure such as professional, support, or administrative staff. Remaining at the major object level serves to limit and narrow the focus as well as guide it. This work does not attempt to translate policy impact with regard to student outcomes or whether student gains were made from a cost-benefit perspective of specific resource allocations. However, analyses revealing policy lead and lag times, as well as trend direction and strength may provide valuable insight for understanding component flows of school district expenditures.

In any given year, as a district operates one budget and prepares the next, there exists an entire culture and fiscal delivery system unique to that district. These systems are usually well entrenched and often act as a stable guide for budget planning; such systems are just as likely to act as barriers to change, or more importantly, actually resist change from policy exerted from district, state or federal levels. These systemic differences may serve to vary results of seemingly similar program and policy initiatives among districts. Indeed, it is expected that state-wide policy initiatives will impact districts or be implemented by them differently. This study uses the object dimension

13

trends over time to provide significant insight to anticipate, and perhaps even mitigate,

undesired impact of new policy initiatives.

Policy impact on school funding and expenditures must by nature be studied post-

implementation of a specific major event or legislative initiative. School finance

litigations for equity or adequacy are a good example. Within the various states, court

challenges to school finance funding methodologies and studies on funding adequacy or

equity abound. Court cases from Serrano (1971) in California, McDuffy (1993) in

Massachusetts, DeRolph (1997) in Ohio and the series of Abbott cases in New Jersey are

a few notable examples where arguments were made that more equitable or adequate resources will lead to improved quality of student outcomes. While such arguments may

be logical, there is much disagreement over any resulting quality impacts (Card and

Krueger, 1992; Hanushek, 1996: Betts 1996).

In the Suffolk Superior Court in Massachusetts, the judge found that the state had not met its responsibility to provide sufficient funding to bring students in poor districts to the standards of equitable education (Botsford, 2004). While this case is not necessarily unique, even in today’s school finance environment, it illustrates that the actual impact of public policy over a long period of time is difficult or impossible to predict (Odden 1999). The Massachusetts case was filed in response to a major tax

reform initiative that had been legislated many years earlier to improve the prior system

by mandating local tax reductions, but had significant implications for school funding in

the state. The lesson from their work is that it can take many years for policy impact to

play out and be revealed in historical data analyses. Assuming such analyses actually take

place.

14

While court cases such as these are typically high profile, it raises the question about impact on school finance from other policy actions which may or may not have been more subtle in implementation. Indeed, a policy or major initiative may have none or a tangential relationship to K-12 education, yet still have direct impact. To that end, this study observes and analyzes educational expenditure and revenue trends over a decade and investigates linkages to public policy initiatives that were occurring over a similar time period.

Pennsylvania Background

Near the end of this study timeline, the Commonwealth enacted major school finance tax reform in 2006, known as Act 1 of 2006, which took effect beginning in school year 2006-2007. Act 1 has had a substantial impact on Pennsylvania school finance as it restricts property tax growth with indices capping the percent of property tax millage growth. Additionally, it also subjects specific taxing and fiscal decisions to public referendums. Over much of this study’s time period, school boards in Pennsylvania had unlimited tax authority to raise property taxes to fill in gaps between revenue and expenses as they funded educational programs and services in their community. While this unfettered property tax authority provided local boards with wide local flexibility, it also served to enable the Commonwealth’s General Assembly and Executive branch to pass mandates and policy dictates downstream with little regard for state funding to pay for those implementations. The Commonwealth’s school finance structure depends heavily on property tax authority at the district level, as well as other limited local funding options within the school finance mix. State revenues as a percent of total district revenues fell from 38.5% in 1997-98 to 36.3% in 2007-08 (Pennsylvania Department of

15

Education (PDE) Annual Financial Reports (AFR) 2008). Meanwhile, local revenue share of the total grew from 56.7% to 59.1% during the same time period.

The Act 1 tax cap indices are tied to the average of two different Federal and

State wage indicators to create a base cap index; the base is modified upward for poorer districts using district wealth measures to calculate an adjusted index for each district, which gives poorer districts some additional taxing capacity. At implementation for

2006-07 fiscal year, the base index was 3.9% and the district-adjusted indices ranged up to 6.3% for school districts, which was not particularly restrictive to achieve an adequate budget. However in the most recent (recession) years the Act 1 indices have dropped to historic lows correlating to the declining economy (see Figure 1.1 below).

Figure 1.1: Pennsylvania Act 1 Base Index History

The Act 1 Index as per Special Session ACT 1 of 2006 5.00% 4.40% 4.50% 4.10% 3.90% 4.00% 3.40% 3.50% 2.90% 3.00%

2.50%

2.00% 1.70% 1.40% 1.50%

1.00%

0.50%

0.00% 2006‐07 2007‐08 2008‐09 2009‐10 2010‐11 2011‐12 2012‐13

16

The study years

The years 1997 through 2008 were purposely selected for the study. While further discussion on selection will follow, these years cross over Governor Ridge and Governor

Rendell administrations which approached public education and related funding differently. Over the course of this time frame several a major policy changes were made for and within the pension system. Termination at 2007-08 was chosen for several reasons. The 2008-09 fiscal year was the first year impact for the state’s new Gambling

Money fund distributions which serve to distort local and state share funding trends. The

‘beginning’ of recessional impacts the 2008-09 fiscal year and that is followed by two fiscal years (2009-10 and 2010-11) of even greater distortions from $735 million in federal ARRA funds. Finally 2011-12 saw public education funding reductions approaching $1.0 billion just as the ARRA funds ran out as well. Trending expenditure comparison for years beyond this study will require significant attention to detail for many complex distortions across the state.

It is an important distinction to note, that the fiscal years within this study are predominantly pre-Act 1 and tax rate authority for districts existed in a different governance and policy environment than districts presently face.

Boyd (2002) notes that chasing the dollars are a bit like solving a mystery and this research approach and design hoped to discover and examine significant clues along the way. When one is looking at resource use and chasing dollars and changes over time,

Hanushek (2007) noted “What matters most are the ways in which the available resources are used” (p.12). Further, he goes on to note that school finance cannot be divorced from school policy.

17

Chapter 2

Review of Literature

The focus of this study was to analyze school district spending patterns across the state and to investigate how major educational policies, initiatives, and mandates translated into or were reflected in school district expenditure trends. The target

population was all 501 Pennsylvania school districts from 1998 through 2008. Within

that time period, district expenditure patterns were examined using the major object and

function series of accounts. District revenue patterns were also reviewed according to

their major source—local, state, federal, and other.

The purpose of this chapter is to provide the reader with sufficient context to

understand the backdrop in which the expenditures and revenues analysis occurred, as

well as the conceptual framework utilized in approach.

Bounding

When taken in total, there is a staggering amount of school finance data available

for the targeted population over the time period, so the study had to be bounded to

provide focus. To accomplish this, the study concentrates on district financial data at the

major object level. Object expenditures were analyzed from multiple perspectives for

state-wide trends over time. While individual district data were used, the purpose was not

to delve into specific individual district trends nor attempt to link expenditure inputs with

student outcomes. This focus is at a state-wide level in which the results of all 501

school districts in Pennsylvania were aggregated and reported at the state level.

This chapter is divided into three major sections to provide a solid grounding into

the setting and data-rich environment into which this study delves:

1. Review and understanding the school finance data to be used 18

a. Expenditures

i. Major Object

ii. Major Functions

iii. Selected sub- functions

b. Revenues

i. State

ii. Local

iii. Federal and Other

2. General Background: Review of known and visible major policies and

events

a. Timeline including pre and post study periods

i. Timeline selection for the study

b. Overview and description of known major polices and events

3. Review of the academic literature to provide guidance for both school

finance analysis and improved understanding for discerning and

investigating policy implications

Expenditures

Major Object Dimension

The object dimension relates to the service or commodity obtained as the result of

a specific expenditure. In Pennsylvania there are nine (9) major object categories, each of which is divided into sub-objects for more detailed accounting. Object spending is a required component for state reporting purposes; however, in Pennsylvania, object reporting is mandated at the major object level, and sub-objects are collected only for

19

selective functional areas. For the purposes of this study expenditure review remained at

the major object level. The state accounting manual identifies nine major object

categories, each with a specific identifying accounting code:

100 Personnel Services – Salaries

200 Personnel Services – Employee Benefits

300 Purchased Professional and Technical Services

400 Purchased Property Services

500 Other Purchased Services

600 Supplies

700 Property (Equipment)

800 Other Objects

900 Other Financing Uses

(Manual of Accounting and Financial Reporting for PA Public Schools, 2007)

An example of coding distinctions that the object can indicate is provided by looking at two different district approaches to solving a special education instructional

service need. The input cost may be virtually identical as well as measured student

outputs. However, without understanding the basic approach differential, much may be

lost, or assumed in error, by any comparable review.

The first district may contract the service need in total to an outside agency which

would be paid (and reported) in the (300) professional service object. The second district

may decide to provide that service in-house, where by the expenditures will require

multiple object (and multiple function) allocations. District two will need to expense staff

costs in the salaries (100) and benefits (200) series, as well as buy their own supplies

20

(600), equipment (700), provide space, utilities, and maintenance and upkeep. Looking at these data over time allows trend observation of the object codes which reveal overall growth or loss in share of spending from any given selected starting point. To complete the example, trends towards more contracting or less, more salaries or less, benefit cost impact, greater reliance on purchased services or less reliance, can be trended for each district as well as for overall state-wide patterns.

Function Dimension

The function dimension describes activities for which a service or material is acquired. Major functions are classified into five broad areas:

1) Instruction

2) Support Services

3) Operation of Non-instructional Services

4) Facilities Acquisition, Construction and Improvement Services

5) Other Financing Uses.

Functions consist of activities that have the same general operational objectives.

For further detail, functions are often subdivided into sub-functions. For example, the function for Instruction is broken down by sub-function programs that are directly tied to instructional activities. (e.g., regular education, special education, vocational education)

Support Service consists of areas such as transportation, pupil personnel services, library services, administration, etc. Construction of the functional coding structure beyond the sub-function classification is based on the principle that the classification of activities should be combinable, comparable, relatable and mutually exclusive (Manual of

Accounting and Financial Reporting for PA Public Schools, 2007).

21

Account Code Strings / Linkage

For state accounting purposes, there are a series of codes beyond the function and object series of account codes, and it is important to understand the manner in which the codes may be linked to provide data. This assists the reader understand the limitations of the “level” of this study, as well as the level of detail state accounting codes may provide.

A full account code string will start with the Fund (e.g. the general fund, the capital reserve fund), and then include, Function, Object, Funding source (e.g. Federal),

Instructional organization (e.g. Elementary, Middle, and Secondary), Operational Unit

(e.g. School), Subject Matter, Job Classification, and perhaps a Special Cost Center tag.

For state reporting, only the Fund, Function, Object, Funding Source, and the

Instructional Organization dimensions are required for the state’s Annual Financial

Report (AFR) as shown in the illustration below.

Fund Function Object Funding Source Instructional Organization XX XXXX XXX XXX XX

X = Digital Coding Fund, Function, Object, Funding Source, Instructional Organization

Table 2.1 lists function and object codes side-by-side. Both function and object

codes are used in combination when classifying and recording a single expenditure. For example, an expenditure in the 1000 instruction series would reflect not only the 1000 instructional level, but would have an object code “attached” to it which serves to detail the expense further. An account code reflecting 1000-100 would indicate instructional salaries while 1000-600 would indicate instructional supplies.

22

Table 2.1 Function and Object Codes

Each function and object is “linked” when recording a district expense.

Object codes Function codes

Object 100 Function 1000 Personnel Services – Salaries Instruction

Object 200 Personnel Services - Employee Function 2000 Benefits Support Services

Object 300 Function 3000 Purchased Professional and Operation of Non- Technical Services instructional Services Object 400 Function 4000 Purchased Property Services Facilities Acquisition, Construction and Improvement Object 500 Function 5000 Other Other Purchased Services Expenditures and Financing Uses Object 600 Supplies Object 700 Property

Object 800 Other Objects

Object 900 Other Uses of Funds

For example, an instructional functional expense in “salaries” would create a linkage of the 1000 function and the 100 object and appear in a segment of an account code string as 1000-100. Greater detail in the codes would break this same concept down further by utilizing sub-functions and sub-objects.

23

Revenues

Local

Local revenue is defined as the sum of total taxes and all other local revenue.

Examples of major local tax sources are property taxes, earned income, realty transfer, and per capita or occupational assessment taxes. Districts may or may not utilize all tax options provided by legislative authority. Other local revenue is usually minor in comparison to tax revenues and examples are building use fees, interest earnings, and miscellaneous collections for a variety of small efforts or projects. For context, in 2007-

08, the statewide average percent of total district revenue was 59.1% for total local revenue.

State

State revenue originates from commonwealth appropriations and is directly disbursed to the school districts. State revenues can be ascribed to three significant components:

1. Basic education subsidy. This component represents the main mechanism by

which the state directs funding to districts for educational services, including

occasional categorical funding for specific state programs

2. Formula driven revenues associated with specific services (i.e. costs) such as

special education, transportation, debt service, and nurse services;

3. Revenues for generally one-half of the employer cost of social security and

pension payments (districts pay the total gross due (i.e.100%) and then get

reimbursed by the state).

24

Federal

Federal revenue originates from federal sources and is made available to the

districts through direct grants, entitlements, various state channels or other agency pass-

through (e.g. Intermediate units). Federal title I funding is generally the largest

component in this category.

Other

Other revenue includes proceeds from the sale of bonds or extended-term

financing, inter-fund transfers, receipts from other local agencies, refunds of prior years

receipts, and sale of or compensation for loss of fixed assets.

General Background

This study examined eleven (11) school fiscal years from 1997-98 to 2007-08 within the Commonwealth of Pennsylvania. A number of major policy initiatives occurred during the decade covered by the research. They are reviewed here to provide background and context for the study. A flow chart (See Figure 3.10) identifies the significant events and major legislation over time for pre and post study time frame.

These elements may have leading or lagging impact to school expenditures.

The Governors

During this time period there were three Governors in Pennsylvania: Tom Ridge,

Mark Schweiker, and Ed Rendell. Mark Schweiker held the post a brief two years after

Governor Ridge was appointed to head the Federal Office of Homeland Security in the

Bush Administration, so most of the policy initiatives and efforts occurred under the

Ridge and Rendell administrations.

25

The first-half of the study versus the second-half coincides with several

phenomena which most likely impact variances observed. It is not a secret that the Ridge

(Republican) administration (first-half) and the Rendell (Democrat) administration

(second-half) were at somewhat opposite ends of the spectrum with regard to public

education investment philosophy. In the first-half of the study Governor Ridge is

generally credited for educational policy which lowered employer pension contribution

and implemented a significant charter school processes (Act 22 of 1997). In the second-

half of the study, Governor Rendell is generally given credit for significantly increased

state funding to public education, gambling legislation with offset funds to district

property tax relief, and specific technology initiatives for high schools (Classrooms for

the Future (CFF)). However, suppressing pension contributions was a practice

purposefully continued under Governor Rendell as well.

Legislation

During the time frame, several legislative / executive implementations were

established with significant costs set up as direct pass-through to local district tax mix.

Some of these were targeted directly in the educational arena, and some were wider state-

wide implementations which had impact on districts. This section reviews selected major implementations which will aide in understanding background and provide additional

context.

Property Tax / School Reform Legislation

The state implemented several major pieces of legislation focused on limiting

growth of school district property taxes and restricting local board governance authority

(Act 50, Act 72, and Act 1). Act 50 and Act 72 both contained optional language which

26

required either school board self-election (i.e. opt in) or voter approval to implement changes in taxing structure and authority. Very few districts made the changes, and nearly all communities rejected the Acts’ optional taxing structure changes (i.e. an

exchange of an increased earned income tax for reduced property tax).

Act 1, the third major tax reform legislation in a succession (Act 50 and Act 72

predated Act 1) mandated property tax limitations on school districts for the first time in

Pennsylvania. Act 1 went into effect in the 2005-06 school year, but it influenced district

fiscal behavior in the 2004-05 fiscal year. With Act 1, districts who wished to raise their

property tax millage rates above an inflationary index either had to qualify for certain

exceptions or put the proposed tax increase to a public referendum. Immediately prior to

the Act 1 implementation date, a grace period was allowed for districts to prepare and

address old business which may have been in process prior to the new law. A major

consideration was that debt implemented prior to the Act’s effective date was exempt

from the property tax rate caps which were soon to be applied (e.g. debt schedules may

have required phasing over several years for funding needs). Districts in the process of

major construction work rushed to get bond resolutions in place, while many districts that

were not “in process” also placed debt resolutions on the books to preserve the exception

from referendum and borrowed funds soon thereafter. Since debt at this level is usually

incurred via general obligation bonds, these borrowings and impact remain on the books

for ten to twenty-five years, based on bond term.

Pension legislation

During the entire decade, changes in both the State Employee Retirement System

(SERS) and Pennsylvania School Employee Retirement System (PSERS) contributed to a

27

systemic trend of significant unfunded liabilities. SERS is the state’s system for State

employees, and generally operates similarly to PSERS. PSERS is a defined benefit plan

where the retirement benefit is determined by a statutory formula that included the employee’s final average salary, years of service, and a multiplier. In 2001, under

Governor Ridge, the PSERS multiplier (for school employee retirement) was increased to

2.5% from a long standing 2.0%, or a 25% increase. This action took place while the actuarially rated funding level of the pension fund was at 114.4% (PSERS 2004

Comprehensive Annual Financial Report). Subsequently, state mandated reductions of employer funding levels as well as significant losses in the market place have dropped the funding level below 60% (PSERS October 2011), creating a significant unfunded liability.

In recognition of that liability in 2010, Governor Rendell signed house bill 2497 into law as Act 120. This legislation preserved all benefits previously in place, but mandated a number of benefit reductions for future PSERS members hired after July 1,

2011.

As indicated above, the General Assembly of Pennsylvania deferred pension liabilities several times over the past decade. These actions basically “re-mortgaged” current liabilities via legislative action, which artificially lowered the Employer

Contribution Rate (ECR) and reduced the amount that the state and employers were required to contribute to the fund. Both governors signed on to these actions, if not willingly encouraged or led the process.

Employees contribute a direct share to PSERS at rates ranging from 5.25% to

7.5% depending on hire date. These rates may appear low compared to some state

28

pension systems; however, Pennsylvania does differ from many states in that pension

system members are also participating (and contributing to) in social security. Future

pension liabilities continue to grow with projected ECR rates increasing nearly threefold from the 8.65% paid in 2011-12. PSERS directly impacts school districts as they generally contribute 50% of the employer share of funds with the state paying other 50%.

Districts actually pay 100% of the ECR and then the state reimburses the district which appears in the state allocations for public education. SERS impact is indirect as payments needed for this fund is 100% state burden; therefore the impact is on state ability to fund all programs statewide. (See Figure 2.2 below)

29

Figure 2.1 - PSERS Historical Employer and Employee Rates

PSERS Total Employer Rate (Blue/Diamond) & Employee Avg (Red/ Square) as % of Payroll (**Projected) 35.00

30.00

25.00

20.0 19.9 19.7 **21.3 19.3 20.00 19.2 17.1 19.5 19.3 **16.9 15.00 16.0 14.2 14.9 15.0 11.7 13.2 10.6 12.3 10.00 11.1 7.1 8.7 8.8 6.0 6.5 5.00 3.8 5.6 4.6 1.9 4.7 4.8 4.8 1.1 4.2 1.1 0.00 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 13** 14** 15** ‐ ‐ ‐ 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

There are significant indications of counter intuitive policy forces when looking at pension issues within the time period. The ECR rate went from nearly 9% to 1.09%, and then back up to 7.13% during the study. For context, these rate variations create significant dollar variations as ECR (i.e. employer cost rate) is a percentage applied to total district payroll. The Ridge Administration presided over a significant increase in pension benefits for both the state and school employees in 2001 while at the same time

30

lowering the ECR. While facing known large and significant pension funding issues, the

Rendell administration lowered the ECR contribution rate twice (from what was

actuarially called for), while implementing educational funding programs which required

additional district spending in staff/salaries. For example, in 2010-11 the Rendell budget

enacted a reduction from the prescribed 8.22% ECR to 5.64%, while increasing basic

education funding substantially. More recently, pension liabilities were partially

addressed with Act 120 of 2010, which altered the PSERS system for new employees

hired after July 1, 2011. As part of that legislation, employer contributions were deferred again.

Major pension system policy had obvious impact, and pension liabilities remain

significant. Additionally, in the immediate post study years, 2008-09 and 2009-10, the

state again lowered the rates (to 4.76% of payroll from projected 7.13% in 2007-08)

amidst the 2007-08 economic downturns, and once again moving liabilities further out.

Presently under Act 120 of 2010 the Commonwealth and districts now face projected

actuarial rates within the next several years going from 5.64% in 2010-11 to over 21%

percent of payroll by 2014-15. The projected ECR out to 2030 remains steady in the mid-

twenty percent range (of payroll).

Charter Schools

Act 22 which passed June 12, 1997 provided for major charter school legislation

and made Pennsylvania the 27th state to provide for the establishment of charter schools.

The cost of charter school operations was passed downstream to be funded by the local

district in which the student resided. The Act was passed after three years of debate with

various pro and con advocacies. Not the least of the contentious issues was emerging and

31

growing Cyber charter schools. The Cyber charter school concept was generally not well vetted or fully anticipated when the legislation was being considered. After passage, costs

(to school districts) grew rapidly and the contentious debate continued. Act 88 of 2002 made a step forward to recognize that issue and provided a state funding component (up to 30%) to offset district charter and cyber charter costs. State funding levels to districts never reached the 30% level but generally averaged around 25% of the cost. The charter school funding formula renders a different amount for each school district to be paid to the charter school, even though several districts may be sending students to the same the charter school. Pennsylvania’s Auditor General Office (2010) conducted a special audit of the charter funding process and issued this statement in a September of 2010 report:

The Governor, the Pennsylvania Department of Education, the

Charter School Appeal Board, and local school districts should

place a moratorium on authorizing new charter schools until the

flawed charter school funding mechanisms are equitable and

reasonable for charter and cyber schools, and sending school

districts and for Pennsylvania taxpayers (pg.1).

In the report’s summary introduction, they state, “Overall, we concluded the

Pennsylvania’s current methods for funding charter schools are inequitable, inefficient, and bear no relationship to the actual cost of educating the students attending these schools” (pg2).

Charter school costs are recorded as tuition payments and recorded as a gross district cost in the 500 object expenditure series. The state reimbursed approximately

25% of charter tuition cost, with those funds showing up in district state revenues. The

32

state began posting Charter school spending data on its web site in 2005-06 providing

three years data for review in this study.

Electric Deregulation

Prior to 1996, the Commonwealth imposed via the Public Utility Realty Tax Act

(PURTA) a tax levy against certain entities furnishing utility services regulated by the

Pennsylvania Public Utility Commission or a similar regulatory body. The

Commonwealth imposed this tax on public utility realty in lieu of local real estate taxes

and distributed those funds to local taxing authorities across the state. Public utilities paid

PURTA tax directly to the state, which then distributed those funds to school districts

(and other municipalities) based on a formula which was predominantly biased to high taxing, high spending districts or municipalities. The Electric Generation Customer

Choice & Competition Act in December of 1996 provided for deregulation of the electric

generating industry in Pennsylvania. This Act set in motion changes to PURTA funding

and utility tax policy.

PURTA changes became effective January 2000, and altered how utility taxes are

paid. Specifically, it removed electric generation property from the PURTA tax rolls, and

moved those realty interests and corresponding assessed values directly to the local

school district and municipality property tax rolls. Individual districts and local

governments in which those generating facilities resided were now to receive those tax

funds directly (i.e. via property tax). Across the state, large utility companies immediately

filed assessment appeals to reduce or negate large and over-inflated “assessment” values.

For many years assessed values for electric generating plants were carried on county non-

taxable assessment rolls. Because PURTA did not use those values, those numbers were

33

not utilized in tax liability calculation. In essence, no one paid too much attention to what

those values were. Once those assessment amounts became the basis of an actual tax

levy, the utilities filed appeals and won significant reductions in tax liability. PURTA

funds, which had previously distributed revenues to districts and municipalities across the state, dropped by nearly $100 million from state revenue distributions. At the district and municipal level, this lost revenue was generally replaced with local revenue. Most major electric generating plants are located in rural areas, and as a result, some of the lowest wealth districts in the state needed to carry the majority burden of court costs in fighting and settling large utility appeals. But they also directly received the utility property taxes, which had been shared across the state. (Pennsylvania Association of School Business

Officials (PASBO) Task Force on Electric Deregulation, 2001).

Fund Balance Legislation

Beginning with the 2005-2006 school year, the Pennsylvania General Assembly regulated the amount that school districts may maintain as an undesignated fund balance. Act 48 of 2003 stipulated that a school district may not raise property taxes unless its undesignated fund balance is less than the percentages listed as follows

(Pennsylvania Fund Accounting Manual, 2007):

Table 2.2

School Code § 688 imposes limits on school district unreserved fund balances. A school district is prohibited from increasing real estate taxes unless its general fund budget has an estimated ending unreserved undesignated fund balance less than the percentage provided below.

School District Budget Size Fund Balance Limit  $11,999,999 12.0% $12,000,000 - $12,999,000 11.5% $13,000,000 - $13,999,000 11.0% $14,000,000 - $14,999,000 10.5%

34

$15,000,000 - $15,999,000 10.0% $16,000,000 - $16,999,000 9.5% $17,000,000 - $17,999,000 9.0% $18,000,000 - $18,999,000 8.5%  $19,000,000 8.0%

Fund balance rule changes and limitation caused many districts to increase

spending (i.e. spend down reserves), transfer funds, or set up capital reserve accounts

right around the mid-point of the study. This policy was designed to address concerns that districts had fund balance levels which were perceived to be too high. Generally, districts with fund balances in excess of the new limits implemented four actions to comply:

1) They budgeted for and then spent required funds above the cap in a rapid

spend-down

2) Declared specific reserves which would not count against the cap (e.g. for

health care costs or future pension costs)

3) Transferred funds to a Capital Reserve fund or other fund (outside of the

general operating fund) for later spending

4) Combinations of the above

Specifically with regard to transfers in item 3, those funds would have been recorded as expended under Object 900 (Other Uses of Funds, Fund Transfers) in the general fund, and perhaps then spent in a different fiscal year from a different fund. In those cases district general fund spending still would have revealed significant expenditure growth, while they actually may have simply been transferring funds.

Overall the legislation did reduce and limit district fund balances which are revealed in general fund reporting (Pennsylvania Department of Education (PDE) Annual

35

Financial Report (AFR) Files). In many cases the limits removed money from a district’s general fund (i.e. the operating account) which also served to reduce local revenue for interest earnings. Interest earnings in the general fund operating account directly served to reduce on the need for local property tax rates. Once these monies were transferred out or spent, those interest earnings were lost to the general fund. Investment rates in the

2003-04 fiscal years were at 40 year historic lows and loss of principal to invest exacerbates reduced local revenue due to lower interest earnings. Subsequently, in 2011-

12, investment returns are now at 50 year lows.

Gambling Legislation

Major gambling legislation (Act 71 of 2004) was enacted in 2004-05 which created the authority and process to introduce slot machine / casino gambling in the

Commonwealth. While quite complex in entirety, for districts the bottom line was that once gambling funds would be available, education (districts) would receive a set portion

(34%) of the proceeds. These funds were driven to districts from the state by a formula, and district shares were driven out to property owners as an offset to their tax via a homestead/farmstead reductions process. In essence the process utilizes districts as a pass-through entity for the purpose of tax relief. No funds were to be distributed after the initial legislation passed until a set “floor” of revenues had been “banked’ by the state.

Finally in 2008-09 enough “cash” was accumulated to fund property tax reductions to property tax owners (via a homesteads/farmstead process). These funds had no impact on the current study but certainly impact revenue trending moving from 2007-

08 to 2008-09. In any continuing review the sheer volume of the gambling fund distributions must be examined, accounted for and explained.

36

No Child Left Behind

Regardless of all other initiatives, the federal No Child Left Behind (NCLB) Act,

enacted in 2001, was a massively influential piece of legislation, and an example of

political dynamics in education policy (Guthrie and Wong, 2008). NCLB technically is an amendment of the Elementary and Secondary Education Act (ESEA), which is the vehicle used to distribute federal funding to states based on multipliers of spending levels and low income students. NCLB mandated student outcome performance targets in exchange for additional funding within ESEA.

NCLB was a prominent proposal in the first administration of President George

W. Bush, and it formally identified school choice as an option for school restructuring, which was consistent with Governor Ridge’ platform in the same era. The following discussion is a brief review of NCLB requirements and key timelines which align with the study.

States were required to adopt challenging academic standards that were to be

applied to all children. Such standards needed to specify what children are expected to

know and be able to do, and those same standards has to define what advanced, proficient and basic levels of achievement are. Enacted in 2001, NCLB required standards for math, reading, and language arts to be in place by July 1 of 2002, with standards for science in place for the 2005-06 fiscal year.

Beyond the standards, yearly academic assessments were required in math, reading, and language arts at least once between grades 3 and 6, grades 7 and 9, and grades 10 and 12. Beginning in 2005-06 annual assessments had to be given every year

37

between grades 3 and 8. Additionally all students with limited English proficiency (LEP) had to take an annual English proficiency assessment beginning in 2002-03.

Finally, an accountability component required states to develop a single statewide accountability system which applies to all public schools and students. The term

“Adequate Yearly Progress” (AYP) is used to establish the minimum level of

improvement a school district must achieve each year. Specifically, AYP is reported for

each school as a whole and then broken out by the following sub-groups: economically

disadvantaged, students from major racial or ethnic groups, student with LEP, and

students with special needs. Each state determined its level for sub-group criterion, in

Pennsylvania the number was 40.

Enrollment: Average Daily Membership (ADM)

One of the factors that influence both expenditures and revenues is the number of

children served by schools. Enrollment levels and enrollment trends provide general

information related to the demand for programs and services from school districts. As

shown in Table 2.4, over the time period of the study, total public school enrollments for

the state were essentially constant, with only slight variations in the number of students

served in public education. From 1997-98 to 2007-08, annual changes ranged from a loss of 8,000 students to a gain of 7,600 with a net enrollment growth of 2,301 students overall. The largest single growth year was in 2001-02 at 7,622. That particular year growth may well correspond to charter school legislative changes. Non-public school students who made the switch to public charter schools could have impacted the count.

According to PDE data from 1997-98 to 2007-08, statewide enrollment (ADM) growth was .13%.

38

Table 2.3 Public School Enrollment

Annual Percent ADM Statewide change change 1997-98 1,798,618 1998-99 1,802,244 3,625 0.20% 1999-00 1,801,960 (283) -0.02% 2000-01 1,799,769 (2,191) -0.12% 2001-02 1,807,391 7,622 0.42% 2002-03 1,809,699 2,308 0.13% 2003-04 1,813,309 3,610 0.20% 2004-05 1,810,569 (2,740) -0.15% 2005-06 1,813,222 2,653 0.15% 2006-07 1,808,946 (4,276) -0.24% 2007-08 1,800,919 (8,027) -0.44% Total change 2,301 0.13%

Spending Patterns and Changes

Utilizing an accounting approach, this research focuses its measures from data which provide dollars of expenditures. A substantial body of school finance research exist which concentrates analysis on changes in spending patterns over time. Much of this research is focused at the functional expense dimension, and / or attempts to study the equity of intra-district distributions or student outcomes (Moser 1998; Rubenstein, 1998;

Iatarola & Stiefel, 2003). Coopers-Lybrand (1994) applied the Coopers-Lybrand

Finance Analysis Model (FAM; model is also known as INSITE) to an analysis of New

York City schools. FAM’s analysis emphasis was generally at the district level with the purpose to drop to the school level and ultimately to the classroom level with ever increasing levels of cost delineation (Chambers, 1999). The advantage of FAM and work which probes into lower levels of district resource allocations, including school level, is that student outcome linkages may be able to be identified. The disadvantage is that allocated resources in schools may include billions of district dollars expended due to

39

total statewide policy implementations, and analysis at such lower levels will rarely be

able to reveal such impact.

Stoicescu and Hartman (2004) analyzed trends in state appropriations for

education in Pennsylvania from 1990-91 through 1999-2000. The results of that study revealed a decade-long decline in the state share of appropriations to the basic education subsidy when compared to total state appropriations. The decline, a total of 1.2% of the state’s budget, indicated that at the end of the decade public education would have received more than $259 million additional appropriations in 1999-2000 had it maintained the 1990 share. As their work shows, trends over time may involve small incremental changes that become both recognizable and substantial over multiple years.

This study looks at both expenditures and revenues to investigate such incremental changes utilizing a wider data set.

Meanwhile, reviews of major state-wide policy impacts from mandates and legislation over time remain limited. Such impacts may well serve to alter and disrupt district spending and revenue patterns which existed prior to any implementation.

Further, such disruptions may serve to obfuscate or even negate impact of basic education funding formulas and special initiatives including state or federal categorical funding. For example a new mandate may cost district A more than district B due to district circumstances. Meanwhile district A also faces a loss of state subsidy due a revised state formula for revenue distribution. Given one impact or the other, district A may be able to cope with the new circumstances, but both in combination could severely impact district ability to provide educational services. With proper diligence, such events may be readily predictable and; therefore, preventable.

40

Neymotin (2010), in a Kansas regression study, studied changes over 1997 –

2006, following the Kansas School District Finance and Quality Performance Act. While specifically looking for effect on student test scores, Neymotin notes that that the

availability of resources is not equivalent to the ability and means to use of resources to effectively to help students. There are various factors and key issues to consider when deciding upon improved policies for school funding. Issues such as parental involvement, teacher and administrative training/ability, poverty, pupil teacher ratios,

enrollments, represent examples of common factors which may be considered. More

significantly on point in Neymotin’s conclusion she notes:

Beside these factors, at issue is whether schools that actually

need and know how to use funds are the ones that receive them.

It is alternatively possible that funds are allocated in a way so as

to satisfy political exigencies rather than school district’s direct

concerns (p.107).

While Neymotin is not clear whether local or state level political exigencies are

weighted equally, one must allow that ‘other’ state-wide policy and mandate impact are missing from her list.

Fahy’s (2011) review of Massachusetts districts over time focused on the particular problem of the effect of overlay provisions on vertical and horizontal equity.

Overlays were Fahy’s term used to describe provisions which are often beset by political tinkering and other modifications. The focus in the Massachusetts study suggested policy implications which grow out of complex funding formulas and state aid which leaves

41

communities wondering why other districts seem to get better deals. Fahy goes on to note:

This sense of injustice is combined with confusion over the

complexity of the financing formulas resulting in a sense of

frustration with the system. Aid formula modifications help

appease constituents who are worried about getting their fair

share....the downside to aid modifications is that spending

becomes increasingly disassociated with the principles of

foundation budgeting, and equity across students is

compromised (p.242).

In contemplating policy implications noted in Fahy’s work, it is just as likely that policy tinkering at levels beyond a basic education funding formula may serve a detrimental impact, and perhaps at even broader scope and scale than generally assumed.

In the final report of the Seattle-based Center on Reinventing Public Education,

Hill, Roza, & Harvey (2008) note significant policy implications and impacts. The report summarized a six-year study of American School Finance systems which included thirty separate studies of over forty scholars nationwide. All this work leads them to note one conclusion: that school finance today works against the focused and efficient use of resources to promote student learning. They go on to note:

A basic flaw in these improvement efforts is that they look to

the education finance system for solutions when the system

itself is the problem. State education finance systems were not

designed with student learning in mind, nor have the

42

superintendents and principals who manage educational

resources been trained to make the strategic connection

between resources and learning one would expect in a learning-

oriented system. What’s more, because of the way these

systems operate, elected officials, educational leaders, and the

public are equally hard pressed to know how resources actually

have been deployed or the ways they may (or may not)

contribute to learning (p.3).

Slowly, improved understanding of how and on what school funds are spent

continues to flow from such growing bodies of research, which include specific school

level investigations (Roza & Swartz, 2007, Shrom & Hartman, 2008), to district, state

and national data sets. However, by their nature, the lower level allocations include many things mandated into the system by events beyond district control.

Liebschutz and Boyd (1996) conducted a review of state and local government

spending on public elementary and secondary education nation-wide from 1970 to 1996.

They reported spending grew six fold from $0.7 billion in 1970 to $156.3 billion in 1996.

When adjusted for inflation, the real per pupil spending increased 86 percent over the 25 year span covered. Their study reviewed enrollment shifts, regional (national) changes in spending at the total expenditure level. Within their report, they reviewed a Lankford and

Wyckoff study of the allocations of expenditure increases from 1980 to 1994 in both New

York City and New York State districts. Expenditure categories reviewed in the New

York study were focused at the function level with a few select sub-functions broken out,

43

while the Liebschutz and Boyd study remained at the total expenditure level. They concluded in their observations and review with specific questions:

 Will Special Education programs continue to grow rapidly, crowding out

growth in other programs?

 Will Education costs continue to grow more rapidly than prices in general?

 Impact of higher educational standards, capital needs, and technological

enhancements – How will all of these issues affect the bottom line cost of

educating children?

They went on to note in their outlook for the future: “Most of these factors will lead to higher costs, suggesting continued pressure on education budgets in the years to come”

(p.9). Since that report in 1996 most would agree their prediction of continued pressure on school budgets was well founded. Special education expenditures continue to exert pressures on all funding sources and existing program allocations. Research needs to continue to find improved ways to delve into why and where spending increases occurred, but not just at the school or district level.

Hannaway, McKay, and Nakib (2000) noted that the last decade has been a time of dramatic shifts in education in the United States. Their research targeted resource allocation patterns in districts and how they changed over time. Their data sources, known as the F-33, as well as the National Center for Education Statistics Common Core

Data (CCD), included an analysis of 11,622 districts. Over the period (1992-1997) analyzed districts nationally made only marginal increases in the fraction of spending that went into instructional areas. The largest increases were noted flowing into instructional support services which they attributed to special education cost growth and not standards

44

or accountability reform. Additionally, they report that besides special education, a

second expenditure which increased rapidly during the study was employee benefits which was attributed to the rise of health care costs in the 1990’s. Pennsylvania Local

Education Agencies (LEAs) are not alone as trended costs change over time and are reflected in national trends.

The Hanneway et.al. (2000) study was focused at the function level and analyzed money flows to instructional or non-instructional areas. They drop down to a single selected object level with a brief analysis of benefit expense and the linkage to health care costs. Interestingly their study predated general pension crises alarms. It should be

noted that over the time frame of the Hanneway et al. study, many states across the nation

had been lowering pension contribution rates, therefore states with shared (with districts)

employer pension costs may have mitigated annual benefit (i.e. health care) cost growth

at the district level by lowering pension costs. It is these same lowering of the pension

contributions over many years which have come back to haunt states and districts

sparking national debate on sustainability while facing massive unfunded liabilities.

Policy Linkages

It is observed that school finance research focus remains on how much and on

what districts are spending resources for. Meanwhile, there remains little knowledge of

external impact from regulatory mandates and policy which are by design incorporated

into lower level spending and structures and units of analysis. Kirst and Anyon (2005),

with regard to urban settings, argue that “macroeconomic policies like those regulating

the minimum wage, job availability, tax rates, federal transportation, and affordable

housing create conditions in cities that no existing educational policy or urban school

45

reform can transcend.” Ultimately, educational policy must address a range of policies

that have a direct impact on children, families, districts and schools, rather than only those issues narrowly aimed at education outputs, for example standardized tests.

William L. Boyd (2000), writing in the Journal of Education Change, observed

the following:

“In today’s social environment, public support for large and

socially inclusive public school systems seems in jeopardy. A

trend toward abandonment of the traditional model of public

schools appears increasingly possible in the United States”

(p.2) “So ironically, just as the public schools find themselves

with a much harder job, due to the combination of more

disadvantaged children and demands to educate all children to

a higher level, they see their public support dwindling” (p.5.).

This statement was written in September of 2000 and now it is two recessions and over a

decade later. Considering the state of public education in 2013, it is hard not to argue that the job is even harder, and that support remains questionable, if not in critical condition.

Boyd’s discussion includes alternative approaches to school reforms such as Incremental vs. Comprehensive or “Systemic Reform” and he frames these several issues stemming

from political struggles. Specifically, addressing the charter school movement and

support for voucher movements, Boyd notes those movements once seen as very radical

are now much more mainstream if not acceptable on many fronts. However, as he looks

back over time, it is the incremental add-ons, applied over several plus decades that

brought us to where we are today; so it remains to be seen whether there exists patience

46

for incremental fixes, and if fixes exist. As we study spending and revenue pattern changes, and attempt to understand why and what we are seeing, contextually, it is all

taking place within a myriad of state, federal and local policy climates, which are

mutually exclusive while yet simultaneously entangled together.

Policy overview: Expert Opinion

The focus of this study was to analyze school district spending patterns across the

state, and to study how major educational policies, initiatives, and mandates translated or

were reflected by school district expenditure choices. In so doing, this research needed to

explore a complex of value-laden observations and issues (e.g., Smith-Sebasto, 2000).

To accomplish this in part, expert opinion was utilized to help address complex issues,

establish a greater understanding and perhaps obtain new insights and interpretations.

Collection and Analysis of Expert Opinion

Dawson & Brucker (2001), state “the Delphi method was developed in the early

1950’s and 60’s by researchers at the RAND corporation as way to get expert opinions on

what the future may hold for fields of study, or to examine long range trends” (p. 126).

As rapid changes continue in public educations and school finance systems, it is rare that

experts in the various fields communicate in any meaningful way to solve problems. To

gain greater understanding of issues with regard to research questions, expert counsel and

opinion will be sought.

The Delphi and the Nominal Group Technique (NGT) represent two major small group decision-making processes, and while variations on each exist, there are similarities and differences between the two. In general the similarities are summarized by Delbecq, Van de Ven, and Gustafson (1975) as follows: both rely on independent

47

work for idea generation. The major difference between the two techniques is that

Delphi respondents typically are anonymous to each other while an NGT group typically

meets face-to-face. In Van de Ven and Delbecq (1971) they compare the NGT to

another small group process known as Interacting Group Process (IGP). The NGT seems

most effective for generating information and for fact finding concerning a given problem. The IGP helps to synthesize and evaluate alternatives and solution possibilities.

Van de Ven and Delbecq (1971) found the two techniques could optimally be used in combination. Both the NGT and IGP predominantly require respondents or participants

to meet face-to-face in at least one if not more meetings.

The selection of a Delphi approach over other small group techniques is

predominantly driven from four concerns. The geographic representation across the

Commonwealth make meeting face-to-face very difficult, if not impossible.

Additionally, it is anticipated that there will be a few powerful personalities within the

panel by their very role in education, school leadership, or public policy. Thirdly, time

required for this study is anticipated to be significant. A design that allows panelists to

respond from home or office will provide greater flexibility for panelist participation.

The imposition of time for a targeted field of experts must be carefully balanced with

requirements of the study. Finally, the panelist themselves are required to possess

expertise in related public education finance and / or policy. However, each will bring

different perspectives and perhaps strong personalities and opinions on the subject.

Responding in anonymity will allow a diverse group to essentially work side-by-side.

Providing for and understanding motivation for panelists to complete all study phases is

important. There are trade-offs between the value of face-to-face small group design and

48

the anonymity design this study proposes. Consideration applied to differences in

geographic locations, demands on time, and strong personalities lead to selection of a

Delphi technique and anonymous design.

One of the most important considerations when carrying out a Delphi study is

selecting the panel(s) of experts (e.g., Stone Fish & Busby, 1996). The selection of the

panelists is important because validity of the study is directly related to this selection

process; that is, knowledge of the panelists must be relevant to the questions being posed.

Unlike almost any other research design, randomization is not warranted nor needed (e.g.,

Stone Fish & Busby, 1996), and the selection of the number of panelists is not based on

any statistical measure.

Summary

This study set out to analyze expenditure trend patterns and determine education

policy, initiatives, events and mandates were reflected in those trends. Investigating a

decade of data with a target population of all 501 school districts will by necessity require organization of a lot of information. Apart from handling the data and generating multi- dimensional analyses of trends, obtaining deeper insight into the influences or implications perceived by others in those trends is highly valued. Design approach utilizing a team of expert participants provides access to a variety of individual experience and observation on the data analyses. While the data analyses will reveal

trends and patterns indicating influence, expert review is expected to provide a mixture of

additional evidence, clues, validations, and opinions on why the patterns show what they

do.

49

Chapter 3

Research Design and Methodology

Introduction

For the past twenty years, a substantial and growing body of literature on the allocation of school district expenditures over time has been developed.2 However, few

studies focus at the object level, and fewer still examine such linkages to expenditure

trends and major policy implementations. Examining changes in object spending patterns

over time offers significant insight for what is happening now as well as indicating policy

impact within observed patterns of the past. Plotting trends over time indicate the speed

(slope of change) of that trend as well variations on the trend line itself. Policy inserted

into any existing complex mix of expenditure trends should be informed by and try to

anticipate differentiated results, including unintended consequences. Well informed

school finance policy will serve to improve implementations and mitigate inequitable and

/ or unanticipated results.

Approach to Research

Complex Systems

Sargut and McGrath (2011) state that “in a complex system, the same starting conditions can produce different outcomes, depending on the interactions of the elements in the system” (p.70). They address the question of how to find a happy medium between convoluted scenarios about what might happen and linear predictions which are over- reliant on past knowledge. It is often hard to make sense of things because complex systems interact in unexpected ways. Their strong advice is to do the research and be

2 See for example Berne and Stiefel (1993), Rothstein (1997). 50

explicit about what will be applicable from past experience and what will be different moving forward.

They note one way to accomplish this is to divide the data into three buckets:

 Lagging: Data about what has already happened. Most financial metrics

and key performance indicator fall into this bucket

 Current: Data and information about where you stand right now

 Leading: Data about where things could go and how the system(s) may

respond to a range of possibilities

Further, Sargut and McGrath discuss what they feel is the best way to understand complex systems, which is to triangulate. They define triangulation as attacking a problem from various angles ---- using different / combined methodologies, making different assumptions, collecting different data, and looking at the same data in different ways.

For example, comparing snapshots of various elements

taken at given points in time yields a different

understanding than looking at how a single element

evolves over time. Or you can do both, studying how

numerous elements evolve over time. (p.75).

To a large extent the methodological approach in this study employs many of Sargut and McGrath’s strategies.

51

Data Specification

The population for the study was all 501 Pennsylvania school districts, and the

time period for the study was from 1997 through 2008.3 The combined data sets contain

selected district expenditure and revenue numbers for each year of the study, as well as

various selected district characteristics. The methodological approach relied heavily on

school district finance data available from the Pennsylvania Department of Education

(PDE). The complexities in gathering and handling a large robust data set required

organized and systematic approach, and the table below summarizes data specification

and collection process.

PDE maintains school district expenditure and revenue data in different formats

including summary and detail versions. Data also exist for a variety of district

characteristic information such as wealth measures and average daily membership

(ADM). Virtually all of these files are posted independent of one another requiring multiple downloads, and then combining and aligning data to each district by fiscal year.

District name and Assigned Unit number (AUN), which are unique numbers assigned by

PDE to each district, were downloaded with all runs as these district “keys” were used to ensure proper alignment of data to the correct district when combining files. Once alignments were complete, the resulting files represented a raw un-filtered un-analyzed data set. Descriptive statistical calculations and various data sorts and ordering views were then run for each fiscal year by district for the entire state. This process provided opportunity to view single district data, countywide data; variations within the timeframe

3 Beginning in 2005-06, PDE began to post Charter school and Career and Technical Center (CTC) School Object code data. For years prior to that, only district data are available. 52

of the study, as well as it provided the desired overview of statewide data. Table 3.1 lists a summary of data downloaded by year for each of the 501 districts:

Table 3.1

501 Districts For Each FY Expenditures Revenues Characteristics

Selected Sub‐ Market Value Major Function Data (Key Personal Average Major Object Function Data Components of Income Aid Daily Data (100's ( 1000 Major Function Local State Federal Other Ratio (Wealth Membership Fiscal Year through 900's) through 5000) Series) Sources Sources Sources Sources Measure) (ADM) 1996‐97 x x x x x x x x x 1997‐98 x x x x x x x x x 1998‐99 x x x x x x x x x 1999‐00 x x x x x x x x x 2000‐01 x x x x x x x x x 2001‐02 x x x x x x x x x 2002‐03 x x x x x x x x x 2003‐04 x x x x x x x x x 2004‐05 x x x x x x x x x 2005‐06 x x x x x x x x x 2006‐07 x x x x x x x x x 2007‐08 xx xxxxxxx

Using Table 3.1 as a guide the following section will briefly list and describe the sections and data collection process.

Expenditures

Object Dimension: data collected

Source file: PDE

Collected for each fiscal year:

1. Assigned Unit Number (AUN) for each district

2. District Name

3. District County

4. Major Object dollar amount expended (in dollars; as per the Annual

Financial Report (AFR) filed with PDE for each of the nine object codes

o Object 100 – Professional Services – Salary

o Object 200 – Personnel Services – Employee Benefits 53

o Object 300 – Purchased Professional and Technical Services

o Object 400 – Purchased Property Services

o Object 500 – Other Purchased Services

o Object 600 – Supplies

o Object 700 – Property (Equipment)

o Object 800 – Other Objects

o Object 900 – Other use of Funds

As the primary focus of the study, the share of total spending for each of the nine major object codes provides a window to observe changes in spending patterns over time. At any given point in time they provide a snapshot of this mix, when viewed over time spending pattern change and direction can be observed in these patterns.

Function Dimension: Data collected

Source file: PDE

Collected for each fiscal year:

1. AUN for each district

2. District Name

3. District County

4. Five Major Functions; dollar amounts expended as per the AFR; inclusive

of all reported sub-functions posted by PDE

o Function 1000 – Instruction

o Function 2000 – Support Services

o Function 3000 – Operation of Non-Instructional Services

54

o Function 4000 – Facilities Acquisition, Construction and

Improvement

o Function 5000 – Other Expenditures and Financing Uses

5. Within the Major Functions; Purposeful Selection of 13 separate sub-

functions were included (13 sub-functions plus the five major functions =

18 functional accounts for each district)

o Sub-function 1100 – Regular Elementary and Secondary Programs

o Sub-function 1200 – Special and Gifted Education Programs

o Sub-function 1300 – Vocational Education Programs

o Sub-function 1490 – Other Instructional Programs

o Sub-function 2100 – Pupil Personnel Services

o Sub-function 2120 – Guidance Services

o Sub-function 2140 – Psychological Services

o Sub-function 2200 – Instructional Staff Services

o Sub-function 2300 – Administration Services

o Sub-function 2380 – Office of the Principal Services

o Sub-function 2600 – Operations & Maintenance of Plant Services

o Sub-function 2700 – Student Transportation Services

o Sub-function – 2750 Non-Public Student Transportation Services

The five major functions are very broad and the selected sub-functions were determined

necessary to add deeper insight into spending pattern changes. For example, the 1000

function for instruction by itself would not reveal changes in regular instruction (1100) versus special education instruction (1200). Additionally, the 2000 function for support

55

services would not differentiate changes in spending for administration, pupil personnel,

or facilities. The sub-functions selected were generally those more dominate (by spending share) within the major function itself.

Revenues

Revenue dimensions: Data collected

Source file: PDE

Collected for each fiscal year: Local Revenues

1. AUN for each District

2. District Name

3. District County

4. All 6000 series of local revenue account code dimensions for each fiscal

year.

5. Purposeful selection of total local Taxes and total Other local revenue to

be able to separate tax stream from other local revenue streams

Collected for each fiscal year: State Revenues

1. AUN for each District

2. District Name

3. District County

4. All 7000 series of State revenue account code dimensions for each fiscal

year.

5. Purposeful selection of total state basic education funding (BEF) and total

state revenue to be able to separate BEF from other State revenues.

Collected for each fiscal year: Federal Revenues

56

1. AUN for each District

2. District Name

3. District County

4. All 8000 series for Federal Revenue account code dimensions for each

fiscal year; only total federal dollars used.

Collected for each fiscal year: Total Other Source Revenues

1. AUN for each District

2. District Name

3. District County

4. 9000 series of revenues account code dimensions; total dollars only

Characteristics

Market Value / Personal Income Aid Ratio (MVPI)

Source file:PDE

Collected for each fiscal year:

1. AUN for each district

2. District Name

3. District County

4. MVPI and component Aid Ratio data as posted by PDE for each fiscal

year

Average Daily Membership (ADM)

Source file: PDE

Collected for each fiscal year:

1. AUN for each District

57

2. District Name

3. District County

4. ADM data as posted by PDE for each year fiscal year

The characteristics were applied and run independently on each of the object series for

each fiscal year. This allowed comparison of the selected sub-set of districts (by MVPI or

ADM) to the overall statewide results. For each fiscal year, the sub-sets were divided into

three groups of fifty (50) utilizing MVPI for the wealthiest, the poorest and middle

wealth; and the process was repeated for district size utilizing ADM. This approach did

not create mutually exclusive cohorts as district ADM and MVPI changes over time; this moved some individual districts in or out of the comparative sub-group during the study

period.

58

Calculations

Table 3.2 lists the major calculations run on the data sets:

Table 3.2 Calculation Tabulation

Statewide Each Descriptive Statistic Calculations of Expenditures Totals / District Averages

Percent Share of Total District Expenditure by each Object X X (100's

90's) Change in Percent Share of Each Object by year X X

Objects Actual Expenditure dollar increase each year by

through Object X X Actual Expenditure percent increase each year Major by Object XX

11

Percent Share of Total District Spending XX and

functions Change in Percent Share of Each Function by year X X ‐ Major

5 sub ‐ Actual Expenditure dollar increase each year by

s Function X X

Actual Expenditure percent increase each year selected

Function by Function XX Percent share changes overtime for Selected time calculation split by first half and second half of

Split the study X ‐ 50 50 ‐ Repeated colocations above for Objects for ADM

50 and MVPI bottom, middle and top 50 districts X

Major object dollar expenditures downloaded from PDE’s web site were aligned by Object level for each year. For each major object dimension (100’s though 900’s) and within each fiscal year, the data were aligned by district and by county. These data contain dollar totals for each object and total expenditures for the year for each district.

Formulas were set up and run to calculate descriptive statistics to reveal object percent

59

share of total spending for each district, as well as statewide averages. Utilizing the

results of the percent share calculation, additional calculations revealed share changes

from the beginning of the study to the end of the study, at the mid-point, as well as the

range of share change over the entire study. With regard to actual dollars, calculations

were run to produce annual dollar increase/decrease per object and the percent of that change. From these results, calculations were run to yield the actual dollar amount and the share of “new” dollars each object commanded over time. New dollars is operationally defined as the actual unadjusted dollar growth in expenditures year over year. All of these calculations required manipulation of the files with sort and order views to observe and analyze results from various perspectives.

For major function and selected sub-functions, calculations run for the object series were repeated in similar format, including charts to reveal trend direction and strength over time.

For revenue, separate analyses and calculations examined changes over time for noticeable trends in the flow of new (growth) monies (i.e. from where new funds came).

These calculations were run for local, state, federal and other revenue sources. Change in share calculations were important to review for the revenues to observe trend changes over time.

Other Data

Pension

Pennsylvania School Employees Pension System (PSERS) data were drawn from three primary sources: the PSERS web site itself, PDE web site, and official PSERS’ comprehensive annual financial reports. Employer Cost Rate (ECR) historical data as

60

well as projected ECR were plotted historically to provide observation of pattern and trend pre-study, during the study, and post-study. Utilizing actual PDE AFR data, calculations were run to determine estimated actual dollar changes year over year within the school data for each fiscal year. Pension expenditures represent a significant share of the 200 object series. Specific PSERS data made publicly available provided the means to extrapolate reasonable estimations of pension policy dollar impact on the districts within the 200 object series. It is important to note that within the data set of this study only the direct PSERS impact on districts employee benefits ( 200 object series) can be observed.

Indirect impact would be significant, as well as significantly harder to extrapolate since

PSERS expenses for pass-through entities would be buried in other district objet codes as they pay for contracted services and tuitions (e.g. Intermediate Units, Career Technology

Centers, and charter schools).

Major Policy / Legislative Implementation

Construction of a multi-decade timeline pre and post study allowed for review and placement major events, policy, and legislative implementations. While not intended to be an exhaustive list, it was designed to include events readily apparent as well as provide context of such activity within the study. Discussion of the timeline and chart follow later in this chapter.

61

Analysis of Spending Patterns

Expenditure data were analyzed for the percent of total expenditures for each

district by year, and statewide average trending was plotted and observed. Once the

actual share of spending was plotted for each year, the change in share among object

expenditures (year-over-year) were determined. Additionally, the data were decomposed

for the first-half of the study and then the second-half to determine disparate patterns or

trends.

Analyses investigated what the average district looked like at the beginning of the study, observed changes overtime, and repeated the process to see what the average district looked like at the end of the study and how much it differed from the beginning of

the time period. Graphically plotted trend revealed the path which the data element

traveled for the beginning point to the end point. For example, in some cases data clearly

showed consistent slope of growth trend over time, while other elements revealed erratic

paths with loss and gain in shares.

Changes in spending patterns were analyzed for direction of percent share trend,

range, command of new dollars, as well as by percent share beginning and ending and

share of change over time. Additionally, using a state wealth measure and student

enrollment characteristics, data were analyzed for spending pattern changes to observe

any disparate patterns associated with these two district characteristics.

Consideration of data presentation was deemed critical to be able to analyze,

summarize and display results clearly in an understandable format. For most of the files,

linked Excel tabs were created to summarize totals and allow for succinct view of either a

single year or multiple years of data. Actual presentation and summary data alignment

62

primarily took the form of charts and tables to provide perspective on the results. For example, percent share results were plotted for each object over time to visually depict trend strength and direction. Sort options of percent share provided perspective on the range of variance as well as outliers from the average. Sorts of selected columns of data

(within spreadsheets) provided observations of the same data from various perspectives over time, including different fiscal year groupings. For example, a change in percent

share of an object from the beginning point of the study to the ending point reveals the

net change over the selected time period. However, plotting each year along the way

reveals the path taken to get from the beginning point to the ending point. The

methodology presented multiple views and cuts of the same data to provide perspective

and observations not readily apparent in a single observation. Calculations and sorts

providing observations over time inclusive of study-wide trend line were deemed critical

to be able to investigate possible linkages to policy implementations and potential impact.

District Characteristics

To examine whether school district characteristics revealed different spending

patterns and trends, district enrollment size, measured by average daily membership

(ADM), and wealth, measured by Market Value / Personal Income ratio (MVPI), were

utilized. These characteristics represent two significant metrics recognized statewide to

proxy district wealth or size.

ADM is a fairly straight forward proxy to provide an indication of district size as

it very closely correlates to a district enrollment. This metric is not weighted with regard

to educational level, (versus Weighted Average Daily Membership or WDAM) as it

simply captures the exact numbers of days pro-rata each student is a ‘member’ of a given

63

districts official enrollment responsibility. MVPI as a wealth measure is not so straight forward, but it perhaps is the single most often used metric to evaluate district wealth in

Pennsylvania. It is helpful to understand the composition of the calculation to arrive at

MVPI (see Table 3.3).

Table 3.3: Market Value Personal Income Ratio: Pennsylvania Wealth Measure

Characteristic

MARKET VALUE / PERSONAL INCOME AID RATIO MV/PI AR

MV AR = 1 — School District MV/WADM x 1 State MV/WADM

PI AR = 1 — School District PI/WADM x 1 State PI/WADM

MV/PI AR = 0.6 (MV AR) + 0.4 (PI AR)

Utilization of the MVPI aid ratio was selected due to its broad acceptance and almost exclusive use as a measure of district wealth across the state. While most wealth measures will have flaws, the MVPI has been used in Pennsylvania for several decades and is widely recognized and utilized in several state subsidy formulas. The formula is heavily reliant on the State Tax Equalization Board (STEB) for the calculated Market

Value (MV) of the formula, and that process is not without suspect issues. However, it is the best measure of wealth available. The MVPI formula above notes how the MVPI is calculated in Pennsylvania. Most significantly, it is biased towards market value as it

64

weights market value at 60% of the formula versus personal income at 40%.

Additionally, it is an inverse measure where a lower value indicates a wealthier district

and higher values are associated with poorer districts.

All 501 districts were sorted and aligned by calculated object share by year and

merged with corresponding MVPI or ADM data specific to that year. For both sets of

data (ADM and MVPI) an average percent share of total spending for each object was calculated. These data were then broken into top, middle or bottom 50 districts in the state. For ADM data this resulted in the smallest 50, the middle 50, the largest 50 districts in enrollment size. The same process was repeated for MVPI with the exception that the districts were the 50 least wealthy, 50 wealthiest, and the 50 middle range districts in accordance with the MVPI measure itself (See Figures 3.4 – 3.6).

This methodology did not provide an intact cohort over time (11 fiscal years) for review as several districts “moved in or out” of the 50 district rankings over time.

However, many districts did remain within the ‘50’ rankings throughout. Overall, the purpose was to investigate observed impact by enrollment size and wealth on spending pattern changes against the overall state averages. Remaining consistent at the measure was deemed more useful than including districts whose characteristics changed enough over time to remove them from the 50 district rankings. Earlier object review provides a base line from which to observe potential characteristic impact indications.

Additionally, utilizing the average of the 50 each year does not adjust for any sensitivity created by trend or the grouping itself. The average ADM and MVPI of each fiscal year grouping plotted over the course of the study did reveal trends. Although

65

within the object results themselves no discernible impact from these trends was readily

visible.

The smallest and middle ADM districts did reveal reductions in enrollment as an average within the sorted group of 50. The 50 largest ADM districts on actually grew in

size over time.

Trends within the wealth measure groupings, while not as pronounced as the

ADM trends, had the wealthiest districts losing wealth on average. Middle and lowest

wealth districts actually trended toward gaining wealth on average during the term of the

study.

Figure 3.1 ADM 50 Smallest Districts

Average ADM: 50 Smallest Districts 800 779 780

760

740 738 720

700 697 680

660

640

66

Figure 3.2 ADM 50 Middle Size Districts

Average ADM 50 Middle Districts 2,500 2,453 2,450

2,400 2,390

2,350

2,300

2,283 2,250

Figure 3.3 ADM 50 Largest Districts

Average ADM 50 Largest Districts 14,200

14,077 14,100

14,000

13,968 13,900

13,878 13,800

13,700 13,690 13,600

67

Figure 3.4 MVPI 50 Highest Wealth

MVPI Avg of the 50 Highest Wealth Districts 0.2150 0.2100 0.2110 0.2050 0.2031 0.2000 0.1950 0.1900 0.1850 0.1800 0.1842 0.1750 0.1700

Figure 3.5 MVPI 50 Middle Wealth

MVPI Avg of the 50 Middle Wealth Districts 0.5920 0.5909 0.5900 0.5883 0.5880 0.5860 0.5840 0.5854 0.5820 0.5800 0.5780 0.5760

68

Figure 3.6 MVPI 50 Lowest Wealth

MVPI Avg of the 50 Lowest Wealth Districts 0.7900 0.7872 0.7850 0.7800 0.7750 0.7732 0.7700 0.7650 0.7689 0.7600 0.7550 0.7500

As a reminder for an important concept, MVPI is an inverse measure where a lower value will be associated with a wealthier district.

Policy context

Major policy events were identified and then aligned to the study time-frame and

examined for possible matches of relationships in observed shifts in spending patterns

(see Figure 3.7).

69

Figure 3.7 Policy Timeline

Significant Events in school funding over time (source: adapted from PSBA Bulletin) (Davare 2008) 1970‐71 1971‐72 1972‐73 1973‐74 1974‐75 1975‐76 1976‐77 1977‐78 1978‐79 1979‐80 1980‐81 1981‐82 1982‐83 1983‐84 1984‐85 1985‐86 1986‐87 1987‐88 1988‐89 R. Shafer M. Shapp D. Thornburgh R. Casey

Collective Bargaining / IDEA Equalized subsidy State‐wide tax reform Intermediate units authorization for basic education (repealed Created (ESBE) Mercantile/Business privilegetax)

Non‐PublicTransportation mandated

1989‐90 1990‐91 1991‐92 1992‐93 1993‐94 1994‐95 1995‐96 1996‐97 1997‐98 1998‐99 1999‐00 2000‐01 2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08 T. Ridge M. Schweiker E. Rendell

PSSA‐ ABG Special School ESBE Eliminated; NCLB; Pension multipliers, Funding Session based Foundation funding contributions ,and related beginsACT 1 tax reformAct 1 ‐ ofall districts manda Testing funding changes; PURTA 2006: Tax payer legislation; mandate waivers ACT 72 Charter School Law, Foundation relief Act implemented; repeal of tax Funding halted; Targeted funding, IDEA reauthorization occupational assessment tax reform and PA includes PSSA goes Student based (Act 24) ACT 1 tax Gifted Students reform ‐ all Recession districts mandated Begins Dec Collective bargaining update limits in 2007 strikes, includesfact‐finding / ACT 50 tax reform arbitration

2008‐09 2009‐10 2010‐11 2011‐12 2012‐13 2013‐14 2014‐15 2015‐16 2016‐17 2017‐18 2018‐19 T. Corbett ACA ARRA NCLB Proficiency Health Pension 1st Yr of funds: rates: Moves to Care $1.0+ Rate ECR "slot" 09‐10 & Billion 100% all grades Impact 10‐11 at Peak : funds State Ed Teacher and Admin from Edu Jobs funding 30%+ of Evaluation systems 2013 + 10‐11 reduction PYRL? ‐ ABG driven from State Funding level Recession 'ends' reduced July 2009 ‐Charter ACA Excise Tax Begins 2018 Reimb KeyStone Testing Begins ends

Other item / Events during the study period Gambling legislation / Homestead reductions Costing out study Fund balance limitations passed mid‐2000's NCLB‐ PA % proficiency level increases Clean and Green Act 319 Annual PSERS rate increase anticipated Special Ed and inclusion emphasis ‐ Gaskins Case Voc‐ed and CTC changes PIMS implimentation...CFF Charter schools & 363 tuition Tax reform Era / Recessionary Impact / Assessment appeals and reductions Cyber charters

Data alignment and analysis by fiscal years or by split time frames provided an additional lens through which to observe for indications of policy impact on data results. Pre and post timeframes are important to incorporate lead and lag times into observations and analysis. Toward that goal the enclosed chart builds from prior work (Davare 2008) and in summary fashion tracks major policy implementations over the past 40 years. The time frame of the study, 1997-98 to 2007-08 is noted and care was taken to ensure major policy implementations in and around that timeframe were understood and noted (see discussion in Review of Literature).

70

Interviews

Education Policy Makers: Review and Interpretation

A primary focus of this study is to examine how, if at all, major educational policies, initiatives, and mandates are translated into and reflected by school district spending patterns. To provide assistance in that objective, this study utilized expert opinion to review issues raised from data trends and observations (See Appendix;

Interview Guide).

Selection

Participants were purposefully selected for their established expertise in the field of education. The participants possessed a range of expertise inclusive of experience in instruction, policy formulation, and school finance. Additionally, their positions and expertise represented various associations and levels within those organizations that provided them with unique perspectives statewide on public education. Participants included the Director of Research at Pennsylvania School Boards Association (PSBA), the Executive Director of Pennsylvania Association of School Business Officials

(PASBO), The Executive Director of Pennsylvania Association of School Administrators

(PASA), a former executive director of PASA and who at interview time worked for the

Pennsylvania State Education Association (PSEA), an Executive Officer of Public

Financial Management (PFM), the Executive Director of the Pennsylvania Association of

Rural and Small Schools (PARSS), and lastly, a former member of the State Board of

Education.

71

Participants were in these positions or in similar high-end educational policy

positions during the timeframe of the study, and were selected due to highly regarded

reputations within educational finance, policy and program expertise.

Scheduling

Each participant was contacted individually by the author and asked if they would

be willing to participate. Nine participants were contacted and all agreed to participate.

One participant incurred schedule conflicts twice for scheduled interviews and eventually

did not participate. A second participant had a serious medical condition and an interview

was not scheduled. For the remaining seven, scheduling for their interviews was

accomplished by setting a convenient time and date and the author travelled to the

participant’s location choice. Each interview was allocated a minimum of one hour,

generally running about 90 minutes in length.

Prior provision of data trends

Several steps were taken to prepare for the interview. A time-line of major policy

/ public education events was compiled to provide context for the study time period. This

information was assembled in chart form designed to fit on one page for easy visual

representation (see Figure 3.10). Dissertation questions were reviewed and the interview

instrument completed. In power point form, it was targeted to the research questions, and

included summary work of actual data results for objects, functions, revenues and district

characteristics.

These summary data results were organized into a 46 page PowerPoint format for presentation for the interview process. One participant was purposefully selected for an initial interview to go through the prepared data results prior to scheduling other

72

interviews. This participant was deemed the most knowledgeable / familiar with regard to

school expenditures over the time period. The participant was informed in advance that

he was the first interviewee, and was more than willing to spend extra time to review the

data. All participants were aware that there were others involved, although that

(notification) was not part of the formal process. Most of the participants knew each

other and it was evident during the interview process several of the participants had

talked to each other about this work.

The initial interview provided valuable feed-back and required additional time to

address specific questions and data formats. Several of the PowerPoint slides (data

summary charts / graphs) were revised, re-organized, or re-ordered based on interview

flow and input. Additionally, it was evident that the data created discussion not just at an

individual slide level (a power point slide of summary data presentation), but also within

an entire grouping of related slides. Based on the initial interview, the “slide” order was

re-organized into related groupings for tracking comments and observations. It was also

felt it improved the flow of discussion.

Prior to each interview the PowerPoint document was e-mailed to each participant

to allow time to review the results prior to the meeting. Each participant was informed

that a hard copy would be provided at the interview if they decided not to print the file. It should be noted that all participants but one had printed the data results and had that in- hand as the interview began. A single copy of the prepared major policy timelines was also included with the participants e-mail. The packet included a general statement of the purpose of the study and data results in various table and chart form. No interpretation or write-up was included with the data set as the objectives for the interview were to capture

73

reaction to the data as prepared, and to discover additional insights or avenues to investigate from observations and comments.

The interviews, conducted individually were designed to be informal, flexible and allow participant exploration of the findings via questions and answers and general discussion. This format allowed them to expound on policy insights and possible linkages

(that they could see or surmise) to data results. Given their extensive expertise and personal experience ‘working in the field,’ their input and reflection would provide valuable input and insight.

Method of collection of interview answers

During the interviews, notes were taken and comments were indicated directly on a copy of the PowerPoint slide. The interview process itself flowed in the order of the

PowerPoint presentation itself. Individual slides and grouping of related slides allowed specific focus on data findings central to one aspect or area of the study. Individual slides predominantly were data trend charts or graphs summarizing specific data findings. The charts (on individual slides) were copied to the PowerPoint from the Excel file work

books.

Notes recorded at corresponding slide numbers and page references provided a

direct link to appropriate discussion or comment. This process served several purposes.

The first helped to ensure that both the interviewee and researcher were discussing /

referencing the same findings on a particular page or slide. Secondly, purposeful common

groupings of data results allowed for discussion to flow from one slide to the next, and

sometimes back again. In general, these groupings correlate to the research questions themselves. For example, combining trends and results within an object grouping were

74

deemed to provide a richer review than had the data not been so organized. Other

groupings followed the same approach. Thirdly, within a grouping a particular slide may

not have generated much discussion or no comment at all. The grouping allowed for

greater perspective and discussion as related information could be observed within a particular grouping. Finally, conversation, reaction or direct focus on a particular slide

could stand out given particular interest, observation or perspective offered by the

participant.

Several of the participants provided notes or offered items of their own to the

researcher to contribute to the effort. These items were reviewed for follow-up or

analysis.

Organization of interview information

An interview review worksheet was utilized to capture information from the

interview notes. This work sheet followed the order of the interview PowerPoint as well

as aligned with the groupings, and was completed for each participant separately.

Information from the interview was systematically placed into the work sheet (from the

interview notes) along with key comments and statements aligned to the appropriate

topic. This process generally allowed observation of the comments and statements in the

order in which they were made. Additionally, the work sheet clearly delineated which

slides or groupings invoked more discussion, reaction, and input from the participant.

A second work file was used to combine the above interview data information in

an Excel file to provide sort and order ability. This allowed for sort and order options to

observe interview results from multiple perspectives. This work sheet captured counts of

comments by slide or grouping, by participant, by main research focus (object, function,

75

etc.), and by key comments and observations. The primary focus of this research was

trend observation of expenditure data, in a complimentary approach the interview data

plotted in the above manner could also compile trend or patterns in interview

observations.

While comments and observations were captured individually for each interview,

they were also combined together within the purposeful groupings. In this manner all of the comments or observations related a specific section were all in one spot.

From the combined work file themes and concepts were reviewed for patterns of relationships as well as individual singleton type issues. Based on the extensive, but different education expertise of the participants, individual comments and observations would be deemed credible and duly expected, and require proper due attention.

Analysis of the Interview Data

Analysis of the interview data flowed from utilizing the combination of researcher notes from the interviews summarized individual interview work sheet, the combined interview sort file, and other documents / notes provided by the participants. Due to the purposeful alignment of the data presentation and recording the interview results in the similar manner, alignment with the research questions provided insight and value.

Additionally, integration of the results with the expenditure and revenue data were improved as key discussions could be tied to distinct financial data findings. The combined grouping of responses allowed for direct comparison for validation of themes, or in some cases insight into conflicting information.

76

Chapter 4

Data Analysis

Introduction

The purpose of this study was to analyze school district spending patterns across the state and to study how major educational policies, initiatives, and mandates were reflected in those expenditures. To accomplish this, expenditures were examined for all

501 Pennsylvania school districts from 1998 through 2008. Expenditure reviews were primarily focused on patterns resulting from compiling various multi-year trends of the object level dimension of account code classifications. These data came directly from

district annual financial reports available from the Pennsylvania Department of

Education. Analysis was also conducted on expenditures and revenues at the functional

level dimension, while not at the same intensity as the object level.

Results from data trending compilations and multi-perspective views of

expenditure data revealed changes in object and function spending patterns. While trend patterns also showed changes for revenue, they generally followed the long-term trend of an increasing local share over state share in funding public education.

From a state-wide perspective, multi-year trends display expenditure changes in terms of dollars (unadjusted) as well as percentage (share) of total spending. Changes in percent share of total expenditure varied in direction (growth or loss of share) and revealed magnitude and strength of those shifts. The data are presented in two main formats: a split-view of the first half and second half of the study and year-by-year trending for the analyzed data target. The multi-year trends for percent share and percent increase display paths traveled from the starting point to the ending point. Plotting the

77

data in this methodology allows one to observe twists, turns and direction changes of spending patterns during the time period, not just beginning and ending point that conceal important changes during the study period. Following is a summary of data findings in order by expenditures, object, function, sub-function, revenues, interviews, and then other related data.

Summary of Findings:

Table 4.1 presents a total spending analysis which is helpful to provide context for findings presented. Expenditures grew to $22.9 billion in 2007-08 from $12.8 billion in

1997-98, for total dollar growth of $10.04 billion. More importantly, readers should note that one percent of 2007-08 expenditures are just short of $230 million, meaning, a one percent share change has the equivalent value of $230 million in the 2007-08 fiscal year.

Table 4.1 Total Spending (Unadjusted dollars)

Total Spending Analysis: 501 Pennsylvania School Districts Annual Annual Expenditure Cumulative $ Cumulative Fiscal Y ear T otal Ex penditures Expenditure % $ Increase Increase % Increase Increase 1997-98 $ 12,873,760,737 1998-99 $ 14,267,360,487 $ 1,393,599,749 10.8% $ 1,393,599,749 10.8% 1999-00 $ 14,902,414,120 $ 635,053,633 4.5%$ 2,028,653,383 15.8% 2000-01 $ 15,698,046,832 $ 795,632,712 5.3%$ 2,824,286,095 21.9% 2001-02 $ 16,535,626,672 $ 837,579,840 5.3%$ 3,661,865,935 28.4% 2002-03 $ 17,381,552,295 $ 845,925,623 5.1%$ 4,507,791,558 35.0% 2003-04 $ 18,495,035,520 $ 1,113,483,226 6.4%$ 5,621,274,783 43.7% 2004-05 $ 19,648,309,817 $ 1,153,274,297 6.2%$ 6,774,549,080 52.6% 2005-06 $ 20,828,530,437 $ 1,180,220,620 6.0%$ 7,954,769,699 61.8% 2006-07 $ 21,801,108,551 $ 972,578,114 4.7%$ 8,927,347,813 69.3% 2007-08* $ 22,915,219,678 $ 1,114,111,127 5.1%$ 10,041,458,941 78.0% * Any one percent (1%) change in share of spending (by spending code / dimension) represents $229,152,197

78

Object Spending Patterns

As indicated earlier, the study was split into two period intervals representing

“half-way” through the study. This split also corresponds to the change from the

Ridge/Schweiker administrations to the Rendell administration. Expected lag and lead

time from executive office changes in education funding approach and policies are also

tied to this time frame break point.

Each of the nine object dimensions was analyzed for multi-year share changes and

these results provide a focal point from which to begin. During the years of the study,

Salaries, (100’s object) lost the largest percent share of total spending at 8.3%. This share

change was the largest share change, positive or negative, of all the object dimensions by

a factor greater than two. Other Purchased Services (500’s object) displayed 3.5%

increase in share change with the Other Uses, (900’s object) at a 2.6% increase.

Professional services 300’s object) displayed a 1.23% increase in spending share.

The object categories showed much smaller changes; Benefits (200’s), Property

Services (400’s), Supplies (600’s), Equipment (700’s), and Other Objects (800’s) all had

less than a 1% overall change from beginning point to ending point. Of these, the 200’s and 400’s lost share, while the others gained share. More significantly, while the 200’s category lost a small share percentage overall, it also displayed significant loss and gain

activity (directional changes) in the two halves of the study. The 200’s lost 3.4% share in

the first-half, and regained 3.2% in the second-half. These alternating changes basically

canceled each other out by 2007-08.

Figure 4.1 plots object share change for the first half, second half, and full-term of the study. The data labels in Figure 4.1 note the overall ten year changes from 1997-98 to

79

2007-08 in share, while the other chart bars depict the five year split-time intervals for the first and second half of the study timeframe.

Figure 4.1 Object Dimension: Change in % Share; 1st Half, 2nd Half, Entire Study

Object Dimension: Change in % Share of Spending

6.00%

4.00% 3.5% 2.6% 2.00% 1.2% Change 0.81% 97‐98 to 0.38% 0.09% 02‐03 0.00% ‐0.16% ‐0.15% ‐2.00% Change 02‐03 to ‐4.00% 07‐08

‐6.00%

Change ‐8.00% 97‐98 to ‐8.3% 07‐08 ‐10.00%

From this overview of spending share change, it is helpful to look at the same data from different perspectives. Actual dollar growth within each object and the percent share of those dollars provide greater context and insight into trend activity. While the 100’s lost

80

share, salary dollars still grew and the rate of the growth slowed down at much greater pace in the second-half than was displayed in the first-half. The 200 object dimension displayed the most dramatic dollar growth change with $181.9 million in the first-half, and then $1.5 billion in the second-half. For the 200’s this represented more than an 8 fold increase from one five year period to the next. The 500’s and 900’s growth displayed directionally consistent growth at strong levels, while the 300’s exhibited the same trend: it was at about half the pace of these two objects. Table 4.2 analyzes the $10 billion dollar ten year growth within the split time frame of the study.

Table 4.2 Dollar Growth by Object

New Dollars by New Dollars by New dollars by Objects Object 1997-98 to Object 2002-03 to Object 1997-98 to 2002-03 2007-08 2007-08

100's Salaries$ 2,063,329,468 $ 1,472,951,024 $ 3,536,280,493 200's Benefts$ 181,826,592 $ 1,488,784,874 $ 1,670,611,466 300's Purch Prof Srvs$ 430,989,947 $ 335,774,238 $ 766,764,185 400's Purchased Prop Srvs$ 117,084,801 $ 122,335,655 $ 239,420,456 500's Other Purch srvs$ 695,243,719 $ 852,629,532 $ 1,547,873,252 600's Supplies$ 242,279,200 $ 232,387,560 $ 474,666,760 700's Property$ 37,802,444 $ 110,574,619 $ 148,377,063 800's Other Objects$ 221,448,567 $ 233,730,401 $ 455,178,968 900's Other Uses$ 517,786,819 $ 684,499,479 $ 1,202,286,298

Total$ 4,507,791,558 $ 5,533,667,383 $ 10,041,458,941

Actual dollar growth was also analyzed as a percent share of the “new dollars” flowing into the expenditure stream. Trend of percent share of new dollars within the first half and second half even more clearly displays various activities. Additionally, this particular analysis serves as a proxy to scope (size of the impact) as well as slope

81

(direction and strength) of the trend as revealed by changes in the shares. Table 4.3 displays the percent share of the new dollars flowing into the expenditures in total as well as by first–half and second–half.

Table 4.3 Percent Share of Dollar Growth by Object

Share of Share of Share of New dollars New Dollars New dollars Objects by Object by Object by Object 1997-98 to 2002-03 to 1997-98 to 2002-03 2007-08 2007-08

100's Salaries 45.8% 26.6% 35.2% 200's Benefts 4.0% 26.9% 16.6% 300's Purch Prof Srvs 9.6% 6.1% 7.6% 400's Purchased Prop Srvs 2.6% 2.2% 2.4% 500's Other Purch srvs 15.4% 15.4% 15.4% 600's Supplies 5.4% 4.2% 4.7% 700's Property 0.8% 2.0% 1.5% 800's Other Objects 4.9% 4.2% 4.5% 900's Other Uses 11.5% 12.4% 12.0% 100.00% 100.00% 100.00% Total

Analysis by Object

Continuing the analysis of object account code dimensions, the next series of analyses looks at the trend line of percent share of spending over the entire study.

Displaying multi-year share trend show the paths taken across time. They reveal direction, direction change, the year in which changes occurred, and to a large extent the strength of the change as indicted by the slope of the trend line. The analysis starts with the 100 object series and is repeated through the 900 series, followed by a brief object summary.

82

Salaries: 100 Object Series:

As a percent share of total spending, salaries statewide had the largest drop in

share of all spending categories. Salaries also had the largest change in share overall.

Salaries’ share of expenditures dropped every year from 1998-99 and the slope of that

decline was steeper in the second half of the study. The downward trend of lost share

increased in pace / scope beginning in the 2002-03 fiscal year with a rapid 6.12% loss in share in that five year period.

Figure 4.2 - 100 Object: Percent Share of Total Expenditures

100's Salaries: Percent Share of Total Expenditures

56.00% 54.1% 54.4% 54.0% 54.00% 53.8% 52.9% 51.9% 52.00%

50.1% 50.00% 49.1% 48.00% 47.6% 46.6% 46.00% 45.8% 44.00%

Share change = ‐2.2% 1st half...... ‐6.1% second half Overall Share Change = ‐8.3%

In total, salaries lost 8.3% share of expenditures from 1997-98 to 2007-08. Salaries in

2007-08 were $10.5 billion and represented the largest spending object dimension. Even

83

though the salary dimension lost significant share, dollar growth (See Tables 4.23 and

4.4) of salaries overall was $3.5 billion and represented 35.2% of total new spending

(increased dollars) with a mean annual growth of 4.2%. However, this dollar growth was not evenly accrued across the study’s timeframe. While declining 2.3% in share of spending from 1997-98 to 2002-03, salaries accounted for a significant 45.8% of new dollars (i.e. representing $45.8 of every $100 in new/ additional dollars expended). From

2002-03 to 2007-08 salary growth dropped to 26.6% of new dollars, or $26.6 of every new / additional $100 in expenditure growth.

The 45.8% salary share of new dollars from 1997-98 to 2002-03 was coupled with a minimal 4.0% share growth by the 200 benefits dimension yielding 49.8% of new dollars flowing to combined salaries and benefits. From 2002-03 to 2007-08 the salary

share growth of new dollars of 26.6% was combined with a rapidly climbing 26.9%

benefits share growth yielding 53.5% of new dollars flowing to salary and benefits. In

actual dollars, benefits captured more than salary in the second half of the study.

Scatter plots for object percent share of all 501 districts were prepared to illustrate

the range of individual district results. Outliers can be identified as those districts that

appear well above or below the average, or which over time do not reflect the patterned

movements. For the 100 object series, the variations of district percent share can be

observed above and below the average trend line as shown on Figure 4.3.

84

Figure 4.3 - Scatter Plot of 100’s; 501 Districts 2007-08

2007‐08 100's % Share Scatter Plot

70.00%

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00% 5 55 105 155 205 255 305 355 405 455 505

A further breakdown of object data is provided via scatter plots of percent share of total expenditures which compares the distribution in the beginning year (1997-98 with red circles) with the distribution in the ending year (2007-08 with blue diamonds). As displayed for the salary object code (see Figure 4.4), the overall pattern and the trend line for the final year show a clear downward movement, confirming the loss of share of salary in district expenditures by 2007-08. Scatter plots were run for all object series for

1997-98 and for 2007-08.

85

Figure 4.4 - Scatter Plot 100’s: First Year and Last Year of Study; 501 Districts

100's % Share Scatter Plot: 2007‐08 in Blue (diamonds) / 1997‐98 in Red (dots) 70.00%

60.00%

50.00%

40.00%

30.00%

20.00% 2007‐08 1997‐98 Average Average 10.00% (lower line) (upper line) 0.00% 5 55 105 155 205 255 305 355 405 455 505

Benefits: 200 Object Series

As a percent share of total spending, the benefits (200’s) dimension lost only

0.2% when comparing 1997-98 to 2007-08. However, when the split time periods of objects expenditures are observed individually, the data present a very different picture.

The decade long minimal share change result is extremely misleading; benefits had the widest swing in share change direction within the years of the study. Benefits also represent the second highest spending dimension at $3.9 billion in 2007-08. The 200 series share change represents a swing of nearly 4% of total statewide expenditure share in both the first half and second half of the study. However, each trend is going in opposite directions, representing major shifts in benefit costs, and significant impact on dollar movements.

86

From 1997-98 to 2002-03 benefits trend of spending share dropped -3.4% and then turned to increase by 3.2% in share gain from 2002-03 to 2007-08. The swing was actually deeper than that as the total percent share of benefits bottomed out in the 2000-

01 fiscal at 13.04%, and it has been climbing ever since. Figure 4.5 displays the multi- year trend which shows the rapid free fall of share and then a steady increase to the end of the study.

Figure 4.5 - 200 Object: Percent Share of Total Expenditures

200's Benefits: % Share of Total Expenditures

18.00%

16.8% 17.00% 17.0% 16.3% 16.8% 16.00% 16.0% 15.4% 15.00% 14.4% 14.00% 14.0% 13.2% 13.6% 13.00% 13.0% 12.00%

Share change = ‐3.4% 1st half.... +3.2% 2nd half Overall Share Change 1997‐98 to 2007‐08 = ‐.2%

New dollars flowing through / to benefits were $181.9 million from 1997-98 to

2002-03, and grew eight fold to $1.5 billion from 2002-03 to 2007-08 (see Tables 4.3 &

4.4). A large 20.0% increase spike in benefits (see Figure 4.6) occurred in 2003-04 and essentially “front loaded” the cost growth and share for the second half of the study.

87

Figure 4.6 – 200’s Annual Percent Increase

200's Benefts: Annual % Increase 25.00% 20.0% 20.00% 15.00% 10.6% 9.0% 8.0% 10.00%

5.00% 1.2% 7.7% 6.3% 5.4% 0.00%

‐5.00% ‐1.8% ‐5.9% ‐10.00% 08 07 06 05 04 03 02 01 00 99 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998

The benefit share reduction in the first half of the study in essence reflects freed up monies / spending which allowed those resources to be reallocated elsewhere.

Concomitantly, the “tax base” or revenue base which supported the higher share benefit

costs in the earlier years were, by default, redeployed to other budget areas.

Overall, benefits captured 16.7% of all new dollars from 1997-98 to 2007-08 and

when added to the salary total of 35.2% for the same time period, salary and benefits

represented 51.9% of all new dollars (see Table 4.4). Considering total personnel costs, in

1997-98 salaries and benefits represented a combined 71.1% total share of expenditures;

this same statewide object combination fell to 62.65% share in 2007-08.

Professional Services: 300 Object Series:

Professional services (300’s) object dimension grew from 4.8% to 6.1% as a share

of total spending over the ten years of the study. It reached the 6.1% level in 2002-03 but

continued to grow and peaked at 6.5% share in 2004-05, and then retreated back to the

6.1% share by 2007-08. As shown in Figure 4.7, the percent share of total spending of the

88

professional services (300’s) object grew +1.2% in share from 1997-98 to 2007-08.

However, this statistic is somewhat misleading in that all share growth occurred from

1997-98 to 2004-05, and then it lost share in the final three years.

Figure 4.7 - 300 Object: Percent Share of Total Expenditures

300's ‐ Prof Services 7.00% 6.5% 6.50% 6.3% 6.00% 5.8% 6.3% 6.3% 5.6% 6.0% 6.1% 5.50% 5.5% 5.00% 5.2%

4.50% 4.8%

4.00%

Share Change = +1.2% 1st half...... 0% 2nd half Overall Change = +1.2%

Professional services share of new dollars grew $431 million from 1997-98 to

2002-03 and $336 million from 2002-03 to 2007-08 (see Tables 4.3 & 4.4). Professional services captured 7.6% share of total new dollars from 1997-98 to 2007-08. From 1997-

98 to 2002-03 professional services captured 9.6% of new dollars, and that rate fell to

6.1% for the second half of the study. Additionally, the first-half of the study displayed consistent significant annual percent increases of the object dollar themselves. After front loading nearly six years of large annual increases, increases dropped rapidly in 2004-05 ending with a 1.6% increase in the final year (see Figure 4.8). 89

Figure 4.8- 300’s Annual Percent Increase

300's Professional Services: Annual % Increase 25.00%

20.00% 20.5%

15.00%

11.0% 9.9% 10.00%

9.0% 8.6% 8.7% 8.1% 4.4% 5.00% 1.6% 3.1% 0.00% 1998‐99 1999‐00 2000‐01 2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08

Purchased Services: 400 Object Series:

Overall, Purchased Services represents a small portion of the total expenditures and, as a result, the percent share changes were quite small as well. As a percent share of total spending the purchased services (400’s) object dropped a minimal -.15% in share

1997-98 to 2007-08. Purchased services share of spending remained fairly consistent throughout the study with -.03% share reduction from1997-98 to 2002-03 and a -.12% share reduction from 2002-03 to 2007-08 (see Figure 4.9).

90

Figure 4.9 - 400 Object: Percent Share of Total Expenditures

400's Property Services

2.75% 2.7% 2.7%

2.70% 2.7% 2.7% 2.7% 2.65% 2.6% 2.6% 2.6% 2.60% 2.6%

2.55% 2.5% 2.50% 2.5%

2.45%

Share Change = ‐.03% 1st Half.... ‐.12% 2nd Half Overall Change = ‐.15%

For the 400 series, there was a large percentage increase in actual spending of

8.0% from 2006-07 to 2007-08 resulting in total spending share increasing from 2.5% to

2.6% and that reversed the share loss trend for the previous five years. Purchased services annual percent increases were consistent enough to allow it to hold share relatively constant (See Figure 4.10).

91

Figure 4.10- 400’s Annual Percent Increase

400's Purchased Prop Srvs: Annual % Increase 9.00% 8.0% 8.00% 7.5% 6.8% 7.00% 6.2% 6.00% 4.7% 5.00% 5.0% 3.9% 4.00% 4.1% 3.7% 3.00% 3.6% 2.00%

1.00%

0.00%

Other Purchased Services: 500 Object Series

As a percent share of total spending, Other Purchased Services (500’s) object gained +3.5% in share of spending from 1997-98 to 2007-08. This growth is significant since the 500’s object series represents the third highest spending dimension at $2.51 billion. Additionally, the 500 object series trend for percent share of spending grew steadily each year with no indication of variance or change from that trend.

92

Figure 4.11 - 500 Object: Percent Share of Total Expenditures

500's Other Purch. Services 12.00% 11.0% 11.00% 10.1% 10.5% 10.7% 10.00% 9.5% 9.2% 9.7% 9.00% 8.2% 8.1% 8.7% 8.00%

7.5% 7.00%

6.00%

Share change = +2.0 1st half....+1.5% 2nd half Overall change = +3.5%

Significantly, the positive 3.5% share change was the highest overall positive object dimension share change over the full study time frame. To put this increased share in context, it represented approximately $800 million for this one object. This positive percentage share change was nearly a full percentage point higher than the second largest positive increase in the 900’s object series.

The 500 object year-over-year percentage increases ranged from the low of 6.5% to a high 11.9%. Figure 4.12 displays the annual percentage increases which were significant enough to maintain consistent decade long trend of increasing expenditure share (see Figure 4.11). To note again, the 19.6% apparent gain in 1998-99 was caused by an anomaly in inconsistent state data in that transition year.

93

Figure 4.12 - 500’s Annual Percent Increase

500's Other Purch srvs: Annual % Increase 25.00%

20.00% 19.6%

15.00% 11.9% 10.5%

10.00% 11.2% 7.6%

9.8% 8.7% 8.3% 5.00% 7.1% 6.5%

0.00%

Other purchased services share of new dollars was 15.4% or $1.55 billion (see Tables 4.3

& 4.4). In terms of dollars this increase was third highest within the object series

(following salaries and benefits) and was very close to the 200’s benefit dollars share increase of $1.67 billion.

Supplies: 600 Object Series

As a percent share of total spending the Supply (600’s) objects gained a minimal

.4% share of spending from 1997-98 to 2007-08. Supplies gained .4% in share of spending from 1997-08 to 2002-03 and remained level from 2002-03 to 2007-08. Supply

94

share of total spending remained remarkably consistent from 2000-01 to 2007-08 (see

Figure 4.13). The 600’s dimension includes energy / utility costs and it is worth noting that during this study time frame energy costs were generally stable if not declining.

Figure 4.13 - 600 Object: Percent Share of Total Expenditures

600 Supplies 4.50%

4.4% 4.40% 4.3% 4.30% 4.2% 4.3% 4.20% 4.2% 4.2% 4.2% 4.10% 4.1% 4.1% 4.00% 4.0%

3.90% 3.9% 3.80%

While overall percent share of supply expenses remained very stable, the 600’s object percentage increase from year-to-year varied widely. Figure 4.14 shows sharp declines and increases over time in an almost every other year pattern.

95

Figure 4.14 - 600’s Annual Percent Increase

600's Supplies: Annual % Increase 18.00%

16.00% 15.4%

14.00% 13.2%

11.1% 12.00%

10.00%

8.00% 6.4% 7.1% 5.2% 6.00% 7.0%

4.00% 2.5% 2.00% 2.6% 0.0% 0.00%

‐2.00%

The 600 object’s share of new dollars was 5.4% from 1997-98 to 2002-03 and

4.2% from 2002-03 to 2007-08. This translated into $242 million and $232 million respectively in the first-half and second-half (see Table 4.3 and 4.4).

Equipment: 700 Object Series

The 700 objects generally represents less than 1.5 percent share of total spending.

As a percent share of total spending the 700’s object only gained .08% from 1997-98 to

96

2007-08. Equipment represented 1.4% of total spending in 2007-08, and actually dropped below 1% of spending share in 2004-05 (see Figure 4.15). Total spending for equipment was only $313 million in 2007-08 and its share was 1.4%.

Figure 4.15 - 700 Object: Percent Share of Total Expenditures

700's Equipment 1.70% 1.6% 1.60% 1.5% 1.50% 1.4% 1.40% 1.4% 1.4% 1.30% 1.3% 1.20% 1.2% 1.10% 1.1% 1.0% 1.00% 1.0% .9% 0.90%

0.80%

Share Change = ‐.12% 1st Half.... +.20% 2nd Half

Overall Change = +.08%

While there were wide variations in equipment percent spending increases from year to

year, the amount of equipment spending overall is not significant enough to alter the percentage spending share (see Figure 4.16). The large percentage increases year-to-year from 2002-03 to 2007-08 did translate into approximately .3% increase in spending share for the second-half. Relative to the total, large percent equipment expenditure increases

had little effect on percentage share of spending since the share was so minor.

97

Figure 4.16 - 700’s Annual Percent Increase

700's Equipment: Annual % Increase

40.00% 35.00% 35.0% 33.9% 30.00% 25.00% 20.00% 15.00% 8.1% 12.2% 10.00% 2.3% 1.0% 5.00% 0.7% 0.00% ‐5.00% ‐2.1% ‐10.00% ‐5.5% ‐15.00% ‐10.1%

Other Objects: 800 Object Series:

As a percent share of total spending the Other Objects (800’s) dimension gained

.8% from 1997-98 to 2007-08 (see Figure 4.17). Then 800’s share of expenditures remained relatively stable over the study. This dimension is often labeled “dues & fees” but one of the major expenses accounted for here would be interest (paid) on long-term and short-term debt (principal is paid in the 900 series). The share of new dollars from

1997-98 to 2003-03 was $221 million and $233 million from 2002-03 to 2007-08. A total of $455.2 million or 4.5% of new dollars flowed to the 800 dimension.

98

Figure 4.17 - 800 Object: Percent Share of Total Expenditures

800 Other Objects

3.70%

3.50% 3.4% 3.5% 3.5% 3.3% 3.3% 3.3% 3.30%

3.3% 3.2% 3.2% 3.10% 3.1%

2.90%

2.70% 2.7%

2.50%

Share change = +.6% 1st Half.....+.2% 2nd Half

Overall change = +.8%

99

Figure 4.18 - 800’s Annual Percent Increase

800's Other Objects: Annual % Increase 35.00%

30.00% 29.3% 25.00%

20.00%

15.00% 10.2% 10.9% 7.0% 10.0% 10.00% 6.6% 5.00% 7.7% 4.3% 4.9% 0.00% ‐0.7% ‐5.00%

Other Financing Uses: 900 Object Series

As a percent share of total spending the Other Financing uses (900’s) dimension

gained 2.6% from 1997-98 to 2007-08 (See Figure 4.19). This translates in over $1.2

billion dollars or 12.0% of new money flowing to this object dimension (See Tables 4.3

& 4.4). Change in 900 object share was 1.4% from 1997-98 to 2002-03 and 1.2% from

2002-03 to 2007-08. While the overall trend was a rising increase in share, the share

increase trend grew at an increased pace during the second half of the study time frame.

Debt, as a major spending component generally has a combined impact in the 800’s and

900’s as interest and principal payments are recorded respectively. Displaying a similar upward change in trend in 2004-05, both the 800’s and 900’s combined to increase share

by 1.4% from 2005-06 to 2006-07.

100

Figure 4.19 - 900 Object: Percent Share of Total Expenditures

900 Other Uses 9.00%

8.50% 8.2% 8.7% 8.00% 8.3% 7.4% 7.5% 7.4% 7.50%

7.00% 6.7% 6.6% 7.2% 7.0% 6.50%

6.00% 6.1% 5.50%

5.00%

Share Change = +1.4% 1st half.....+1.2% 2nd half

Overall Change = +2.6%

In the first-half of the study a 9.30% annual increase was followed by a 12.29% increase

and frontloaded the first half change. A 20.71% increase in 2005-06 (see Figure 4.20)

frontloaded the remaining three years of the study in the second half of the study. All of which resulted in an overall increase share of spending.

101

Figure 4.20 - 900’s Annual Percent Increase

900's Other Uses: Annual % Increase 25.00% 20.7%

20.00% 19.6%

15.00% 12.3%

9.3% 9.2% 10.00% 5.9% 6.8% 5.00% 5.8% 6.0% 3.3% 0.00%

Statewide Object Summary

Changing share trends (inclusive of dollar impact) in the two halves of the study indicate significant scope of activity particularly for several of the larger (dollar & % share) object categories. Salary loss of 8.3% in share of total expenditures was not expected, and it lost that share while still commanding an overall 35.2% of new funds into the system. In the first-half of the study nearly 46% of new expenditures flowing directly to salaries were also not expected. The rapid first-half of 200’s (benefits) drop in share was expected but not at the share rate observed. Revenues supporting this object at the beginning of the study had to be allocated elsewhere, and those dollars amounts would be significant. The subsequent observed “re-building” of that money base in the second-half of the study is nearly $1.5 billion dollars in a five year period in just benefits

102

alone. Total benefit growth was $1.67 billion. With such large dollar amounts moving around across key object categories, the entire Commonwealth’s school finance picture at

both local and state levels would by necessity have to be impacted. Further, spending

reallocation among functions and objects would impact reported instructional and non-

instructional expenses.

Other purchased services (500’s) trends are nearly a straight-line increase in share

every year. This dimension includes the major factor of charter school tuition policy as

well as other tuition payments. Tuition for special education and career and technology

centers would be two examples of tuition expense in addition to charter payments.

Another major expense in the 500’s is public and non-public transportation which also is

recorded within this dimension. The overall 15.4% percent of new funds captured for the

500’s is just behind benefits at 16.6% of new funds The 500’s object growth of $695

million in the first-half followed by $895 million in the second-half is a close third

behind benefit growth. Total 500’s growth of $1.55 billion showed consistent straight-

line growth trend over the entire study.

The other major share change in the 900’s at nearly 12% of the new funds for

educational expenditures is significant and reflects a 2.6% overall share change. The

800’s and 900’s together capture significant portions of new or additional capital

spending costs and payments for general obligation borrowing (debt service). The 900’s

grew $1.2 billion over the study which is the fourth largest growth. When combining the

800’s and 900’s together, the growth is $1.65 billion. These four dominate objects drove

dollar flow, and Figure 4.21 shows those percent shares of new dollar expenditure growth

by object code. New, or additional revenues in support of analyzed district expenditures

103

totaled over ten billion dollars from 1997-98 to 2007-08. From 1997-98 to 2007-08 salary

(100 object series) lost nearly 8.3% share, yet it still took in 35.2% of new dollars

flowing in to the system.

Figure 4.21 – Bar Chart: Share of New Dollars to Each Object

Share of New Dollars by Object 1997‐98 to 2007‐08 40.00% 35.2% 35.00%

30.00%

25.00% 16.6% 20.00% 15.4% 12.0% 15.00% 7.6% 10.00% 2.4% 4.7% 1.5% 4.5% 5.00%

0.00%

Analysis by Function

Major Function Analysis

A summary review of major function and selected sub-function expenditure trends

was conducted in conjunction with the object analyses. Functional analysis is the

predominant norm in school finance studies; however, functional review in this study

serves to provide an alternate perspective to the same expenditures analyzed in the object

series. It is both supplementary and complementary to the object review.

104

Review of expenditure functions begins at the highest level perspective which

consists of the five the major functions. The instructional series (1000’s) trended with a

steady loss of expenditure percent share over the study timeframe. This was not an

expected result. Meanwhile, the 5000 series (Other Financing Uses) revealed a steady increase in share over the studies timeframe. The 2000 Support Services, 3000 Non-

Instructional Services and the 4000 Facilities & Acquisition functions basically remained unchanged. In essence, from the 50,000 foot view the major function provides, trends show that the 1000 function loss of share was nearly all confiscated by the 5000 function’s increase in share (see Table 4.4).

Figure 4.22 plots the changes in percent share of expenditures for each of the five major functions for the first-half, second half, and the overall study timeframe. Data labels note the overall share changes for the major functions.

105

Figure 4.22 – Bar Chart: Percent Change in Share by Major Function

3.00% 2.6% Change in % Share: Major Function – Statewide 2.00% average all 501 School Districts % share change 1.00% 1997‐98 0.10% 0.00% 0.00% to 2002‐ 0.00% 03

‐1.00% % share ‐2.00% change 2002‐03 ‐2.7% to 2007‐ ‐3.00% 08 1000 2000

Financing % share

4000 change Construction 3000

Services

and

1997‐98 Instruction 5000 Noninstructional

to 2007‐

of 08

Services Uses Support Improvement

Acquisition,

Expenditures

and Operation Other Facilities

106

Table 4.4 – Table of Major Function % Share

Pennslvania School Districts: Major Function Major Function Dimension Percent Share of Total Expenditures % Share 1997‐98 % 2002‐03 % 2007‐08 % Change Expenditure Fiscal Year Share Share Share from 1998 to 2008

1000 Function: Instruction 59.69% 58.04% 56.99% ‐2.70%

2000 Function: Support Services 29.08% 29.46% 29.18% 0.10% 3000 Function: Operation of Noninstructional Services 1.83% 1.91% 1.83% 0.00% 4000 Function: Facilities Acquisition, Construction and Improvement 0.16% 0.19% 0.18% 0.02% 5000 Function: Other Expenditures and Financing Uses 9.24% 10.40% 11.82% 2.58%

The share change from Instruction to Other financing uses provides a unique picture of

fund movement over time. In both cases, the direction of share gain or loss was

consistent in the first half and second half of the study. Clearly, Instruction’s share loss

was greater in the first half and the loss slowed pace in the second half. Meanwhile, Other

Financing Uses had an increasing trend (in gain of share) in the second half over the first.

This evidence, when combined with analysis of the Object share changes, reveals a more

complete picture of change and trend. Results show object share changes at significant

levels, and the major function level clearly indicate share flow away from instructional

spending accounts.

Similar to the object review, total Pennsylvania district expenditures state-wide in

2007-08 was $22.9 billion, so a full percentage point change in share represents $229 million (See Table 4.a). Table 4.5 lists the actual share change percentages for the split-

107

time frame of the study. While minimal (two decimal places required) it can be noted that the 2000’s, 3000’s and 4000’s all lost a tiny share in the second half, and overall held share consistently over the decade.

Table 4.5- Major Function Share Changes

% s har e % share % s har e Major change change change 1997-98 2002-03 to 1997-98 to Function to 2002-03 2007-08 2007-08 Instruction 1000 ‐1.65% ‐1.05% ‐2.70% Support Services 2000 0.38% ‐0.28% 0.10% Operation of Noninstruction al Services 0.08% ‐0.08% 0.00% 3000 Facilitie s Acquisition, Construction and 0.03% ‐0.01% 0.02% Improvement 4000 Other Expenditures and Financing 1.16% 1.42% 2.58% Uses 5000

Major function review provided additional information, but with the limited two dimension result (1000 and 5000) it raised more questions. To gain deeper perspective on function expenditure trends, thirteen key sub-functions were purposely selected and analyzed for trend. The sub-functions were selected based on size (dollar) and key functional roles within education expenditures; and combined they represent 88% of total expenditures. Table 4.f presents these data in dollar format and Table 4.g converts the dollars into share of total expenditures. It is noted again that state data contains an anomaly between function and object data for the transition year from 1996-97 to 1997-

108

98, however, for all other years of the study data reconcile between the two expenditure

dimensions.

Sub-Function Analysis

Table 4.6- Sub-Function Expenditure (Un-Adjusted Dollars)

Pennslvania School District Level Expenditures - K-12 Major Function Dimension & Pennsylvania Selected sub-functions Expenditure Fiscal Year 1997-98 2002-03 2007-08 Instruction 1000$ 8,203,783,707 $ 10,087,895,689 $ 13,059,348,549 Regular Programs - E/S 1100$ 6,112,215,786 $ 7,148,698,483 $ 9,203,962,604 Special and Gifted Education 1200$ 1,299,258,870 $ 1,898,553,576 $ 2,762,992,090 Vocational Education Programs 1300$ 475,235,282 $ 542,095,735 $ 619,604,419 Additional Other Instruction Programs 1490 $ 215,877,410 $ 316,213,872 $ 146,382,242

Support Services 2000$ 3,996,844,934 $ 5,120,103,403 $ 6,686,419,974 Pupil Personnel 2100$ 383,295,974 $ 485,376,529 $ 625,832,478 Guidance Services 2120$ 280,017,258 $ 332,632,462 $ 413,411,810 Psychological Services 2140$ 48,162,058 $ 70,348,579 $ 96,138,860 Instructional Staff 2200$ 407,104,691 $ 520,944,809 $ 702,716,006 Administration 2300$ 871,849,079 $ 1,060,969,863 $ 1,313,518,118 Office Of The Principal Services 2380$ 561,073,420 $ 681,346,466 $ 844,790,846 Operation & Maintenance of Plant Services 2600 $ 1,252,143,331 $ 1,575,675,992 $ 2,046,188,435 Student Transportation Services 2700$ 675,558,466 $ 880,762,208 $ 1,180,030,844 Nonpublic Transportation 2750$ 102,850,392 $ 123,989,357 $ 201,224,648 Operation of Noninstructional Services 3000 $ 251,297,888 $ 332,104,068 $ 418,988,982

Facilities Acquisition, Construction $ 21,727,937 $ 33,206,195 $ 42,164,656 and Improvement 4000

Other Expenditures and Financing Uses 5000 $ 1,270,281,146 $ 1,808,242,940 $ 2,708,297,518 Total Expenditures (1000 thru 5000 $ 13,743,935,612 $ 17,381,552,295 $ 22,915,219,678 levels)

Table 4.7 lists the selected sub-function percent shares, share changes, and the actual percentage dollar growth of the specific line item. Within instruction (1000’s) it is

109

notable that special education gained 2.6% expenditure share while regular education lost

4.3% in share. Vocational education and Other Instructional programs also lost share at

.7% and .9% respectively. Within the support services function (2000’s) the largest gain in share was transportation at .20% and the largest share loss was administration at .6%.

Table 4.7: Sub-function % share; Share change; percent of dollar growth

Major Function Dimension & Selected sub- Pennslvania School District Level Expenditures - K-12 Pennsylvania functions % of Dollar 1997-98 % Share 2002-03 % Share 2007-08 % Share % Share Change Expenditure Fiscal Year Grow th Instruction 1000 59.69% 58.04% 56.99% -2.70% 52.94% Regular Programs - E/S 1100 44.47% 41.13% 40.17% -4.31% 33.71% Special and Gifted Education 1200 9.45% 10.92% 12.06% 2.60% 15.96% Vocational Education Programs 1300 3.46% 3.12% 2.70% -0.75% 1.57% Additional Other Instruction Programs 1490 1.57% 1.82% 0.64% -0.93% -0.76% Support Services 2000 29.08% 29.46% 29.18% 0.10% 29.33% Pupil Personnel 2100 2.79% 2.79% 2.73% -0.06% 2.64% Guidance Services 2120 2.04% 1.91% 1.80% -0.23% 1.45% Psychological Services 2140 0.35% 0.40% 0.42% 0.07% 0.52% Instructional Staff 2200 2.96% 3.00% 3.07% 0.10% 3.22% Administration 2300 6.34% 6.10% 5.73% -0.61% 4.82% Office Of The Principal Services 2380 4.08% 3.92% 3.69% -0.40% 3.09% Operation & Maintenance of Plant Services 2600 9.11% 9.07% 8.93% -0.18% 8.66% Student Transportation Services 2700 4.92% 5.07% 5.15% 0.23% 5.50% Nonpublic Transportation 2750 0.75% 0.71% 0.88% 0.13% 1.07% Operation of Noninstructional Services 3000 1.83% 1.91% 1.83% 0.00% 1.83% Facilities Acquisition, Construction and Improvement 4000 0.16% 0.19% 0.18% 0.03% 0.22% Other Expenditures and Financing Uses 5000 9.24% 10.40% 11.82% 2.58% 15.68% Total Expenditures (1000 thru 5000 levels) 100.00% 100.00% 100.00% 0.00% 100.00%

Five of the thirteen sub-functions show share changes greater than an absolute value of .50%. Share gain or loss is consistent across the first and second-half of the study in four of those sub-functions (see Table 4.8). Notably, the 1490 sub-function reflects a major federal accounting policy change which shifted reporting (account code entry) from one functional account code to another during the term of the study. The

110

negative share loss for the 1490 sub-function in the second- half of the study is a direct

result of that change. The only sub-function to gain more than .50% share is Special

Education (highlighted in Table 4.30). It gained 1.5% share in the first half of the study

and another 1.1% in the second-half. Interesting to note that special education’s “gain” in share occurs within the major 1000 instructional function, so even as special education gained 2.60% in share, total instruction still lost 2.70%

Table 4.8 - Sub-function 1st Half & 2nd Half Percent Share Change

% share % share % share change change change Share Change of Total Expenditures: Sub-Functions 1997-98 to 2007- 1997-98 to 2002-03 to 08 Split 2002-03 2007-08 Instruction 1000 ‐2.70% ‐1.65% ‐1.05% Regular Programs - E/S 1100 ‐4.31% ‐3.34% ‐0.96% ‐ ‐ Special and Gifted Education 1200 2.60% 1.47% 1.13% + + Vocational Education Programs 1300 ‐0.75% ‐0.34% ‐0.41% ‐ ‐ Additional Other Instruction Programs 1490 ‐0.93% 0.25% ‐1.18% + ‐ Support Services 2000 0.10% 0.38% ‐0.28% Pupil Personnel 2100 ‐0.06% 0.00% ‐0.06% Guidance Services 2120 ‐0.23% ‐0.12% ‐0.11% Psychological Services 2140 0.07% 0.05% 0.01% Instructional Staff 2200 0.10% 0.04% 0.07% Administration 2300 ‐0.61% ‐0.24% ‐0.37% ‐ ‐ Office Of The Principal Services 2380 ‐0.40% ‐0.16% ‐0.23% Operation & Maintenance of Plant Services 2600 ‐0.18% ‐0.05% ‐0.14% Student Transportation Services 2700 0.23% 0.15% 0.08% Nonpublic Transportation 2750 0.13% ‐0.03% 0.16% Operation of Noninstructional Services 3000 0.00% 0.08% ‐0.08% Facilities Acquisition, Construction and Improvement 4000 0.02% 0.03% ‐0.01% Other Expenditures and Financing Uses 5000 2.58% 1.16% 1.42% + + Total Expenditures 0.00% 0.00% 0.00%

Figure 4.23 presents total sub-function share changes over the full term of the

study. In essence, it validates that from the sub-function perspective, share changes

occurred in the 1000 and 5000 series. However, within the four sub-functions of 1100,

1200, 1300, and 1490, Figure 4.23 provides perspective on changes within these

111

instructional areas. Again, this graph is presented at two decimals due to capturing minor changes in some sub-functions.

Figure 4.23- Bar Chart of Sub-function % Share Change

Selected Sub‐ Functions: % Share Change from 1997‐98 to 2007‐08

3.00% 2.6% 2.6% 2.00%

1.00% 0.1% 0.1% 0.1% 0.2% 0.1% 0.0% 0.00% ‐0.1% ‐0.2% 0.0% ‐0.2% ‐1.00% ‐0.4% ‐0.8% ‐0.9% ‐0.6% ‐2.00%

‐3.00% ‐2.7% ‐4.00%

‐5.00% ‐4.3% and…

1100 1300 2100 3000 1000 1200 1490 2000 2120 5000 2140 2300 2200 2380 2600 2700 2750

E/S Staff Uses ‐

Services Services Services Services Services Services Services

Programs Programs Education Personnel

Instruction Construction

Plant

Programs Financing Administration Transportation

Pupil of Support Gifted

Principal

Instructional

Guidance and

Education

Instruction and

The

Psychological

Regular Transportation Acquisition,

Of

Noninstructional Nonpublic

Other

of

Special Maintenance

Office Vocational Student & Expenditures

Facilities

Additional Operation Other

Operation

112

Function Share Trends

Continuing the analysis of function and sub-function account code dimensions, trend lines of percent share of spending over the entire study was plotted to show paths traveled and patterns.

Instruction

Overall from 1997-98 to 2007-08 instructional services (1000 series dimensions, see Figure 4.24) lost share of total expenditures in both the first half and the second half of the study. Instruction lost the largest share within the five major functional dimensions at 2.7%. The percentage share loss was higher in the first-half, and the loss trend slowed in the second-half. The downward path of loss-in-trend was only interrupted once in

2004-05, which revealed a slight share increase of .3%, and then downward trend pattern returned to the end of the study.

Figure 4.24 – Instruction Percent Share of Total Expenditures

Total Instruction 1000's: % Share 60.00% 59.7% 59.50% 59.3% 59.00% 58.7% 58.50% 58.4% 58.3% 58.00% 58.1% 58.0% 57.4% 57.50% 58.0% 57.1% 57.00% 56.50% 57.0% 56.00% 55.50%

Within the 1000 instructional series, four of the traditional (and largest) sub- functions were analyzed for change in share. Regular education programs (1100’s) are by

113

far the largest dollar instructional category. Regular programs had total share loss of

4.3%, which was the largest share change of all functions reviewed. Regular education

share loss was a rapid 3.3% in the first-half, and an additional loss of 1.0% continued in

the second-half. Regular education’s loss of share was not entirely unexpected, but the

scope of it was unexpected.

Figure 4.25 – 1100 Regular Education Share Trend

Regular Education ‐ Elem & Sec 1100's: % Share of Total Expenditures 45.50% 44.5% 44.50% 43.8% 43.50% 42.8% 42.50% 41.6% 41.50% 42.3% 40.7% 40.2% 40.50% 41.1% 39.7% 40.6% 39.50% 39.5% 38.50%

Special Education

Special education programs (1200’s) conversely had the largest gain in share of all selected functions analyzed. Special education’s share increased +1.47% in the first- half and +1.13% in the second-half. The trend line was a consistent straight-line with not real observed diversions from that growth. Special education spending in 1997-98

(1200’s) was $1.3 billion and grew to $2.8 billion in 2007-08 for a very significant 112% increase in the decade. Total share gain of +2.6% in special education is notable when observing the overall 1000 instructional series lost 2.7% share and regular education lost

4.3% share (see Figure 4.26).

114

Figure 4.26 – 1200 Special Education Share Trend

Special & Gifted Education 1200's: % Share of Total Expenditures 12.50% 12.06% 12.00% 11.50% 11.97% 11.71% 11.00% 11.57% 11.19% 10.50% 10.92% 10.56% 10.00% 10.31% 9.50% 9.81% 10.10% 9.45% 9.00% 8.50%

State aid for special education financing in Pennsylvania is a census-based formula, and since ADM’s overall did not grow very much across the state, this would have implications for local revenues when facing such expenditure growth4. Pockets within the state of high special education programs / students would be expected to face challenging funding needs.

Vocational and Other Education

From 1997-98 to 2007-08 vocational education programs (1300’s) lost share of

.7% which was trended across the first-half and second-half fairly evenly at -.3% and -

.4% respectively (See Figure 4.27). Other instructional programs (1490) lost share of -

.93% overall with a.25% gain in the first-half and -1.18% loss of share in the second-half.

Vocational function percentage actual dollar growth annually was also consistent and steady across the study time frame even though the function lost spending share as a percent of the total overall (See Figure 4.28).

4 See also Baker and Ramsey (2010) 115

Figure 4.27 – 1300 Vocational Education Share Trend

Vocational Education 1300's: % Share of Total Expenditures 3.70% 3.5% 3.50% 3.4%

3.30%

3.10% 3.2% 3.2% 3.2% 3.1% 2.90% 3.0% 2.9% 2.9% 2.70% 2.8% 2.7% 2.50%

Figure 4.28 – 1490 Other Instruction Share Trend

Other Instructional Programs 1490's: % Share of Total Expenditures 2.50%

2.00% 1.9% 1.50% 1.8% 1.8% 1.8% 1.8% 1.9% 1.7% 1.6% 1.6% 1.4% 1.00%

0.50% 0.6%

0.00%

For the early years of this study, the majority of Federal Title I program dollars were recorded (accounted for) in function (1490’s). In the second-half of the study

116

Federal policy altered account code classification and moved significant expenditure

dollars to the 1190 sub-function from 1490. Interestingly, had it not been for significant

portions of Title I programs being re-allocated to the 1190 function; the loss of share for

regular instruction (1100) would have been greater (i.e. 1190 expenditure is a sub-set of

1100 sub-function and 1190 dollars are included in the 1100 dollars). In essence, the

1100’s loss of share would be about 1.0% greater than the data show. Federal dollars

which were shifted (via mandated federal accounting changes) stayed within the total

1000 series dimension, so total instructional share loss was not biased by federal dollar

accounting changes. Of course, any Federal funds which did flow directly to functional

areas outside of instruction would impact instructional and non-instructional accounting

(e.g. Federal programs targeting parent involvement, community relations, or student

support would not be accounted for in the instructional functions).

Support Services

Support services (major 2000 series function) share of total spending remained

basically unchanged gaining .1% share from 1997-98 to 2007-08. Support Services gained .4% in the first five years and then lost .3% share over the second half of the study

to net the minimal positive gain (See Figure 4.29). Share change over all was small if not minuscule in several of the sub-functions of this series. Two more prominent sub- functions, Principal Services and Facilities / Maintenance are reviewed individually (see

Figure 4.30 and Figure 4.31) and then the other sub-functions are grouped together (see

Figure 4.32) to finish the 2000 series.

117

Figure 4.29 – 2000 Support Services Share Trend

2000's Support Services ‐ Percent Share of Total Expenditures 29.80% 29.7% 29.70% 29.6% 29.60% 29.6% 29.5% 29.50% 29.5% 29.40% 29.5% 29.5% 29.4% 29.30% 29.3% 29.20% 29.2% 29.10% 29.1% 29.00%

Notably, within administration itself, the sub-function 2380 Office of the

Principal, revealed a share loss of nearly .4%, which would have contributed heavily to the -.6% in the 2300 total expenditure share loss (see Figure 4.30).

Figure 4.30 – 2380 Office of Principal Share Trend

Office Of Principal Services 2380's: % Share of Total Expenditures 4.20% 4.1% 4.10% 4.0% 4.0% 4.00% 3.9% 3.9% 3.9% 3.9% 3.90% 3.80% 3.9% 3.8% 3.70% 3.7% 3.7% 3.60%

118

Facility operations (2600’s sub-function) revealed a two year gain of .2% from

1998-99 to 2000-01, losing that gain immediately in 2001-02 and then trended remarkably consistent to the end of the study.

Figure 4.31 – 2600 Plant Operations Share Trend

Operation & Maintenance of Plant 2600's: % Share of Total Expenditures 9.40% 9.3% 9.30%

9.20% 9.1% 9.2% 9.10% 9.05% 9.00% 9.1% 9.0% 9.0% 9.1% 9.0% 9.0% 8.90% 8.9% 8.80%

While individual charts are valuable, Figure 4.32 plots six key selected sub- functions whose percent shares allow for similar scale on the chart for graphing purposes.

These key sub-functions represent a mix of direct services in support of students and student instruction as well as administration and transportation. The data remarkably reflect stable trends at these sub-functions.

The largest share decrease within the 2000 functions was in 2300’s (Total

Administration) sub-function which lost .6% share. Administrative share lost .2% in the first-half of the study, and the rate of loss increased to .4% of share in the second-half.

Notably, student transportation gained .2% in share of total expenditures and Plant operations decreased share by .2%, in essence offsetting each other’s movement within

119

the 2000’s functional share. The modest rise in transportation was unexpected, as growth in bus and fuel costs had led to intuitively expect a higher share growth than revealed.

Figure 4.32 – Selected Sub-Functions Share Trends

Selected Support Sub‐functions ‐ % Share of Total Expenditures

7.00% Administration 6.3% 2300 5.7% 6.00% Student Transportation 5.00% Services 2700 5.1% 4.9% Instructional 4.00% Staff 2200

3.1% 3.0% 3.00% Pupil Personnel 2.8% 2.7% 2100 2.00% 2.0% Guidance 1.8% Services 2120 1.00% 0.4% 0.4% Psychological Services 2140 0.00%

Non-instructional and Facilities improvement

Changes in share for Operation of non-instructional Services (3000 series dimensions)

were non-remarkable for the term of the study with very minor offsetting changes

through the mid-years (See figure 4.33).

120

Figure 4.33 – 3000 Non-instruction Share Trend

Operation of Noninstructional Services 3000 ‐ % Share of Total Expenditures 1.95%

1.90% 1.9% 1.85% 1.8% 1.8% 1.80%

1.75%

1.70%

1.65%

Facilities acquisition, construction and improvement

The 4000 function trend was non-remarkable indicating little movement over

time. It is worth noting that the title of this function is can be mis-leading (See Figure

4.34). This particular function is reported within school district Annual Financial Reports for the General Fund only. In most cases large borrowings and school construction programs are accounted for within a specific construction or bond fund and do not necessarily show up in the general operating fund. So while large capital intensive projects and construction may be occuring, they are generally not reported here.

121

Figure 4.34 - 4000 Facilities Acquisition, Construction Share Trend

Facilities Acquisition, Construction and Improvement 4000: % Share of Total Expenditures 0.25% 0.19% 0.20% 0.16% 0.18% 0.15%

0.10%

0.05%

0.00%

Other Financing Uses

Other expenditures and financing uses (5000 series dimension) gained 2.6% share of total expenditures from 1997-98 to 2007-08. With respect to financial accounting, this major function records substantive transfers to other funds (e.g. athletic, food service,

capital reserve, health trust, and debt service funds). In the first half of the study the gain was 1.2%, and the growth pace increased an additional 1.4% in the second half. The 2.6% gain in share for the 5000’ is significant, and within a major function perspective it basically consumed the 2.7% loss of share in the instructional 1000 function. Trend line for the 5000 clearly denotes system impact toward more rapid growth in 2004-05.

122

Figure 4.35 – 5000 Other Expenditures and Financing Uses

Other Expenditures and Financing Uses 5000: % Share of Total Expenditures 12.00% 11.8%

11.50%

11.00% 10.4% 10.50%

10.00%

9.50% 9.2%

9.00%

Statewide Function Summary

The overall 2.7% share of spending loss by instruction was surprising given the

known large increases of special education spending occurring within instruction overall.

Additionally, the pace of that share loss actually increased in the second half of the study.

Logically this is contrary to the emphasis and instructional need brought about by continued and expanding focus on NCLB efforts.

Within instruction, Special education itself captured an increased share of spending of 2.6% during the study. This represented nearly $1.5 billion in (expenditure growth) money flow to special education. The year-over-year trend line for special

education clearly indicates a straight-line share trend growth with no indication of

slowing down.

The reduction in share of administration, office of the principal, and plant

operations reveals indication of re-allocation of funds to other functional areas, or at the

123

very least, less new money flowing to these areas. The increase in share revealed by the

transportation function was expected; however, the growth was minimal at best and is

somewhat counter intuitive to expectations. This finding has implications for the

significant 500 object code growth since a major portion of transportation costs are coded

to the 500’s, yet the entire transportation function itself did not increase share

significantly.

Revenues

To provide additional understanding of the fiscal changes of school districts, a basic review of revenue share changes was conducted utilizing the same format as the function and object expenditure dimensions. The revenue data were also analyzed for observation within the first half and second half of the study’s time. Table 4.9 lists the year-by-year percent shares for Local, State, Federal, and Other revenues. Table 4.10 lists the share changes within the first and second-halves of the study.

Table 4.9 – Revenues Percent Share of Total Revenues

Revenues as a % Share by Fiscal Year

Local State Federal Other Revenue Reveneu Revenue Revenue Year as a % of as a % of as a % of as a % of Total Total Total Total Revenue Revenue Revenue Revenue 1997-98 56.69% 38.47% 3.13% 1.71% 1998-99 57.26% 38.27% 3.21% 1.26% 1999-00 57.54% 37.89% 3.55% 1.01% 2000-01 57.92% 37.33% 3.62% 1.13% 2001-02 56.70% 36.37% 3.69% 3.23%

2002-03 58.10% 36.69% 4.13% 1.08% 2003-04 58.10% 35.77% 4.49% 1.64% 2004-05 58.27% 35.98% 4.29% 1.46% 2005-06 59.12% 35.35% 4.09% 1.44% 2006-07 59.29% 36.08% 3.65% 0.98% 2007-08 59.09% 36.33% 3.55% 1.03% Share 124 Change 2.41% -2.14% 0.42% -0.68%

Table 4.10 Revenue Share Changes

Revenue Share Change by First-Half and Second-Half of Study 1997- 2002- 1997- Revenues 98 to 03 To 98 2002- 2007- to2007- Local Rev % 1.42% 0.99% 2.41% share change

State Rev % -1.79% -0.36% -2.14% Share Change

Federal Rev % 1.00% -0.58% 0.42% Share Change

Other Rev % -0.63% -0.05% -0.68% Share Change

Figure 4.36 graphs the change in share for total local revenue, total state revenue, and total federal revenue. Changes occurring in the first and second halves of the study are plotted as well as the total. While state share continues a 30 year decline with share loss in both halves, the data clearly show a slower pace of that loss during the second-half of the study. The state loss of share is combined with federal loss of share in the second half for nearly a 1% of total share change.

Interestingly, in the midst of National and State NCLB efforts, federal dollars as a share of total spending shows a loss of share over that time period. For the total period of study, total local revenue share increases 2.4% while total state share dropped by 2.1%.

125

Figure 4.36 – Bar chart of Revenue Change in Share

Change in Percent Share Local, State and Federal Revenues: Total Local All 501 School Districts Rev: 3.00% 2.4% Change in Share of 2.00% Total 1.4% Revenue 1.0% 1.0% 1.00% Total State : 0.4% Change in Share of 0.00% Total Revenue ‐0.4% ‐1.00% ‐0.6%

Federal : ‐2.00% ‐1.8% Change in ‐2.1% Share of total ‐3.00% Revenue 1997‐98 to 2007‐08 1997‐98 to 2002‐03 2002‐03 to 2007‐08 change change change

Revenue Share Trends

Plotting the trend line for percent share of total for local revenue reveals the general upward slope of increased share as well as distinct changes in slope and direction

(See Figure 4.37). A large 1.0% share change anomaly shows up in 2001-02 and then share growth continues with a leveling out in the last three years.

126

Figure 4.37 - Local Revenue Percent Share Trend Line

Local Revenue as % Share of Total Revenue

59.50% 59.3% 59.1% 59.00% 59.1%

58.50% 57.9% 58.1% 58.3% 58.00% 58.1% 57.5% 57.50%

57.00% 57.3%

56.50% 56.7% 56.7%

56.00%

For the state share trend, there was a steep consistent decline in the first-half followed by a much more erratic second-half (See Figure 4.38). State revenue share loss shows a smooth straight-line trend in the first-half, and is followed by erratic behavior in the second-half. Beginning in 2001-02 share trend alternates from growth-to-loss and ends with two fiscal years of small gains at the end of the study.

127

Figure 4.38 – State Revenue Percent Share Trend Line

State Revenue as % Share of Total Revenue 39.00% 38.5% 38.50% 38.3% 38.00% 37.9% 37.50% 37.3% 37.00% 36.7% 36.50% 36.3% 36.0% 36.00% 36.4% 36.1% 35.50% 35.8% 35.3% 35.00%

Table 4.11 shows the percent annual percent increase for the revenue sources. For the local and state, the percentages show fairly stable activity in the first-half of the study and then significant variability in the second. The State average percent increase was 4.6% and the Local revenue percent increase averaged 5.7% during the term of the study.

Generally, both local and state revenue grew (as a percent) at higher rates in the second- half of the study. Federal increases dropped to 2.9% on average after averaging 10.7% in the first-half of the study. Annual percent increase in state funding exceeded the total local percent increase only three years of the study. All three of those events occurred in the final four years from 2004-05 to 2007-08.

128

Table 4.11 – Revenues Percent Increase

Revenues: Percent increase Year‐over‐Year

% Increase Total Other Rev State % Federal % over prior Local % % Increase Increase fiscal Year Increase Increase

1998‐99 5.2% 3.6% 6.8% ‐23.4%

1999‐00 4.4% 2.9% 15.1% ‐16.8%

2000‐01 5.0% 2.7% 6.2% 17.1%

2001‐02 4.8% 4.3% 9.3% 205.1%

2002‐03 6.2% 4.6% 16.0% ‐65.4%

2003‐04 6.7% 4.0% 16.0% 62.1%

2004‐05 7.3% 7.7% 2.3% ‐5.1%

2005‐06 7.3% 3.8% 0.7% 4.9%

2006‐07 5.9% 7.8% ‐5.8% ‐28.0%

2007‐08 3.9% 5.0% 1.3% 9.2%

Average 5.7% 4.6% 6.8% 16.0%

First half 5.1% 3.6% 10.7% 23.3%

Second Half 6.2% 5.7% 2.9% 8.6%

When the annual percent increases are plotted (See Figure 4.39) the second-half variability is plainly visible. The State revenues show erratic percent increases in the

129

second-half while local revenues show significant downward movement in the same half,

ending at a low point for the decade in 2008.

Figure 4.39 – Graph of State and Local Revenues Annual Percent Increase

9.00%

8.00% 7.7% 7.3% 7.3% 7.8% 7.00% 6.7% Total Local % 6.2% Increase 6.00% 5.2% 5.0% 4.8% 5.9% 5.00% 4.4% 4.6% 5.0% 4.00% 4.3% 3.8% 4.0% 3.9% 3.00% 3.6% 2.9% 2.00% 2.7% State % 1.00% Increase

0.00%

A final revenue analysis looked at change in share within local revenue itself as

there are always concerns about taxes. Taxes are the major component of total local

revenue, so observations of changes in share within total local revenues may offer insight. This analysis provided perspective on the observed local revenue share increase.

For the first-half of the study total local revenues and total local taxes revealed changes in percent share that were identical. For the second-half of the study local taxes actually lost share while all other local revenue sources gained share (See Figure 4.40). Overall,

130

from1997-98 to 2007-08, local tax share grew 1.2% and total local revenue grew by

2.4%. The data strongly indicate that in the second-half of the study something altered share between local revenue sources other than taxes themselves. The change represents more than a full percentage point of total revenues. Similar to the Federal function accounting change to 1190 from 1490 on the expenditure side, in 2006-07 certain Federal

IDEA revenues (to districts) were re-classified and have since been recorded in the

6800’s of the 6000’s local revenue series of accounts. This accounting change alone would nearly explain almost all of this significant impact. Further, increased interest earnings in a growing rate-of-return era may also contribute to the local total growth outside of taxes.

Figure 4.40 – Local Revenues and Local Taxes as a Percent Share of Total Revenues

Change in Share of Total Revenues for Local taxes and Total Local revenues

3.00% Local Taxes: 2.50% 2.4% Change in Share of 2.00% Total Revenue 1.4% 1.4% 1.50% 1.2% 1.0% Total 1.00% Local Rev: Change in 0.50% Share of Total 0.00% Revenue

‐0.2% ‐0.50% 1997‐98 to 2007‐08 change 1997‐98 to 2002‐03 change 2002‐03 to 2007‐08 change

131

School Characteristics: Object Analysis

In this section, analyses were conducted to determine if observed percentage change by major object share varied by selected school district characteristics. The two

characteristics examined in detail were district size measured by student average daily

membership (ADM) and district wealth as measured by market value personal income

ratio (MVPI). For the two major characteristics, for each fiscal year each district was

aligned to their specific ADM or MVPI measure. Sorts were then run to segment three

different groups of fifty districts each, for each fiscal year. For ADM this grouping

targeted the fifty smallest, the fifty mid-sized, and the fifty largest enrollment districts in

the state. The process was repeated for MVPI which resulted in the fifty lowest wealth,

the fifty middle wealth, and the fifty highest wealth districts.

Average Daily Membership (ADM)

Average daily membership in Pennsylvania is very closely correlated to what can be best described as actual district enrollment. It is a measure of the total students

‘enrolled’ in a particular district. This is to differentiate the ADM measure from related measures of average daily attendance. Table 4.12 below shows the results of object analysis by district ADM size. It lists the share change for all three groupings and

includes the average share change for all districts as previously reported. Object share

changes over time indicate variability based on the size of the district.

Review of Table 4.l2 provides observation of variability in the share change especially in the 100’s, 500’s, 800’s and 900’s. Observations of significant share change activity in these objects are consistent with overall object findings. However, utilizing

132

district ‘size’ with ADM as a proxy, share change variability is indicated at the grouping levels selected within several object levels.

Table 4.l2 Changes in Percent Share By ADM Characteristic

Change in % Share from 1997‐98 to 100's 200's 300's 400's 500's 600's 700's 800's 900's 2007‐08

Statewide all Districts ‐8.3% ‐0.2% 1.2% ‐0.2% 3.5% 0.4% 0.1% 0.8% 2.6% Smallest 50 ADM Districts ‐10.0% ‐0.1% 0.9% ‐0.4% 5.2% 0.4% 0.0% ‐0.5% 4.6% Middle ADM Districts ‐6.5% 0.6% 0.7% ‐0.2% 1.9% 0.5% 0.2% 0.1% 2.7% Largest ADM Districts ‐9.6% ‐0.2% 1.5% ‐0.2% 3.7% 0.5% 0.2% 1.0% 3.2%

Year-to-year share changes at the small ADM district level displayed high variability year-to-year, most likely due to small size itself. Erratic trend lines can be observed in small ADM districts within the various objects (e.g. see Table 4.7). Further, small enrollment districts lost 10.0% share of spending on salaries versus the 8.3% of the average district. Significant variance of share change continued in the 500’s, 800’s and

900’s for the smaller districts. When viewed in sum total for just those four objects, small district expenditures were re-allocated by over 20% in share change over the course of the study.

As a residual finding, the average enrollment of the 50 small districts dropped over time by an average of 82 students or 10.5%. The average ADM of these districts was

779 in 1997-98 and dropped to 697 in 2007-08. In essence, small districts got significantly smaller. (See Table 4.13)

133

Table 4.13 ADM Average by Fiscal Year by Grouping

Average ADM Size for the Targeted Districts

Average ADM Average ADM Average ADM Fiscal Year 50 Smallest 50 Middle 50 Largest Dis tr icts Dis tr icts Dis tr icts 1997‐98 779 2,453 13,690 1998‐99 773 2,447 13,727 1999‐00 765 2,433 13,779 2000‐01 754 2,416 13,779 2001‐02 750 2,404 14,077 2002‐03 738 2,390 13,968 2003‐04 750 2,367 14,011 2004‐05 727 2,344 14,045 2005‐06 720 2,335 14,050 2006-07 710 2,325 14,003 2007‐08 697 2,283 13,878

The middle ADM districts lost 7% in enrollment over time, while the 50 largest districts gained 1.4% over the study.

ADM year-over-year trend

All three ADM size sub-sets generally trended with the state average although smaller districts clearly had a larger share of expenditures flowing away from salary. In the 100 salary series, small districts exhibited erratic change in share from 2000-01 forward. This erratic pattern was observed in several of the objects (for ADM characteristic) when plotted year-by-year for share trend (See Figure 4.41).

134

Figure 4.41 -100 ADM Characteristic Share changes

100 Object Series: Change in Share (ADMS) Statewide 60.00% Average Object % Share

55.00% 50 Smallest ADM Districts 50.00%

Largest 50 45.00% ADM Districts

40.00% Middle 50 ADM Districts 35.00%

The 200 object series presented in Figure 4.42 reveals that district size groupings selected generally followed average trend within benefit expenditures. Further, within these benefit expenditures the path followed for share change is generally smooth in nature, despite the severe swing within the object level itself.

135

Figure 4.42 - 200 ADM Characteristic Share changes

200 Object Series: Change in Share (ADMS) Statewide Average 18.00% Object % Share

17.00% 50 Smallest ADM 16.00% Districts

15.00%

Largest 50 14.00% ADM Districts

13.00%

Middle 50 12.00% ADM Districts

11.00%

The 500 series (See Figure 4.43) exhibits greater variation and propensity for erratic effect on the smaller ADM districts. This occurs even while the upward growth in share trend impacted all districts. The variability of small districts is evident and appears to start at the 2000-01 fiscal year as observed in the 100 objects.

136

Figure 4.43 – 500 ADM Characteristic Share changes

500 Object Series: Changes in Statewide Share (ADMS) Average Object % 18.00% Share

16.00% 50 Smallest 14.00% ADM Districts 12.00% Largest 50 10.00% ADM Districts

8.00%

Middle 50 6.00% ADM Districts 4.00%

Variability for the small districts continues the erratic behavior in 900’s object series among the ADM selected districts (See Figure 4.44). The 900’s have linkage to capital funding needs as well as debt service obligations for borrowing. The upward trend

for the 900’s share of expenditures is reflected across the board with the caveat that small

ADM districts saw a sudden share climb in 2003-04 and again in 2007-08 to over 11% of their expenditures.

137

Figure 4.44 - 900 ADM Characteristic Share Changes

900 Object Series: Change in Share (ADM) 12.00% Statewide 11.00% Average Object % 10.00% Share 9.00% 50 8.00% Smallest ADM 7.00% Districts

6.00% Largest 50 ADM 5.00% Districts

4.00%

3.00% Middle 50 ADM 2.00% Districts

Overall, ADM data reflect evidence that trend line and variability within object share change is impacted by district ADM size. Share trends among the sub-groups generally mirror average district movements over time, however, variations can be observed. Additionally, the overall consistency observed in trend (i.e. compared to the average district) was not expected given some of the major legislative and policy changes over the time period. That said, observations reveal several objects which indicate variability and impact on expenditure share and would require additional investigation.

138

Market Value Personal Income (MVPI) Ratio

The following wealth measure review utilizes a similar analysis as conducted in the ADM characteristic. In general, MVPI grouped trend patterns follow the average district in similar direction. However, certain MVPI groupings do reveal trend which show share change over time. Variability can be observed in several objects on either side of the study mid-point as well. For some objects, “changing of positions” (crossing trend lines over time) and range differences between the lines (narrowing or increasing range of difference) was also observed in the analysis.

One of the most significant differences from the average district shows up in the

900s for low wealth districts (See Table 4.14). The change in spending share is 1.7% lower than the average district. At the 100 salary dimension, low wealth and middle wealth districts saw considerably less change in share. Surprisingly the middle wealth groupings experienced the smallest salary share change. That said, all MVPI groupings still showed lost salary share mirroring the average district trend. For benefit share expense, middle wealth and low wealth districts both reveal a .7% differential from the average district. The wealthier districts revealed a higher share change by a margin in the

800 and 900’s, most likely due to capital cost and borrowings.

139

Table 4.14 - Changes in Percent Share by MVPI Characteristic

Change in Share from 1997‐98 to 100 200 300 400 500 600 700 800 900 2007‐08

Change in Share ‐8.3% ‐0.2% 1.2% ‐0.2% 3.5% 0.4% 0.1% 0.8% 2.6% All Districts

Lowest Wealth ‐6.9% 0.5% 0.9% 0.1% 3.7% 0.6% 0.1% 0.1% 0.9% Districts

Middle Wealth ‐6.7% 0.5% 0.5% 0.0% 2.9% 0.7% 0.0% ‐0.3% 2.4% Districts

Highest Wealth ‐8.1% ‐0.1% 1.4% ‐0.3% 2.4% 0.0% 0.3% 1.4% 3.0% Districts

The 100 series reflects certain dynamics to observe (See Figure 4.45). The higher wealth districts share of salary started higher than the average and stayed there during the study. Wealthier districts generally lost salary share across the study right at the state- wide average. This was somewhat of a surprise given it was expected that being

‘wealthy’ may translate a different impact on salary expenditure share trend. The middle wealth districts began below the statewide average and ended the study basically on the average, which is reflected by their lower share loss overall.

140

Figure 4.45 – 100 MVPI Characteristic Share Changes

100 Object Series: Change in Share by MVPI 58.00% MVPI Highest 56.00% Wealth 54.00% Districts 52.00% MVPI Lowest 50.00% Wealth Districts 48.00% MVPI 46.00% Middle 44.00% Districts 42.00% All Districts 40.00% Avg

The lowest wealth districts started below the average district and ended there as well, although the differential closed by about 1.5%.

At the 500’s level, the change in share for low wealth districts was also significantly more than higher wealth, indicating greater impact on the 500’s share change for low wealth districts. Tuition costs certainly have a role here as will transportation cost, both of which have large portions of expense accounted for in this dimension. For the 500’s object, lower wealth districts started the study paying a higher share, and at the end of the study that trend continued as the range of variance actually widened (See Figure 4.46). Lower wealth districts ended the study with 13.2% of spending committed to the 500 object code, with higher wealth districts at 9.6%.

141

Figure 4.46 - 500 MVPI Characteristic Share Changes

Figure 4.60 500 Object Series: Change in Share MVPI MVPI Highest 14.00% Wealth Districts 13.00%

12.00% MVPI Lowest 11.00% Wealth Districts 10.00%

9.00% MVPI Middle 8.00% Districts

7.00% All 6.00% Districts 5.00% Avg

4.00%

A similar trend was found in the 600’s supplies with the notable difference that in the second half of the study a greater range was found among all the district sub-sets (See

Figure 4.47).

142

Figure 4.47 – 600 MVPI Characteristic Share Changes

600 Object Series: Change in Share MVPI MVPI 5.30% Highest Wealth 5.10% Districts 4.90% MVPI 4.70% Lowest Wealth 4.50% Districts

4.30% MVPI 4.10% Middle Districts 3.90%

3.70% All Districts 3.50% Avg

For the 900 object series middle and low wealth districts both exhibited variation and movement during the study. Middle wealth share increased dramtitcally for two years in the first half of the study, and low wealth dsitricts dropped rapidly in 2004-05 and then increased rapidly in the second half (See Figure 4.48).

143

Figure 4.48 - 900 MVPI Characteristic Share Changes

900 Object Series: Change in share MVPI MVPI 11.00% Highest Wealth Districts 10.00% MVPI 9.00% Lowest Wealth 8.00% Districts MVPI 7.00% Middle Districts 6.00%

All 5.00% Districts Avg 4.00%

MVPI District Sub-Group Trends

The methodology employed to assemble the lowest, middle and highest wealth districts provided a unique data set on trend for these sub-groups as a whole over time.

MVPI is an inverse measure which means the higher the number, the less wealthy the district is. The 50 lowest wealth districts as a group trended towards becoming less poor, meaning over the study they got a little wealthier in the eyes of the state. As a group the lowest wealth districts MVPI dropped by 2.3% (See Table 4.15). The middle wealth remained basically unchanged. The higher wealth districts however, trended toward becoming less wealthy. In essence, these low and high wealth sub-groups had trend convergence towards each. From 1997-98 to 2007-08 high wealth districts had MVPI

144

ratios indicating they were 14.5% “poorer’ with low wealth districts becoming 2.3% wealthier.

While outside the scope of this investigation, it is difficult to tell if these movements are significant. The convergence of MVPI for these two subsets of districts would have impacted various state subsidy distributions (where MVPI is utilized). Table

4.15 shows the average MVPI for each subset over the years of the study.

Table 4.15 – MVPI Average by Year for Each Sub-Group of 50 Districts

MVPI of MVPI of MVPI of the the the Fiscal year Lowest Middle Highest Wealth Wealth Wealth Districts Districts Districts 1997‐98 0.7872 0.5909 0.1842 1998‐99 0.7833 0.5870 0.1846 1999‐00 0.7797 0.5890 0.1894 2000‐01 0.7767 0.5867 0.1961 2001‐02 0.7766 0.5869 0.1958 2002‐03 0.7732 0.5883 0.2031 2003‐04 0.7680 0.5835 0.2125 2004‐05 0.7645 0.5843 0.2085 2005‐06 0.7671 0.5822 0.2029 2006‐07 0.7693 0.5865 0.2076 2007‐08 0.7689 0.5854 0.2110 10 yr AVG MVPI ‐0.0183 ‐0.0055 0.0268 Change

% Change in the Avg MVPI by sub‐ ‐2.3% ‐0.9% 14.5% group from 1997‐ 98 to 2007‐08

Utilization of these two major district characteristics provided support for the conceptual premise that district expenditure share trends reveal different paths based on

145

size and wealth. While some object codes showed little variation, some revealed strong evidence that the sub-group districts were impacted by or were reacting differently to all the forces at play.

Interviews

Interview data followed from notes taken and organization of the information into data groupings as per Table 4.64. A data collection sheet was prepared for each interviewed participant and a summary was prepared to combine key comments and observations within a group. The interview process provided constant comparison for the participant as the prepared material flowed across topics. Discussion themes, concepts, opinions, and categories of information combined to allow for comparison and contrast at many levels. As the interviews progressed through the prepared data results, comments and reflections were built on the previous information in a flowing manner. For example, what could be observed by a participant in one object series would often then be linked to or validated later in a either a related function or characteristic review discussion.

Utilizing feedback and experience gained from the initial interview, the interview material was organized to align with the particular subject matter being analyzed (e.g. object / function / characteristic / revenue). Table 4.16 shows the general grouping of information provided to the expert participants. It also shows how many slides (data results) were included and aligned to the specific grouping. Each participant generally discussed their observations in this order as a hard copy of the material was used as a guide. Key comments and observations offered by the participants were noted on as the conversations flowed. From an initial count of 298 key comments from the interviews,

146

comments were coded into similar topical areas provided by the participants themselves

(See Table 4.17).

Table 4.16 – Power Point Alignment of Data Results

PowerPoint PowerPoint Grouping Grouping Slide #s Slide #s

MVPI 50/50/50 Purpose 2 (District Wealth) 25 ‐ 31 Function and Object Overview 3 PSERS ECR rate 32 ADM and Expense per ADM 4 PSERS Dollar Est 33

ADM and Expense Objects: % share per ADM 5 Charters 34 ‐ 36 Objects Statewide 6 ‐ 12 Revenues 37 ‐42

Objects dollars (New Revenue: Basic Ed Money & share of $'s) 13 ‐ 15 Funding 43 ‐44 Functions and sub‐ functions 16 ‐ 19 Policy Specific 45 ADM's 50/50/50 (District size) 20 ‐ 24 Discussion / Other 46

147

Table 4.17 – Major Topic Counts from the Interviews

Major Generated Topics Count

Act 1 ( Tax restrictions) 3 Charters 13 Pensions / PSERS 14 State / Administration 16 Objects 21 Functions 15 Mid‐year splits 2 Characteristic impact 21 Revenues / Funding 17 Debt 5 Fund Balance 5 Other 3 Total: 135

While there is plenty of overlap to the guide, major generated topics areas did not necessarily align with the prepared interview guide. The flowing interview format generated conversation according to the participant’s expertise and interest areas. Some of the counts within each topic are closely related, but there were enough subtle

differences in context from participants’ response that they were placed in best fit

category.

It is noted that all of the interviewed participants were more than gracious and

definitely generous with their time. All participants exhibited a general enthusiasm and

appreciation of the prepared data. Discussions within the study’s time frame utilizing the

data set provided significant value and insights, as well as cross-reference to other

observations and responses to the data. It was clear that the focus on object code trended

148

format provided a perspective on district spending patterns which virtually none of the

participants had a real familiarity with. It was also clear that they understood the data; it

was just that they rarely, if ever, saw it analyzed in this manner. These participants were

highly experienced in state policy and funding matters related to public education, and

yet the data format presentation concept was relatively foreign.

It was evident that various spending patterns and share trends elicited numerous

kinds of responses from the participants. Examples included general surprise to direction

or strength of certain trends, or inversely, a validation and confirmation of expectation of

what was observed. Additionally, there were many counter intuitive observations within

the data they would not have imagined, and interestingly, observations of things they

would not have observed without the multiple approaches to the data. Data trends by

first-half and second-half of the study versus the overall change during the total study

time frame presented rich discussion. Participants generally were seeing this information in a format that was not commonly used or seen. The year-by-year trend mapping showing actual paths of the data over time also provided for key observation and discussion, and proved valuable just for sheer visibility and linkage to events.

Summary

With visuals provided by the Figures, data analyses revealed a continuing story of trends, changes in trends, and what those trends did over time. Observations within objects, functions, revenues and the two major school characteristics all revealed patterns of change and trend. Utilizing a multiple analysis inclusive of percent share changes, annual percent increases, actual dollar flow, total and split-study view, and the decade- long trend lines provided the participants with new perspectives.

149

Meanwhile, the interview process itself aligned well to strengthen observations in

the data as well as provide greater insight from recognized experts who worked in the

field during the study. The study focus on the nine object codes provided a significant

perspective change from the standard functional review. The inclusion of function and

object complementary comparison of the same expenditure data generated significant observations within the data. It is noted in these reviews, that utilizing just function or just object would not have provided the breadth of insight, or depth of discussions.

Actual timing of these interviews is an important consideration with regard to the

topics generated from the participants. The first interview took place September 14, 2010

and it took several weeks to re-align the materials and then schedule the rest of the

interviews. The second interview was held on October 19, 2010 and the last one finished

November 11, 2010. Governor Rendell was still in office and much political capital had been invested on a new state ‘Costing Out Study’ for school funding needs. Meanwhile, the educational community across the state was still trying figure out how Federal stimulus monies were going to play out as the recession continued. In short, while the recession officially began in 2008, direct public education impact was geographically sporadic, and many districts had been well protected via the insertion of Federal Stimulus monies.

While many things emerged from the data, particular stories clearly dominated.

Within the object series, major share trends shifts in the 100’s, 200’s, 500’s and 900’s dominated the change in spending activity. This same dominance continued in the district characteristics object series review as well. Within the functions (and sub-functions), the loss of share in instructional spending, as well as a greater loss of share within regular

150

education in the era of NCLB efforts was not expected. The growth in share for special

education as well the 5000’s (other financing uses) were no surprise to the participants,

but the scope of those changes were. Finally, the revenue review shows a continuation of

a thirty year decline in state share of public education funding (versus local share), as well as a declining Federal commitment in the second-half of the study, at least in terms of share of expenditures. Chapter five will present further perspective on these stories and interview linkage to findings.

151

Chapter 5

Patterns Revealed, Trends Emerged, and Changes Observed Over Time

During the timeframe of this study, well-developed spending patterns among expenditure dimensions were clearly observed. The patterns and changes reflected over time strongly point to major policy implementations and events. The complimentary perspectives provided by dual object and function expenditure analysis methodology

provided improved insight into emergent account code patterns. Generally, trend patterns

reflected changes in account code share of expenditures overtime, and specifically where

monies flowed to or from, and how much. Observed changes in spending patterns

exhibited differentiated behaviors among account code dimensions which contain different

types of expenditure information. For example, some account codes maintained share,

some lost or gained share, and some lost or gained share alternately. In several instances,

changes in share were so systemic in nature that their measures of spending share were

dramatically altered.

Changes in percent spending share, trend and direction of that share, and the pace

(or slope) of changes over time offered key insights into policies and events which

influenced these money flows. Data results, along with supporting observations from a

field of policy experts, offered insight to linkage of major policy events and

implementations. Some of the empirical results would appear to reflect intended design,

while others appear to be unintended consequences.

One of the more obvious findings of this study is simply that district spending

patterns changed significantly. Multi-year trend analyses covered the ten year time period

of the study—1998 through 2008. The pictures these data show over time provide

152

evidence of both the magnitude and direction of the fiscal changes occurring. Specifically

with regard to object expenditure levels, if money “leaves” one expenditure dimension and

is still being spent (i.e. not cut entirely from the budget), it must be recorded in other object dimensions. This observed ‘expenditure exchange’ ranged from being widely spread among the eight remaining objects, or highly concentrated to one receiving object.

However, and it must be emphasized, with the examination of statewide expenditures at the level of $23 billion, it takes a large and sustained force, or forces, to alter dimension spending patterns and share substantially or even noticeably. Yet, in this decade, four of

the nine objects reflected significant changes with to / from changes representing billions

of dollars in movement. Consequently, this chapter will focus on those four objects with

the largest movements: Salaries (100’s); Benefits (200’s); Other Purchased Services

(500’s); and Other Uses (900’s).

This study’s visual representation of those movements assists with policy lead and

lag time analysis and reveals breaks in trend for potential event impact investigation.

Observations of the data offer evidence of significant account code impact which provided

the basis for deeper inquiry into causation of the movement of funds. It is a natural

progression to investigate underlying forces influencing these changes. Tying data results

back to policy implementation and events is not an exact science. Taken all together

however, using these observed findings combined with informal and formal expert

corroboration, major policy linkage implications are supported.

Another finding to emerge from this study is important to note. All of the policy

experts interviewed expressed that the dual object and function perspective (e.g.

specifically the methodology in which the data were prepared and presented) was highly

153

valued. For whatever reasons, single focus on object code analysis is not commonly used at state policy levels or in the academic community. Several of the policy expert participants admitted they were not too familiar with object code expenditure data. Similar reactions were received in partial presentations of this research at three national education finance conferences (AEFA / AEFP / NEFC). It was clear that not all academics working in school finance research were familiar with the object dimension of expenditures.

However, with review and explanations of content, individuals had little trouble grasping the concepts. What was clear from the early research work, as well as interviewing the experts, was that object focus presentations were clearly valued, and the data presented many “surprises” to the audiences. The common theme was that this methodology and approach offered deeper insight into spending trends and patterns, and served to create natural inquiry for causation linkage.

What follows is a discussion which provides a focus on the major findings on the events and polices. Key explanations for observations focus in account code trends in general, with particular on object expenditures. This discussion is approached and organized by the four selected major object account code dimensions which revealed the most prominent trend behaviors with policy indicators. However, account code activity is highly inter-connected and discussion during the interviews contained many comparable references and insights among various observed data analyses.

154

Object Expenditures

100 Object Salaries

Figure 5.1 - 100 Object: Percent Share of Total Expenditures

Observation of salary’s loss of 8.3% of expenditure share was a surprise to all of those interviewed or who saw the data presented. This drop in percent share of expenditures, and particularly one this large, just was not expected. Even at this magnitude, it was not on their radar, which in and of itself speaks volumes as these experts all worked in the Commonwealth’s public education arena during this period of study.

One of the most common explanatory terms utilized by the policy experts was “counter- intuitive,” and it was a good fit for salary’s loss of share.

155

Two of the policy experts who are noted for their instructional expertise

commented, (E3 and E4) “...the 100's loss of expenditure share, given Federal focus on

Title I and NCLB and State testing and accountability efforts, is just contradictory.” A

third participant simply commented, (E5) “the trended loss in (salary) expenditure share

just was not expected.”

Meanwhile, three of the more financially-inclined policy experts offered a few key

insights for event and policy impact related to the findings. One of these policy experts

(E1) approached discussion from what he termed the ‘slope’ of the changes over the term of the study. His observations lead to discussion related to the first-half and second-half observations of the trend line changes. Salary share of expenditures fell in the first four years, but not nearly at the pace observed the second-half of the study. He attributed this to several most likely events and policy: revenue growth, freed up pension funds, and “no rules”. While more insights will follow, his point was that by spending more in other categories salary share can be driven down, but to drive down share of the largest spending object dimension, requires very significant expenditure changes.

Two of the policy experts commented on factors related to statewide staff demographic questions with regard to the salary share loss phenomenon. One policy expert commented (E6), “...while the visual (graph/chart of salary object trend) first represents as counter intuitive, it is wondered how factors such as teacher turnover and related demographics came into play. Did we lose a large number of higher paid teachers?”

A second policy expert followed that line of thought with, (E1) “while salary lost share in the first-half, it still commanded nearly 46% of all new dollars coming into the

156

system; the release of PSERS (Pension) funding freed up 100’s of millions of dollars, and

while salary, in terms of real dollars, grew due to raises being given, he believed it also

grew with new hires (meaning added personnel). In the second-half of the study, salary

commanded 27% of the new dollars which represents a very significant trend change from

46% in the first-half. This dramatic shift pre-dated the 2008 recession, and was basically

attributed to “kicking-the-can-down-road” pension policy starting to catch up. On top of

that, health care costs were compounding as well. There is little doubt significant forces

were at play to create such a significant trend change.

Discussions also brought forward lead and lag issues regarding changes over time.

While interview data indicated surprise over the loss of salary share, the $2 billion, 46% of

new funds growth in the first-half of the study was also found to be a surprise (E3 and E4).

This time period corresponds to the Ridge Administration, which generally was not viewed as public education friendly as the Rendell Administration which followed in the second-half. In essence, the data show the Ridge administration presided over salary growth in the first-half which exceeded the Rendell Administration (second-half) by

nearly one-half billion dollars. Several of the policy experts discussed this finding as even more counter-intuitive than salary losing share. One policy expert (E1) put it this way as he attributed this first half anomaly to the PSERS rate reduction: “there was just so much freed up money with no rules, it flowed to salaries, to charters, to professional services and debt. Of these, only the charter cost was mandated by state policy, the rest was at board discretion.” While the Ridge administration presided over the introduction of charter schools, it also presided over the employer contribution rate (ECR) reduction era

of PSERS. Further, the results of this investigation clearly support direct linkage to the

157

Ridge Administration for significant salary growth in the first-half of the study. One

implication of this occurrence is that it was an unintended consequence of so much freed

up funding. Another implication is that the ‘lead’ for actual dollar salary growth in the

first-half of the study would ‘lag’ to contribute to added costs for health care and pension

in the second-half of the study. Meaning costs were front-loaded and provided

compounding effect in later years.

Finally, two of the experts (E2 and E6) discussed the fact (mathematically) that

with (what they defined) as an inordinate amount of increased spending growth in other

areas of the budget, salaries’ share of spending would have been driven down. This fact is

more important than perhaps would be assumed upon first hearing. Mathematically, any

object spending share could be driven down by simply spending less in that category, or

by spending significantly more in others.

One way to spend less is to cut expenditures. During this era, significant salary

cuts did not generally occur in districts across the state. A second way to drive down

spending share is to spend increasing shares of any new money coming into the system in

dimensions other than salary, especially if total expenditures overall remain strong. This second manner is what the evidence indicates, and in general it seems that even though salary grew in real dollar terms, it simply did not keep up with a flood of new spending overall. This spending shift was comprised of both new funds coming in to the system as well as the shift of funds within the system from pension.

During the study timeframe “staff layoffs” statewide were not a common occurrence; this significant trend change led to his (E2) opinion that there was a great propensity to hire additional staff in the first-half with freed up dollars, while by the

158

second-half, sustainability issues caught up to districts with limited revenue resources. By the second-half, new expenditure growth was being mandated by the state to via increased significant pension payments, while charter school payments were now entrenched and growing rapidly. Meanwhile as will be discussed in later sections, health care costs and debt expense are contributing factors to significant cost pressures as well.

Hiring activity (and staff demographics) would not be hidden from state policy

makers as the data would be available from required district reporting. However, this type

of analysis is not widely circulated or utilized, let alone reconciled to expenditure growth

patterns. The relevance of the significant salary share loss, inclusive of the first-half and

second-half variance is that it serves as a clear indicator of major change in expenditure

composition. It is part of a decade-long trend of money flows to spending dimensions

which are hard to alter once commitments are made.

This major change was occurring over the entire decade, yet experts, who worked in this environment, were surprised at many of these findings. This continued finding of

surprise at the results implies that reported and available statewide data may not be well

analyzed or utilized as effectively as it could be.

100’s Summary and Linkages

As discussed earlier, interview observations offered insight on the sheer weight of

additional spending increases overall which served to drive down salary as a percent share. Related to the 100’s directly, other observations inquired about teacher

demographics themselves with possible linkage to retirements and turnover to any extent above normal. It was noted throughout several interviews that this era contained an

inordinate amount of growth of categorical funds which were targeted to specific

159

programs. While such programs would contribute to total spending, it is not common that salary (i.e. long-term commitment) or salary payments are targeted in such funds. In fact, in many such grants and categorical programs salary payments are actually forbidden to be included in the fund use. Further, programs targeting teacher training, technology utilization and curricular integration generally would drive money to other object codes.

Contracting for professional services (300 Object), including the dramatic special education cost growth observed in this study, also served to drive share away from in- house salaries. Policy driven mandates in charter tuition services (500 Object) contributed to total expenditure growth in areas outside of salary. Out flow of dollars which were previously spent on pension cost were also now partially channeled toward debt (800 and

900 Object). As a result, this cost would serve to lock in spending for many years (i.e. long-term debt and general obligation bonds).

In a mathematical observation, one of the policy experts linked the large loss of salary share (as the largest expense), combined with the early years of pension benefit dollar reductions, directly to the loss of instructional expenditure share as revealed in the function analysis. Spending on salaries with proportional linkage to benefits is by design heavily weighted to professional staff in a district. Professional staff by design is heavily weighted to instructional costs. As money flowed to areas outside of direct classroom teaching as well as the actual dollar benefit expense reduction from the pension rate reductions, instructional spending would fall accordingly. This phenomenon may have had far reaching impact as it would have altered instructional expense calculations used in various state funding formulas and altered per student expense evaluations by the

Pennsylvania Department of Education.

160

When viewed in context of the entire study, the evidence that the salary

expenditures experienced significant reduction as a share of spending clearly serves as a key indicator of change. The fact that such a key indicator does NOT appear to be factored into systemic statewide analysis for policy makers is considered by the author a key finding. At the very least, an 8.3% decade-long share change should be understood in educational fiscal policy discussions. Additionally, salary’s share of dollar growth

between the first-half and second-half of the study was altered remarkably even as share

loss continued. Salary is the largest expenditure component in terms of actual dollars and share of spending. In as much, its share of spending is mathematically the hardest to impact, yet the data show its share change was the largest by a margin. For policy makers to able see this trend over time would cause at least a few questions to be asked. It also

begs the question as to what the next five years added to this study would look like.

In summary, it is clear that in combination, major events and polices were in play, and that they impacted the salary object dimension significantly. In the next section, discussion related to the 200’s object benefits dimension will delve deeper to understand related dimension impact.

161

200 Object Benefits

Figure 5.2 – 200 Object: Percent Share of Total Expenditures

Within the 200 object series, pension expenditures represent a significant portion of this object since the Employer Contribution Rate (ECR) as a percent of payroll is recorded here. The results of this investigation show that state policy on pension funding was a key factor for the entire decade of expenditure shifts and trend changes. The 200’s benefit major object dimension revealed a significant loss of expenditure share in the first- half followed by an equal offsetting gain in share in the second-half. Both trends align to state mandated / dictated employer pension rate trends.

Moreover, this study demonstrates the importance of trending analysis over time as the 200’s share of spending at the beginning and end of the study are virtually identical.

162

Restricting one’s perspective from a point A to a point B over time may miss crucial indications of change within the time frame.

In Figure 5.2, billions of dollars are represented as the trend line (percent share of

total spending) reveals directional change. One of the more obvious findings in the study

was direct linkage of pension ECR manipulation to the benefit share trends. State efforts at

manipulation of the ECR were consistent throughout the decade. The suppression of the

ECR was combined with increasing the benefits in the same era. In several events /

actions, the annual ECRs were either lowered directly or artificially held below what

should have been the rate. It was noted time and again by the expert participants as

evidence for both the loss and gain observed in share. Specifically, meaning that benefit

share went down as they lowered the rates, and it climbed back up again as they increased

the rate. The evidence supports that linkage; however, there is much more to it than that.

Public policy of pension rate reduction (ECR) of nearly 9 full percentage points (i.e. as a

% of total local education agency payroll) in less than six years acted as a catalyst to

impact district spending patterns. This rate reduction resulted in “freeing” up of $100’s of millions annually. It was unrestricted cost shifting for both districts and the state.

Revenues supporting those expenditures had been built into district and state revenue base over the prior decade to specifically fund pension obligations. At both district and state levels, tax revenues levied for one specific purpose were released to be utilized elsewhere.

For districts, the vacuum caused by lowering pension rates was predominantly filled with long-term fixed costs for additional staffing, growing and added health care cost, new state charter mandates, and district and state debt obligations. In the second-half

163

of the study the pension rate needed to be increased, but even then rates implemented were

artificially suppressed from what the actuarial rate calculations dictated. This suppression was employed by both administrations.

The 200’s Object code contains not just the gross pension expense for districts,

but also includes health care costs. Reflective of national trends, health care costs for

districts grew over the timeframe of the study yet “total benefit costs” overall fell

dramatically in the first-half of the study. This (E1) was noted in one interview as

providing a masking effect for district budgeting. Generally, the PSERS ECR reduction

provided a greater dollar amount of reduction than what health care costs grew. Therefore,

budgeting for health care growth was paid for with ‘dollars left over’ to go elsewhere in

this exchange.

In 1997 the pension rate was 10.6% of payroll, it fell to less than 1% of payroll in

the midpoint of the study, and in 2008 it was 5.6%. At 5% of payroll lower ECR, the

percent share of 200’s expenditures is nearly identical in 2008 as it was in 1997 (See

Figure 5.2). Salaries certainly increased over time so a lower rate on a larger base would

serve to grow the expenditure, but it was also impacted by continued and compounded

health care cost growth.

This masking effect of health care cost served to artificially remove pressure from

school budgets. Cost growth was actually being ‘netted’ while adding new staff,

negotiating collective bargaining agreements, or borrowing money for new bonded debt.

The private sector spent this decade working to reduce health care cost by implementing

higher co-pays and other cost shifting mechanisms, HMO plan design, and overall benefit

plan reductions (Reeher, 2003) and (Gabel, Claxton, Holve, Pickreign, Whitmore, Dhont,

164

& Roland, 2003). For the most part, school districts did not follow this pattern, and three

of the participants (E2, E1 & E6) noted (in their opinion) that most districts expanded

health care benefits during this era. Even if districts did not add benefit coverage to their

plan designs, they did not reduce coverage either. In the first-half, even with health care cost growth, district total benefit budget was shrinking due to the scope of pension rate reductions, the data show it was a different story in the second-half.

Overall, the result was that health care cost growth was not a major pressure point

in district budgeting during much of this study time period. In as much, this would by

design have had an impact on collective bargaining and resulting collective bargaining

agreements. Whether this was a positive or negative impact would be arguable depending

on the audience. Counter-factual perspectives aside, if the ECR had not been suppressed

(several times during the decade) and were raised to double-digit levels, it is highly likely

that these trends would have been altered.

One result of hundreds of millions in outflow of pension dollars was that it

masked increasing health care costs, another was that it also masked other benefit costs in

the 200 object dimension as well (e.g. workers compensation, unemployment, social

security, other coverage expense for dental, vision, life insurance etc.). The pension cost

reductions mitigated cost growth in these other areas, offsetting their impact within district

budgets. ECR reductions as percent of district payroll were significantly large enough to

mask even large percent increases on these “lesser” shares within benefits.

(E1) One participant stated it just made it easier to spend. Reallocating these funds

in combination with new funds coming in to the system altered spending patterns in

significant ways. By extension the 200’s would have been impacted by new hires (added

165

health care coverage, added social security costs, added pension liability) and related decision criteria for strategy in collective bargaining.

Other (200 Object) Summary and Linkages

Generally described as cost shifting for health care coverage, over this decade private sector companies implemented higher employee premium share, higher co-pays, higher deductibles, and plan design strategy targeted lowering costs (Gabel et. al, 2003; and Hurley, Felland, Gerland, & Pickreign, 2006). To a degree, this served to move the two sectors in opposite directions creating disconnect or variance from taxpayers footing the bill. As noted by one participant (E2) school district plans became the “plan” of choice within district communities over the decade. Meaning, district employees became the preferred “spousal or family” coverage vehicle for an employee’s family. This adverse selection against the public sector serves to provide fiscal relief to the private sector employers as district employees enrolled working spouses and /or other dependents, shifting those costs from private sector coverage to the public.

In the second-half of the study, districts now faced benefit cost increases at gross amounts all heading in the same upward direction. The netting effect was no longer in play. In the second-half, there was recognition of growing health care cost (often double- digit annual percent increases). A significant attempt in 2006 was made by the Rendell

Administration to implement a “state-wide” school district health care mandate under the assumption it would reduce costs. The program failed to take any meaningful shape, but it was clear towards the end of this study that health care cost growth was no longer

“masked” from view.

166

Finally, upon observation of the findings, one policy expert (E1), whose expertise is in school finance, stated; “there is a direct correlation to the PSERS reduction in rate

and the lower / lost share in instructional expenditures.” While this study did not focus on

correlations, the observation is linked to major reductions in spending for pension cost as

well as the lost share in salary. Both occurrences would by design represent a significant portion of expenditures in the instructional function area. So as funds were reallocated out of the 200 object dimension, to the degree funds were allocated out of “instructional” function account code classifications, these accounts would lose expenditure share.

Further, this would have impacted statewide Actual Instruction Expense (AIE) calculations and any state funding formula utilizing AIE or instructional expenditure

linkages.

Conversely, this same observation should hold true in an ERC increasing rate

environment. In the future, pension costs are projected to rise as a percent of payroll from

the mid 4% range at post study to a projected 30% or more of payroll by 2016. As this

happens, “instructional spending” as a share of the total may increase even as district

spending cuts and reductions occur to help pay the pension bill. Instructional spending

(due to concentrating dollars in instructional accounts) may indeed grow, albeit not for the

reasons that many would want, nor will it be a given to improve instruction.

The following quotes from the interviews are instructive as they offered insights on the

200 object data findings:

 “This is mind blowing...it is not intuitive at all...we think in amounts of Basic

Education Subsidy increase or specific Categorical program funding...these

amounts are mind blowing in scale. E7

167

 “Object level 200’s and the new dollar share are just staggering over the

second-half.” E2

 “Benefit and salary share change in the second-half of the study are

remarkable.” E4

 .... this is just not what you would think knowing the governors and their public

education leanings.” E6

500 Object Other Purchased Services

Figure 5.3 ‐ 500 Object: Percent Share of Total Expenditures

500's Other Purch. Services 12.00% 11.0% 11.00% 10.1% 10.5% 10.7% 10.00% 9.5% 9.2% 9.7% 9.00% 8.2% 8.1% 8.7% 8.00%

7.5% 7.00%

6.00%

Share change = +2.0 1st half....+1.5% 2nd half Overall change = +3.5%

Act 22 which passed June 12, 1997 provided for major charter school legislation and made

Pennsylvania the 27th state to provide for the establishment of charter schools. The cost (or

168

tuition) of charter school district expense is based on a resident district tuition formula

yielding an amount to be paid to the charter school by the local district in which the

student resides. While other costs are included in the 500 object series, all participating

experts linked the steady decade long linear increase in the 500’s to charter school tuition

costs. At a 3.5% increase of total expenditure share, the 500 object series was the third

highest overall growth behind salaries and benefits (See figure 5.3). In the first-half of the

study, share change was 2.0% and slowed in the second-half to 1.4%. This slowing was

viewed as mathematical based on the explosion of benefit cost in the second-half (driving

down share growth), and is not necessarily attributed to “slowing charter costs”.

Total spending in the 500’s was $2.5 billion in 2007-08 of which $1.37 billion was

for tuition payments, which included tuition for charters, CTC’s, certain IU programs, and

other LEA’s (PSBA 2010). In 2003-04 charter tuition was $295 million. Charter tuition in

2007-08 was $628 million or 45.6% of total tuition paid. For comparison, the next largest tuition payment was to Career and Technology Centers (Vo-Techs) at about $326 million, or 25% of total tuition paid. Tuition payments in the 500’s (other than charter) such as

those to private schools, detention centers, non-public entities and other accounted for

about 30% of tuition paid, and cost increases in those areas certainly contributed to growth

trend. The four year increase in charter tuition from 2003-04 to 2007-08 was $332.5

million or 112.6%. Interviewed participants associated the strength and direction of the

500 object series cost with charter policy, and cost data support that. As noted earlier, in

addition to tuition, significant transportation costs are also included in the 500’s.

During the study time period total, transportation costs (2700 function) grew $504.4

million or 74.6%. The 500 object code contains large segments of transportation spending,

169

so some of transportation’s function increase would impact the 500 object series.

However, the 500 object dimension grew $1.55 billion or 160.7% versus transportation’s 74.6%. Something else was obviously impacting 500 object code

expenditures. Additionally, chapter 4 analyses of the 2700 sub-function (Transportation)

data findings reveal a small 0.23% increase in total expenditure share change over the

entire study. As a share of total expenditures, total (2700’s) transportation cost (which

would include 500 level expenditures) changed very little, which again supports charter

schools as a main driver of 500’s growth. The $627 million in 2007-08 charter expenditure

was basically near zero in fiscal year 2000. Cost growth was significant and impacted

districts disparately within this object code as a mandated district expense.

500 Summary and Linkages

One participant commented that the advocates of charters and cyber are in authority now; so much of this cost impact is intentional. Further, he noted that the cost is just so expensive with the burden on the public dollar, and it is worse as the state mandate requires local school boards (with local funding) to pay the lion’s share of the cost. The

Commonwealth’s charter school policy creates an adversarial environment pitting many

players against one another, including school districts, school personnel, parents and students. This adversarial environment is generally labeled competition. With annual costs approaching one billion dollars in 2008, state policy mandates the cost to be borne by the local district, out of local monies and resources regardless of ability to pay.

During the period of study the state reimbursed school districts approximately

25% of their charter costs. Subsequent to this study in 2011-12, the state withdrew its

portion and districts paid their full tuition at 100% of the cost.

170

While not the purpose of this study, several comments during the charter discussion were made regarding student outcomes and what was deemed as a lack of charter accountability, especially given the amount of money involved. According to one participant, (E7) charter policy is not worth the investment when looking at the student outcomes over time. It was argued that by far districts, CTC’s, and IU’s have greater accountability than charters do.

Slower growth of 500’s share of expenditures in the second-half of the study may indicate the slowing of charter school growth after a decade of start-ups. It is just as likely that the share has grown so rapidly over the first-half, that it is now mathematically harder to ‘change share’. It will be interesting for further studies to examine if the trend slows or declines over the next five years as charters “mature” and market saturation slows charter growth. Meanwhile, the 500’s trend line remains on a steady linear growth trajectory with no current indication of change to that pattern.

Although the state average trend was consistent, one policy expert pointed out that the direct local impact (of charter cost) is all over the board with very different results. According to one participant (E1), in the first-half of the study many wealthy districts had low or little charter student activity. Meanwhile, many less wealthy district were impacted much more dramatically. In his opinion, by the second-half of the study many wealthy districts were being “targeted” by cyber charters due the higher amount of money their tuition calculation derived. State policy for charter tuition provides no distinction between a brick and mortar school and that of a cyber-charter. Under state policy there is no connection between the cost of charter school operation and the tuition

171

payments received. For example, a cyber-charter may collect $12,000 for a student from the local district, but that same student may only cost cyber-charter $3,500.

While not necessarily apparent from data observation, policy expert discussion

(E1 and E6) also noted that cyber charters are not generally investing/returning dollars back to the local community. The money for cyber charter tuition is largely associated with out-flows. Most cyber charters are not spending their money on buildings, local services or salary within the community paying the bill. In general, cyber charters are paying others outside the community to do work and money flow is very different from

‘in’ local district expenditures.

Finally, within the 500’s trend is the observation that the other tuition payments (to other LEA’s, CTC’s, and perhaps IU’s) would have been artificially impacted by the 200’s pension reductions. IU’s and CTC’s would have experienced the same general cost relief from the dropping pension rates and it follows that their tuition costs would have been impacted in some manner. As “pass-through” agencies, IU’s and CTC’s generally get a significant percent of their funding from the local school districts. With no rules on the pension reduction flow of freed up monies; these entities also would have used the funds in many different ways. In essence the 200’s pension reductions most likely served to mask costs at the pass-through agency level as well. Since those agencies do not have the ability to tax, as pension rates need to grow, those additional increases must be borne by increased revenue from member districts or cuts in programs.

The results of this research support the claim that charter school tuition and policy served as a driving force for the 500’s strong trend line slope and direction for increased spending share. Transportation and other tuition payments within the 500 series

172

contributed to the decade-long growth trend; however, it is clear from the transportation

function perspective that total transportation as a share of total expenditures did not change and its impact on share change would have been minimal.

900 Object Other Uses

Figure 5.4 Object 900: Percent Share of Total Expenditures

To a large degree, the 900 object series mirrors the 5000 function series for recording debt and capital transfer expenditures. It can be argued that the 800 object series contributes within this category as certain debt expenditures are also recorded there (see

Chapter 4; 800 object series results). However, trended share change in the 800 series was minimal compared to the 900 series, yet expert interviews offered linkages to both series.

The 2.6% share growth of the 900’s represents nearly 12% of the “new spending” over the time frame or approximately $1.2 billion. The share trend line indicates a decade long pattern of overall growth; however, the year-by-year trend line indicates lead and lag

173

changes within that trend. Observations from the experts provide several emergent linkages to policy and events.

To provide some perspective, one expert (E6) promptly noted on observation of the 800’s and 900’s trend that this was an era of historically low (and falling) interest

rates. To him, these trends reflected cycles of refunding and funding needs within school

debt. With historic low borrowing (interest) rates, the 800 series minimal share increase

makes sense even with increased debt. Study results indicate this was a period of greater

debt borrowings (see chapter 4, 5000 function); however, the 800’s saw minimal impact

(which records the interest payment portion of allocated debt cost) due to refunding higher

cost debt legacy. While it is evident that additional debt was added, it was added during

historically low public sector borrowing interest rates for the period.

From 2004-05 to 2005-06, the trend lines reveals impact from the Act 1

“borrowing window” as the 900’s share grew a full 1% of total spending in one year. Act

1 of 2006 provided ‘exceptions’ for debt payments (exemption from some restrictive

aspects of the law on property tax rate increase caps) placed prior to the Act’s

implementation date. The trend line reveals the economic activity this policy generated

moving from 7.2% to 8.2% in one fiscal year.

During the early years of the study, the 900’s had a 6.1% share of total spending

and that rose to 7.5% five years later in the first-half of the study. Several of the experts

commented that this ‘shift’ was not necessarily on their radar, but clearly money was

moving to this object dimension. As pension payment rates were reduced, over the course

of five years, monies were shifted to debt and capital funding. However, with the advent

of Act 1 policy and the borrowing window, a full percentage point was added in one fiscal

174

year. Such borrowings are generally for 15 to 20 years, so monies flowing to this purpose

are long-term and do not unwind or allow for easy resource re-allocation.

Debt obligation made by districts also obligated state funds as well to the extent they entitled district a revenue stream under state policy with PDE’s PLANCON process.

This process enabled districts to receive partial reimbursement on all debt payments for certain prescribed construction activity. While not all debt obligation incurred by districts would obligate state revenues, the majority of major school construction does utilize the

PLANCON process.

None of this discussion is to imply that pension flow to debt, and the Act 1 borrowing ‘incentive’ was bad in and of itself. With regard to borrowing, two of the participants (E6, E1) noted that over this time period many districts took advantage of 40 year historic low interest rates to fund capital project / infrastructure needs. From that perspective, one may wonder if there was not an opportunity for state policy to leverage that environment with improved policy and direction. Given the state-wide ‘jointure era’ of the late 1950’s and early 1960’s, many school buildings were ageing and in need of repairs and upgrades. In many cases, emergent technology needs in the late 1990’s and

2000’s (specifically instructional technology) were included in district construction programs. Many districts used borrowed funds (Debt Obligations) for IT infrastructure and equipment as well as energy saving investments to upgrade heating, cooling, electrical and lighting systems. Further, many district projects added new school safety features and addressed compliance with three decades of new state and local building codes.

The participant discussions on debt (spending share) growth ranged from not being intuitive to it all makes complete sense given the environment of the decade. The analysis

175

now shifts to investigate whether the changes in district spending patterns varied based on

the two major district characteristics of size and wealth. As before, within the district characteristics analyses the object focus as a means of understanding the results, generated significant discussion and insight among the experts.

District Characteristics

Size of District

The first characteristic analysis is for district size, measured by average daily

attendance (ADM). Three sub-groups of 50 districts each were established as the smallest,

middle, and largest 50 districts in the state. Table 5.1 lists the share changes for each

object category by sub-group over the study time frame. Generally, patterns and trends for

the three sub-groups followed patterns which were exhibited by the average district.

However, what emerged from the study were in certain spending dimensions, size of

district revealed significantly different impact in share change during the study

Table 5.1 ADM Characteristics 50 smallest, 50 Middle, & 50 largest

Change in % Share from 1997‐98 to 100's 200's 300's 400's 500's 600's 700's 800's 900's 2007‐08

Statewide all Districts ‐8.3% ‐0.2% 1.2% ‐0.2% 3.5% 0.4% 0.1% 0.8% 2.6% Smallest 50 ADM Districts ‐10.0% ‐0.1% 0.9% ‐0.4% 5.2% 0.4% 0.0% ‐0.5% 4.6% Middle ADM Districts ‐6.5% 0.6% 0.7% ‐0.2% 1.9% 0.5% 0.2% 0.1% 2.7% Largest ADM Districts ‐9.6% ‐0.2% 1.5% ‐0.2% 3.7% 0.5% 0.2% 1.0% 3.2%

Major statewide policy changes / shifts may have a greater impact on smaller

schools as they do not have economies of scale (of students, of financial resources, of

176

staff) to absorb shocks and changes. Comparison of the share change results in the small

district found that share changes for the 100’s, 500’s, and 900’s were much greater than

the average district. Further, small district share change differentials were even greater

when compared to larger or middle size districts. Clearly, in some spending dimensions,

impact over time is differentiated, and the participants expressed many opinions from their observations as to why.

Small districts lost a greater share of 100’s than all district sub-groups. Economy of scale would certainly play a role, and so would spending more in the 500’s and 900’s which is supported in the data. It would appear to be a combination of both. Small district

100’s loss of share at 10.0% was nearly completely offset with share flow to the 500’s at

5.2% and 900’s at 4.6%. For all three of these object codes, small ADM districts share change reflected more share change than any of the other sub-groups. As shown in Figure

5.5, the 200’s for small districts hardly changed from beginning to end. However, the study’s 200’s analyses revealed small district first-half share change at -2.6% which was actually less than other sub-groups, as was the second-half share gain at +2.5%. Large and middle ADM district 200’s share change for the first-half and second-half followed very

close to average district results of -3.4% and-+3.2% (See Figure 4.8).

A hot button for most of the expert panel was the variation at the 500’s which was

attributed to two major policy concepts. First, charter cost impact on small ADM districts was felt to have more impact on their budget in that the tuition cost of 15 to 20 students in charter schools (approximately $150,000 to $200,000) would be much more significant.

The financial burden of this mandated expense cannot be absorbed as readily as larger districts with greater economy of scale. If a small district happens to be located within

177

easy reach of several local charter schools, the cost impact could be substantial. State

charter policy sets up an environment of disparate unintended consequences, and the data indicate evidence of this impact. Another participant noted the concept of charter

enrollment ‘churning’ (students enrolling in and out) which would partially explain the erratic nature of trend lines revealed. (E7)

The second concept exerting pressure on the 500’s was that, in general, it was noted smaller districts contract more services than the middle size or larger districts. It was

participants’ experience that ‘smalls’ have little choice (to contract) as they cannot afford to hire the expertise on staff to deliver the mandated services or educational programs to students within their community. The point was that smalls are often forced to contract for many services. These services could include: special education programs and services, contracting and paying tuition for student instructional needs, staff training, constantly changing state mandates in curriculum. This makes the smaller districts more vulnerable state policy directives which may cause less predictable variations in this budget object for

them.

Emerging from participant interviews were several observations on the 900 series

for small districts. Small districts would have had the opportunity to use freed up pension

funds to take on debt obligations as other districts. With budget capacity from the freed up

funds, taking on debt would have been more affordable. The Act 1 borrowing window

incented those who were contemplating borrowing in the near future to make a decision,

and the data suggest many smalls decided to borrow. To a large degree this is an obvious

choice as small district ability to raise taxes were going to be capped under Act 1, and no one knew what that impact would be.

178

Wealth of District

Similar to ADM review, Chapter 4 presents a series of year-over-year trended share changes by district wealth (as measured by MVPI), and Table 5.2 lists the share changes for the 50 lowest wealth, 50 middle wealth, and the 50 highest wealth districts across the state over the study time frame. Similar to the ADM analyses, patterns and trends for the splits by district wealth followed that which was exhibited by the average district. However, again what emerged from the study was that the wealth of the district revealed different impact in share change during the study. In particular, the 100’s, 500’s and 900’s object dimensions.

Table 5.2 MVPI Characteristics: 50 Highest Wealth, 50 Middle Wealth, & 50 Lowest Wealth

Change in Share from 1997‐98 to 100 200 300 400 500 600 700 800 900 2007‐08

Change in Share ‐8.3% ‐0.2% 1.2% ‐0.2% 3.5% 0.4% 0.1% 0.8% 2.6% All Districts

Lowest Wealth ‐6.9% 0.5% 0.9% 0.1% 3.7% 0.6% 0.1% 0.1% 0.9% Districts

Middle Wealth ‐6.7% 0.5% 0.5% 0.0% 2.9% 0.7% 0.0% ‐0.3% 2.4% Districts

Highest Wealth ‐8.1% ‐0.1% 1.4% ‐0.3% 2.4% 0.0% 0.3% 1.4% 3.0% Districts

The largest share change differentials for wealth were in the 900’s. For the least wealthy, this was attributed to not being able to afford to put debt in place. Since the 900’s also account for fund transfers and debt, low wealth districts generally do not have funds

179

to transfer or the capacity to borrow. Other interview observations noted that the poorer

districts were now paying more (as a share of spending) for supplies (600’s), benefits

(200’s), and the other purchased services (500’s). For the 500’s, again the attribution is predominantly tuition cost for charter policy. One participant (E7) interjected that those who least can afford it are targeted by the charter mandate, and this is to where a lot of their money is flowing. It is worth noting that what he meant was intentional targeting by policy makers, not unintentional or unintended consequences. Nevertheless, not having the ability to borrow (raise large blocks of capital) also placed the least wealthy at a disadvantage to be able to raise capital for instructional technology as well as repairing infrastructure.

Characteristic Summary

Results of this research show spending pattern and share of spending changes which indicate events and policy are having disparate impact on districts based on the two major characteristics of wealth and size. Participant interviews on this topic revealed a general frustration with state policy implementations which in their opinion placed small and lower wealth districts and their students at severe disadvantage.

Comments generated from the interviews regarding characteristic analyses

numbered some of the most frequent along with object level itself. If nothing else the data

validated beliefs that there were differential impacts happening to schools based on certain

characteristics. One policy expert (E6) noted that low wealth districts are restricted what

they can pay staff (i.e. they cannot match salary competition) so they have to contract and

pay others for much of what they do. Commenting on the 500’s observation, one

participant (E5) simply noted that the poor pay more with tuition policy in play.

180

Another participant (E7) commented that the smalls and low wealth districts emptied their reserves (fund balance policy) in the early 2001 to 2003 era, and then borrowed money with the Act 1 incentive. (E1) Another noted that the low wealth pay a higher percent of their budget for supplies, because they cannot afford to spend money on other areas and they struggle with a lack of economies of scale. Further, he was suspicious that the wealthier districts had greater access to capital and borrowed more and bought large amounts of technology (in bond funds) outside of the supply object. This would have driven down their 600’s and 700’s spending, but it would have front-loaded large investments in instructional technology. Simply put, money does matter.

Key Events and Policies Impacting Trends

Results of this study found that several key events and policy were identified through observations of expenditure/revenues trends and patterns. Many of the participants focused specifically on policy or events linked to: Pension (benefits) and funding, Charter schools, Debt, Tax reform efforts with Act 1 of 2006, Fund Balance, and

State Funding. Additionally, participant observations discussed issues surrounding the two main Governor Administrations with lead and lag time around the mid-term splits.

Interestingly, participants discussed the mid-term study point almost exclusively in terms of these different administrations. In some manner all the participants commented on observations in the first-half and second-half percent trend changes data which were counter-intuitive based on their familiarity of those administrations.

181

Pension Policy

Pension policy over the study’s time frame places itself front and center as a key

event with many spin-offs. In the first-half of the study a flood of money was released by the state’s action lowering the employer contribution rate to virtually zero. By the end of

the fourth year in the study it was estimated that districts reallocated over ½ billion

dollars in pension spending on an annual basis. This would not include pension dollars

freed up to IU’s, CTC’s or other LEA’s. It also would not include freed up pension funds

which the state would have realized in its own budget, both in PSERS and SERS. The

evidence of this study indicates that these funds (accounted for in the 200’s) flowed to

other objects across the expenditure spectrum, but mainly to salaries, tuition, and debt.

Debates, discussions, or even arguments lamenting the State mandated “pension

payment holiday” taken during this era rarely mention that fact that these funds were

indeed spent in and for education. Stake holders in the Commonwealth received the

benefit of diverted state SERS and PSERS funds to other programs. Stake holders in

education received the direct benefit of those freed up funds to salaries, health care and

improved infrastructure. Education salaries grew significantly even while losing share,

district infrastructure was improved and repaired, and state mandated tuition to charter

schools clearly claimed a significant share. For lack of a better term, the ‘pension

dividend’ funded growth for both mandate driven as well as individual district choice

driven expenditure trends. This would by necessity include salary and expanded health

care coverage in the collective bargaining arena.

Pension policy included:

182

 Significant reduction of employer rate payments to levels well below normal

cost

 Investment losses in 2001, 2002 and 2008 (and again in 2009, which was

outside the scope of the study)

 Legislated increases in benefits in 2001 (multiplier changes) and again in

2002 (Act 38)

 Act 40 in 2003 pushed pension obligations to out years (i.e. moving current

liability out to future years), subsequently, a similar move to suppress current

liability and move it out to future years was done again with Act 120 in 2010.

Reallocated funds were supported by various tax and revenue streams which had been attained or levied over the prior decade in support of pension obligations; funds were now spent on salaries, new hires, benefits, charter mandates, technology and debt.

In the second-half of the study districts had to rebuild district revenue tax base needed to pay for increase employer cost rates.

In the second-half of the study, benefit expenditure growth overwhelmed all other object dimensions expanding $1.3 billion more than it did in the first-half. It exceeded salary growth in terms of both dollars and percent share growth as it captured 27% of all expenditure growth in the second-half. Comparatively, benefits only captured 4% of expenditure growth in the first-half. At the end of this study in 2008, trend lines indicated continued benefit share rate growth, with significant known pension liability yet to be dealt with.

In 1997 the pension ECR rate was 10.6%, it fell to 1.09% in 2002, and in 2008 it is back up to 7.13% of payroll. However, due to a decade of deferral, the rate is projected

183

to go to 31% or more of payroll by 2025. Failing to meet the pension issues head-on

during the study timeframe set the stage for all that may happen post-recession.

Ironically, the legislation of 2003 and 2010 were designed to “buy time,” for market

recovery, however, the result has been that these actions have virtually ensured that

pension policy will remain a key issue for the next two decades.

Fund Balance Policy

There were no rules for districts with regard to an amount of fund balance at the

beginning of the study. As noted in chapter 2 the state legislated policy placing caps on

district reserves right around the mid-point of the study. Several experts commented on

the trend line data which revealed “spike” indications of district transfers and spending in

years leading up to the new rules. In several interviews, it was noted that the data show a distinctive spike (see the 900’s in chapter 4) indicating districts were ‘emptying out’ their reserves. (E5) One of the participants indicated that the fact that many districts had large fund balances created politically difficult issues with General Assembly leadership, particularly for discussion on annual State-wide education funding decisions. Whatever the politics of fund balance, data indicated districts spent and transferred funds in advance of this implementation deadline.

Further, data show that this appears to have impacted smaller districts and less wealthy to a larger degree. For districts which could afford to leverage a fund balance, the new rules created a “spiked’ spending surge, and in subsequent years it served to lower the amount of money they had to invest for interest earnings, which serves to off- set local revenue needs. (E2) Another participant commented that the requirement for districts to meet new capped fund balance limits varied in district approach. Some

184

districts outright spent their way to compliance, while some transferred excess money physically to other district funds, and some set up ‘reserve’ balances within their general fund. Spending a fund balance can only be done once, and once it is used it is gone.

Further, district goals were compliance with a new policy mandate, not necessarily to spend the money the well. To a large degree, this policy was viewed as punishing frugality, or at the very least, there was little reward for a district which

exhibited frugal spending traits. (E2) Moreover one participant stated that districts which

had large and significant fund balances were often taken to task by local newspaper editors or even members of the general assembly. Whatever the reason for the change in fund balance policy, it was followed by continued efforts at tax reform policy (ACT 1 of

2006) which restricted district tax authority. In summary, the overall impact was to reduce or eliminate district reserves, and then move to restrict district ability to raise funds.

Debt and Borrowing

(E6) One of the participants astutely noted that debt trends generally will follow certain cycles influenced by funding itself or re-funding opportunities. State policy toward debt and borrowing, whether intentional and not, provided the opportunity and incentive to borrow. Further, this opportunity and incentive occurred in an era when buildings were aging, technology needs were emerging, and the economy produced historic lows in borrowing rates. The release of pension funds certainly allowed money to flow to debt, as there were no rules on how the money was to be spent. Further, the low rate environment coupled with released funds allowed for larger dollar borrowings.

185

The impact on state funds as districts borrowed more is impossible to observe from this study’s approach. However, state PlanCon policy remained largely unchanged during the study. Which means the more districts borrowed, the more they committed spending by the state. The PlanCon process for how districts borrow to renovate or build new schools provide for partial state reimbursement of those costs at a formula driven level. What is observed is that data show districts took on much more debt, which by extension means the state had its own funds committed to these projects via predetermined formula. While pension policy clearly provided capacity to borrow, trended observations strongly indicate that Act 1 implementation itself altered capital spending share by one full percentage point of total spending in one fiscal year.

New and additional debt for construction programs is generally ‘locked in’ for the next 15 to 20 years based on typical general obligation borrowing periods. Any state funding tied to these programs would also be locked in for the same time period. Revenue streams committed to support these expenditures are not available for reallocation moving forward for quite some time.

Charter Policy

Act 22 which passed June 12, 1997 provided major charter school legislation and as noted in chapter 2, the primary cost burden for charter school operations was mandated to be funded by the local district in which the student resided. The Act was passed after three years of debate with various pro and con advocacies. It was evident from the participant conversations that this same controversial funding policy also set the stage to create, grow, and sustain adversarial cultures within K-12 education sectors. The results of this investigation show that charter funding was a major contributor to the 500’s share

186

of spending growth. Charter funding contributed to a sustained decade-long outflow of

funds from local school districts which funded the lion’s share of charter cost. One of the

participants (E6) noted that the adversarial component was not just between “Charter and

Public School Districts,” but it clearly (in his experience) had a very negative impact on traditional non-public schools. Charter policy served to remove public school district resources and focus them on a select few charters. This was true for advances in on-line learning technology, which even though funded by local tax payers, by charter policy such advances became proprietary instead of being shared and leveraged for the common good of all students. Meanwhile, he believed that parochial schools were losing enrollment and / or closing in certain regions as they too had to compete against this new

“free” charter education opportunity.

Charters are “free” (no tuition) to parents of sending students and many charters advertised free internet and computer equipment to the students in addition to having no tuition cost. Traditional non-public schools must charge a tuition rate, and parents struggling financially could now leave a public school district or the non-public school and avoid tuition costs. This particular concept may be much more identifiable for future studies in the post-recession years. Charter policy also mandated transportation to brick and mortar charters within ten miles of a sending district’s boundaries. Interestingly, the majority of any charter transportation costs would also have been recorded in the 500 object code.

187

Tax reform

During the study time frame there were three legislative attempts to provide some measure of tax reform. Tax reform attempts can generally be defined as progressive attempts to reduce property tax growth by restricting school board authority. As discussed in chapter 2 the first two of these attempts presented options to the board or community with regard to implementation. Act 50 required the board to opt in, and Act

72 required a vote of the community. Neither generated any significant district implementation and participation. By the time Act 1 of 2006 was introduced, the general assembly made it mandatory. For the first time in Pennsylvania, Act 1 contained very real and significant tax authority restrictions on school boards. Those restrictions during the study however, appear to have had little impact on the last two years of the study. The indices utilized to cap school board authority provided a sufficient range up to the cap and most districts could work within the range. Post 2008 recession and post-study, it is much more likely to observe Act 1 impact in subsequent research data. Meanwhile, it was the debt window aspect of Act 1 implementation which impacted the most.

Intentionally, one purpose of Act 1 tax reform policy was to limit board authority for raising property taxes. Ironically, one of the issues that emerge from these findings was the significant incentive to borrow and take on debt as the debt window provided time for districts to borrow or establish borrowing resolutions in advance of the Act’s effective date. Legislation which was intended to reduce the utilization of property tax authority created the single largest shift in expenditure share in the 900’s during the decade. One participant (E2) observed that borrowing (share trend line) had slowed and was falling right before Act 1 implementation. Debt and borrowing are not bad in and of

188

themselves. However, the data suggest that any impact Act 1 may have had on property

tax growth in the last two years of the study was more than offset with the full percentage

point growth in spending share change in debt. This debt had to be supported by local

revenues (primarily property taxes) for the full-term of any long-term bonds (generally

10, 15, or 20 years). Further, to the degree much of this debt qualified for the state’s

PlanCon process and partial state funding, it would have committed future state funding as well.

Administrations

One unanticipated finding can be best described as the counter-intuitive results of the data trends with the years generally associated with the two main governor administrations. In short, all of the participants agreed that between Governor Ridge and

Governor Rendell, that the later would by a margin be described as ‘more friendly’ to public education. Further, there were expectations that his education funding and policies would have presented favorably in the data results. While it is no secret that all administrations will attempt to implement their own programs and have them carry-over beyond their term, everyone was caught off guard by the inverse trend associations. Lead and lag were visible here and this rather contradictory result is heavily weighted due the sheer size and scope of the dollars involved.

Governor Ridge had a unified General Assembly and did much by way of legislation because he could. Rendell had a divided General Assembly, and did much by administrative fiat which caused divisiveness, because he had to (E4 & E3). For

Governor Rendell that led to state budgets being passed after the June 30 deadline (E2).

The late budgets led to uncertainty and in the early years Rendell transition; one

189

participant (E2) noted it led to districts “putting on mills” as they had no clue what the state was doing. Figure 5.8 shows local revenues climbing for the first three years of the second-half, and then the increase drop off dramatically. The drop-off appears to be tied to both some impact of Act 1 as well as final established impact of Rendell increases to state education funding. The erratic nature of state increases is attributed to the turmoil and late budget battles the Rendell administration had with a split general assembly.

Additionally, both the state and districts had to rebuild budget capacity as pension ECR rate was on the rise. For districts trying to plan budgets in the face of such unknowns, district ‘putting’ on mills appears to be logical unintended result of the state’s erratic budget process.

190

Figure 5.5 State and Local Revenue Percent Increase Trends

With regard to the 100’s object share changes, the different administration

leanings were not necessarily readily apparent in the data. While on one hand salaries lost share under both administrations, share loss actually increased under Rendell. Further, and perhaps more ironic, actual salary growth in the first-half under Ridge captured

45.8% of every new dollar versus 26.6% under Rendell (See Table 4.4 in chapter 4). This is credited to the freed up pension funds in the first-half, and the enormous growth in benefit costs during the second-half impacting the Rendell administration. The free flow of released funds inclusive of lower but continued state funding increases fed first-half spending (See figure 5.5).

191

What cannot be seen in these analyses is that state spending with the release of

SERS and PSERS state shares plus any growth would also be occurring. In any event, as health care and pension costs compounded over the second-half, those costs were too much of a ‘lag’ for the Rendell administration’s education funding priorities to overcome.

Other Events / Non-Events

A few other events and topics are worth mentioning not because of what participants observed, but rather what did not show up. Looking at this decade given the national and state policy and effort for No Child Left Behind, it was expected that certain trends would indicate visible linkage to this massive education undertaking. In reality, at least at this study level, it was not observed and was discussed very little from the data perspective. Counter-intuitively, the data indicated the opposite. State revenue share continued its 30 year decline in terms of funding share, federal revenue share in the second-half dropped dramatically (See Figure 4.36), and instructional spending share declined during the entire study (See Figure 4.25). While there are intertwined complex forces at work, observation of the data provide few indications NCLB was a factor.

PURTA (Tied to Electric Deregulation) changes became effective January 2000, and altered how utility taxes are paid in Pennsylvania. By 2002 electric generating facilities were paying nearly $100 million less in taxes to the state. Those taxes were passed on to municipalities, cities and school districts. At least at the level of the study’s data very little could be identified to link this major event. However, this may all be due to timing. Loss of state PURTA funds would have impacted many districts differently, and the legislation occurs during the same time frame as pension funds were released.

192

Those districts which were most impacted by PURTA changes, would have had the opportunity to “net” that loss via the pension rate reductions.

Several participants commented about categorical fund efforts and that they were hard to observe in this data alignment. For example, Governor Rendell’s major statewide effort with Computers for the Future (the CFF technology program), did not reveal any direct links to the data. The 600’s supplies and 700’s equipment object remained remarkably consistent in share across the decade. Considering the billions that were shifted, that alone may indicate that categorical funds may have helped hold those expenditure shares level. At the scope of shifted dollars, the participants clearly felt that many categorical efforts are just too small to impact and got lost in this movement.

Revenue

Table 5.3 Revenue Increase Table

Revenues: Percent increase Year‐over‐Year

% Increase Total Local State % Federal % Other Rev over prior % Increase Increase Increase % Increase fiscal Year 1998‐99 5.2% 3.6% 6.8% ‐23.4% 1999‐00 4.4% 2.9% 15.1% ‐16.8% 2000‐01 5.0% 2.7% 6.2% 17.1% 2001‐02 4.8% 4.3% 9.3% 205.1% 2002‐03 6.2% 4.6% 16.0% ‐65.4% 2003‐04 6.7% 4.0% 16.0% 62.1% 2004‐05 7.3% 7.7% 2.3% ‐5.1% 2005‐06 7.3% 3.8% 0.7% 4.9% 2006‐07 5.9% 7.8% ‐5.8% ‐28.0% 2007‐08 3.9% 5.0% 1.3% 9.2%

Average5.7%4.6%6.8%16.0% First half 5.1% 3.6% 10.7% 23.3% Second Half 6.2% 5.7% 2.9% 8.6%

193

What was surprising was the annual average percentage revenue growth for both

the state and local revenues. Revenue growth was considered to be strong over the decade

based on observations of the annual increase (see Table 5.3) Local increases averaged

5.7% and State increases average 4.6%. Combined with the pension funding ECR reductions expenditures would have had to match the pace to ‘balance the budget’. With new fund balance caps in play, as money flowed in either from local revenues or state, districts had to spend it or transfer it out of the general fund. In Table 5.3 Other Revenue in 2001-02 shows the 205% change which would capture districts making transfers in advance of fund balance restriction changes.

Figure 5.6 Revenue Sources Change in Share

194

Long term state funding in general continued its 30 year annual decline in share losing 2.1% during the study period (See Figure 5.6). The second-half of the study clearly shows a slowing of that trend as share loss was .4% versus 1.8% for the first-half. Federal revenue share decline in the second-half was a surprise given the era of student

accountability and NCLB. Overall Federal programs changed share by .4% for the decade

mitigating that portion of the state share loss.

Implications

First and foremost, the evidence from this study strongly supports the value of research utilizing this combined spending dimension approach to school expenditure analyses. More importantly, the lessons found in the fiscal policies for education observed in the study may not be learned by policy makers. If look-back analyses are not performed and included in future deliberations to understand linkage to spending patterns, learning from state policy impact cannot happen. The results of this investigation show billion dollar impacts which served to create significant spending and revenue pattern changes. Tax monies raised over two decades for one reason were spent for other reasons in a span of five years. Some of these changes were large enough in scope to dwarf categorical funding attempts and even the major basic education subsidy itself. It cannot be overstated that these large overriding factors are hidden a layer below

(Object level) much school finance investigative research, whether at the district or school level.

The scope of these movements, including the impact on instructional expenditures,

may influence state funding formulas as spending pattern changes disrupt formula

195

components. The money is still being spent at the district, but since everyone is spending

it differently, those trends should be known and understood. Additionally, characteristic

analyses showed over time that the poorest districts got wealthier, and the wealthier

districts got poorer. This also may have had impact on funding allocations.

The expert participants found value in the format and the visual that the trends

provided. Even though all of the participants lived and worked in public education during

this era, the data provided deeper insight into issues, events, and policy linkages to observed data trends. Participants were able to “see” issues related to the data findings and provide descriptive activity to help explain it.

Lack of Policy

The general lack of policy to scrutinize spending pattern trends allowed policy makers an escape from accountability.

 Pension funding reductions released $100’s of millions with no rules for

districts, IU’s, CTC’s, and the state itself for both pensions.

 Front loading of new charter mandates with freed up pension funds and very

little state commitment to share costs

 The same released funds masked Health Care cost growth, which at a

minimum were not substantially mitigated within the bargaining arena

 The same funds shifted to and for construction and PlanCon policy committed

state funds as well. The state took no action in an era of historic low interest

rates to help and guide infrastructure needs.

196

 Tax effort and capacity (for pension payments) which was painstakingly built

over a 15+ year period prior to the study was released in a few short years

 Each governor’s administration saddled the next with ever larger liabilities

 Each Administration and all the General Assemblies kicked the can down the

road again and again on significant liability issues.

With regard to pensions and released funds in the first-half of this study, while purposefully ‘not acting’ can be interpreted as policy at work, the findings here clearly suggest for the future, it probably is not a good policy. It lay at the center of many intertwined complex events inclusive of district spending pattern change. Therein lay a critical warning for a decade out or so. Beginning in 2010, continued pension ECR increases are much larger than actual rate increases in the second-half of the study and

ECR are projected to reach levels at 30% of payroll. For perspective, the ECR rate was

7.13% in 2007-08, in 2014 it is scheduled to be 16.93%, and in 2019 it is projected to be

30.14% (PSERS projections as of June 30, 2012).

These funds that are to be raised (taxed) for public education to meet the current fiscal crisis, will likely still be in the education funding system fifteen to twenty years from now. PSERS’ estimates of the dollar value needed are nearly $5.5 billion more annually than is currently being contributed. Eventually, pension rates are projected to come down, and when they do, PSERS projections show $3 to $4 billion dollars released from state and district budgets in as few as three years. At that point, Pennsylvania will face a very similar situation as in the first-half of this study period, except with three to four times the amount of money. Policy makers would be well advised to heed the

197

warnings of the early years of this study. Planning for the effective use of these funds in future years is important to be done ahead of time to achieve policy goals.

The next five years

Adding the next five years to this study would add substantially to understanding post-recession impact. Consistent dynamic analyses for future years present an opportunity to observe expenditure pattern changes during unparalleled historic school finance events. To note just a few examples in the subsequent years:

 In 2008-09 for the first time significant dollars in gambling funds were

distributed to districts across the state

 In 2009-10 Federal American Recovery and Reinvestment Act of 2009

(ARRA) funds supplanted state funding targeted for Basic Education

 In 2010-11 Continued Federal ARRA plus Federal Education Jobs Act of 2010

Funds supplanted state funds targeted for Basic Education; pension ECR rates

begin to climb significantly

 In 2011-12 State education funding streams were reduced nearly $900 million

and Federal Funding noted above was eliminated; pension ECR rates continued

their rapid growth; PPACA Federal health care legislation impact begins

There will be no shortage of billion dollar events in this five year period which are likely to cause revenue and expenditure trends and shares to be altered.

198

Future studies

Based on these results, the methodology of multi-dimension review at lower account code levels may well provide improved understanding, particularly in select units of study. Utilizing sub-object / sub-function level review for expenditure trends in districts with similar characteristics may provide valuable information with regard to

finding similarities or discrepancies among them, including student outcomes.

The results of this research support the concept that districts are in “motion” with

regard to spending pattern trends, which means it may be critical to understand what

those patterns / directions are for:

 Researchers investigating a particular unit of study and comparability.

 Policymakers crafting and implementing new programs to weigh predictable

disparate impact.

 Local education leadership to understand their own organizational patterns

and trends for local decision making.

The researcher, a new policy, or an event intersects a district at a point-in-time and the district’s spending patterns are in motion and the speed and direction of that path matters. Spending trend direction and strength within function and object should be

understood with regard to a district’s alignment with the average or as an outlier. To study and compare districts without this understanding may miss valuable indicators and clues.

During the era of NCLB and significant annual revenue growth, this study showed that instructional spending share in Pennsylvania fell. This may serve to diminish effectiveness intended in state subsidy formula results. No formula is perfect, but the

199

same formula applied to districts that were spending money very differently during the

study time frame would produce different results. Further, the data show districts with

different size and wealth characteristics were impacted differentially with regard to salary share and tuition or contracting share. Expenditure trends which create wide variances

between districts which contract and those who use in-house staff will impact account

code allocations and perhaps funding formulas. It is possible that funding allocation

formulas using instructional expense as a factor are not being adjusted accordingly given

the scope and direction of spending pattern changes. Further, given the post study economy and known school funding issues, the evidence casts doubt on determining if

instructional spending will decrease or rise dramatically.

Instructional spending share may continue to fall as districts have implemented

furloughs and utilized position attrition vacancies to lower costs. However, this study

shows instruction spending share loss was also linked to reduced pension payments.

Therefore it is just as likely that reductions in other non-instructional areas (i.e. budget

cuts) and significantly increased benefit expenditures for health care, pension, and charter

mandates tied to instructional functions will serve to increase instructional spending as a

share of the total. At least, this is what it could look like in theory. Many studies work to

link inputs (Roza and Swartz 2007, on allocating resources) with student outcomes and they by necessity delve into “instructional” inputs. While student outcomes are by far the

most important of all measures, it is clear that a significant variable on the input side are

driven by a confluence of policies outside of the districts’ control.

200

Summary

Upon review of the findings one participant, Jay Himes, Executive Director of

The Pennsylvania Association of School Business Officials summed up observations by stating:

"For the long‐term, we could not have planned any better to make things

worse”. (Quoted with permission, Interview with the author October 15,

2010)

Another participant Dr. Dave Davare, Retired, Director of Research for the Pennsylvania

School Boards Association put it this way:

“It is worse than it looks; most of the expenditure share shift went to long-

term fixed costs like charter schools and other tuition as well as long-term

debt. The first-half of the study front loaded salary and benefit cost

increases, specifically for health care coverage later followed by pension

cost growth. All of these expenditures are very hard to slow down, control

or eliminate, and other than level debt, all will increase annually”.

(Quoted with permission, Interview with the author September 14, 2010)

The hodge-podge of conflicting, counter-intuitive, and often adversarial educational policy implementations place Commonwealth school finance schemes in the center ring of a three ring circus. It is arguable, however, that most circuses are better organized.

Sargut and McGrath’s (2011) research on complexity note that unintended consequences come from even small decisions in complex environments. They warn of

201

failing to grasp critical interdependencies and being blinded by focusing so much on one

thing it prevents seeing others. They note three major precepts:

1. Events interact without anyone meaning them to.

2. Consequences are based on an aggregate of individual elements.

3. All too often, policies and procedures remain in place long after the reason for

the creation becomes obsolete.

This study provided insight into patterns and relationships and the way various

elements interacted in a complex system. By looking at what has already happened, and

establishing a base-line for where things ended, future work about where things could go

and how the system may respond is better informed.

The Commonwealth’s current fiscal stress on school finance will be long-lived

and had its origins in this study’s decade. In as much today’s education policy strives to

hold individual districts accountable for student results. Trending and tracking

education’s financial lead and lag change linkages may be a missing component for holding policy makers accountable. Trended change in revenues or spending patterns in and of themselves may not be a bad thing; however, not knowing the direction, speed and scope of those changes, is clearly not a good thing. Standard and consistent analyses which provide the next year of a pattern trend may well be enough to validate a direction worth heading, or recognizing one that needs altered. Not doing such analyses, or at least not doing it well, holds no one accountable while the impact of policy decisions escape public debate.

202

203

Appendix A – Figure Listing by Page

Chapter 1: Introduction Page

Figure - 1.1 Pennsylvania Act 1 Base Index History 16

Chapter 2: Review of Literature

Figure - 2.1 PSERS Total Employer and Employee Rate 30

Chapter 3: Research Design and Methodology

Figure - 3.1 Average ADM: 50 Smallest Districts 66

Figure - 3.2 Average ADM: 50 Middle Districts 67

Figure - 3.3 Average ADM: 50 Largest Districts 67

Figure - 3.4 MVPI Average of 50 Highest Wealth Districts 68

Figure - 3.5 MVPI Average of 50 Middle Wealth Districts 68

Figure - 3.6 MVPI Average of 50 Lowest Wealth Districts 69

Figure - 3.7 Significant Events in School Funding Over Time 70

Chapter 4: Data

Figure - 4.1 Object Dimension Change in % Share 80

Figure - 4.2 100's Salaries Percent Share of Total Expenditures 83

Figure - 4.3 100's 2007-08 Percent Share Scatter Plot 85

Figure - 4.4 100's 07-08 v. 97-98 Percent Share Scatter Plot 86

Figure - 4.5 200's Benefits: % Share of Total Expenditures 87

Figure - 4.6 200's Benefits: Annual % Increase 88

Figure - 4.7 300's Prof. Services: % Share of Total Expenditures 89

Figure - 4.8 300's Professional Services: Annual % Increase 90

204

Page

Figure - 4.9 400's Property Services: % Share of Total Expenditures 91

Figure - 4.10 400's Purchased Property Services: Annual % Increase 92

Figure - 4.11 - 500 Object: Percent Share of Total Expenditures 93

Figure - 4.12 500's Other Purchased Services: Annual % Increase 94

Figure - 4.13 600's Supplies: % Share of Total Expenditures 95

Figure - 4.14 600's Supplies: Annual % Increase 96

Figure - 4.15 700's Equipment: % Share of Total Expenditures 97

Figure - 4.16 700's Equipment: Annual % Increase 98

Figure - 4.17 800's Other Objects: % Share of Total Expenditures 99

Figure - 4.18 800's Other Objects: Annual % Increase 100

Figure - 4.19 900's Other Financing Uses: % Share of Total Exp. 101

Figure - 4.20 900's Other Financing Uses: Annual % Increase 102

Figure - 4.21 Share of New Dollars By Object 97-98 to 07-08 104

Figure - 4.22 Change in % Share: Major Function 106

Figure - 4.23 Sub-Functions: % Share Change From 97-98 to 07-08 112

Figure - 4.24 Total Instruction 1000's: % Share 113

Figure - 4.25 Regular Education - Elem & Sec 1100's: % Share 114

Figure – 4.26 Special & Gifted Ed 1200’s: % Share 115

Figure - 4.27 Vocational Ed 1300's: % Share 116

Figure - 4.28 Other Instructional Programs 1490's: % Share 116

Figure - 4.29 2000's Support Services: % Share of Total Expenditures 118

205

Page

Figure - 4.30 Office of Principal Services: % Share 118

Figure - 4.31 Operation & Maintenance of Plant 2600's: % Share 119

Figure - 4.32 % Share of Expenditures: Selected Support Sub-Functions 120

Figure - 4.33 Operation of Non-Instructional Services 3000's: % Share 121

Figure - 4.34 Facilities Acq. Const. & Improvement 4000's: % Share 122

Figure - 4.35 Other Expenditures and Financing Uses: % Share 123

Figure - 4.36 Change in % Share: Local, State & Federal 126

Figure - 4.37 Total Local Revenue as % Share of Total Revenue 127

Figure - 4.38 State Revenue as % Share of Total Revenue 128

Figure - 4.39 State & Local Revenues: Annual % Increase 130

Figure - 4.40 Change in Share of Total Revenues for Local Taxes & Total Local

Revenues 131

Figure - 4.41 100 Object Series: Change in Share Over Time (ADMS) 135

Figure - 4.42 200 Object Series: Change in Share Over Time (ADMS) 136

Figure - 4.43 500 Object Series: Change in Share Over Time (ADMS) 137

Figure - 4.44 900 Object Series: Change in Share Over Time (ADMS) 138

Figure - 4.45 100 Object Series: Change in Share by MVPI 141

Figure - 4.46 500 Object Series: Change in Share by MVPI 142

Figure - 4.47 600 Object Series: Change in Share by MVPI 143

Figure - 4.48 900 Object Series: Change in Share by MVPI 144

206

Chapter 5: Patterns Revealed, Trends Emerged, and Changes Observed Over Time

Page

Figure - 5.1 100 Object: Percent Share of Total Expenditures 155

Figure - 5.2 200 Object: Percent Share of Total Expenditures 162

Figure - 5.3 500 Object: Percent Share of Total Expenditures 168

Figure - 5.4 Object 900: Percent Share of Total Expenditures 173

Figure - 5.5 State and Local Revenue Percent Increase Trends 191

Figure - 5.6 Revenue Sources Change in Share 194

207

Appendix B – Table Listing By Page

Chapter 1: Introduction Page

No Tables

Chapter 2: Review of Literature

Table - 2.1 Function and Object Codes 23

Table - 2.2 School Code Fund Balance Limits 34

Table - 2.3 Public School Enrollment 39

Chapter 3: Research Design and Methodology

Table - 3.1 Data Sources and Organization 53

Table - 3.2 Descriptive Statistic Calculations for Expenditures 59

Table - 3.3 MVPI Aid Ratio: Pennsylvania Wealth Measure 64

Chapter 4: Data Analysis

Table - 4.1 Total Spending Analysis: 501 PA School Districts 78

Table - 4.2 Share of Dollars Flowing into Expenditures 81

Table - 4.3 Percent Share of New Dollars Flowing into Expenditures 82

Table - 4.4 Major Function: % Share of Total Expenditures 107

Table - 4.5 Major Function: % Share Change 97-98 to 07-08 108

Table - 4.6 Major Function and Sub-Functions: Total Expenditures 109

Table - 4.7 Sub-Function % Share; Share Change; % of $ Growth 110

Table - 4.8 Sub-Function Split Study and Full Study % Share Change 111

Table - 4.9 % Shares for Local, Federal & Other Revenues 124

Table - 4.10 Revenue Share Changes First and Second-Half 125

208

Page

Table - 411 Revenues: % Increase Year-Over-Year 129

Table - 4.l2 Observation of Significant Share Change Activity 133

Table - 4.13 Average ADM Size for Targeted Districts 134

Table - 4.14 Change in Share Object Study by Wealth of District 140

Table - 4.15 MVPI Average by Year: Sub-Group of 50 Districts 145

Table - 4.16 Power Point Alignment of Data Results 147

Table - 4.17 Major Topic Counts from the Interviews 148

Chapter 5: Patterns Revealed, Trends Emerged, and Changes Observed Over Time

Table – 5.1 ADM Characteristics 50 Smallest, 50 Middle, & 50 Largest 176

Table - 5.2 MVPI Characteristics: 50 Highest Wealth, 50 Middle Wealth, & 50

Lowest Wealth 179

Table - 5.3 Revenue Increase Table 193

209

Appendix C – Miscellaneous / Secondary Analyses

C.1 Annual % Increase in Selected Objects

Annual Percent Increase By Object 25.00% 100's Salaries

20.00% 200's Benefts 15.00% 500's Other 10.00% Purch srvs 900's 5.00% Other Uses

0.00%

‐5.00%

210

C.2 ADM and MVPI Matrix of Share to Average District

Matrix of Object Share Position Relative to The Average District Lowest Highest Middle Large Middle FY & Object Small ADM Wealth Wealth Wealth ADM ADM MVPI MVPI MVPI 1997‐98 100 ‐2.28% 3.26% ‐1.56% ‐3.20% 2.49% ‐1.27% 2002‐03 100 ‐0.50% 2.44% ‐0.59% ‐2.30% 1.03% ‐1.74% 2007‐08 100 ‐4.05% 1.97% 0.19% ‐1.84% 2.65% 0.31% 1997‐98 200 ‐0.85% 0.47% ‐0.56% ‐0.60% ‐0.42% ‐0.67% 2002‐03 200 ‐0.12% 0.35% ‐0.26% ‐0.08% ‐0.71% 1.57% 2007‐08 200 ‐0.82% 0.40% 0.25% 0.06% ‐0.36% 0.00%

1997‐98 300 ‐0.80% ‐1.35% ‐1.00% ‐0.10% ‐1.62% ‐0.12% 2002‐03 300 ‐1.61% ‐1.31% ‐1.45% ‐0.98% ‐1.01% ‐0.27% 2007‐08 300 ‐1.17% ‐1.12% ‐1.56% ‐0.46% ‐1.48% ‐0.89%

1997‐98 400 0.17% ‐0.01% 0.14% ‐0.30% 0.70% ‐0.06% 2002‐03 400 0.10% 0.12% 0.17% ‐0.09% 0.74% 0.10% 2007‐08 400 ‐0.05% ‐0.02% 0.13% ‐0.06% 0.57% 0.09% 1997‐98 500 2.74% ‐0.83% 1.15% 2.06% ‐0.23% 1.01% 2002‐03 500 ‐0.09% ‐0.69% 0.10% 1.43% ‐1.10% ‐0.04% 2007‐08 500 4.47% ‐0.63% ‐0.44% 2.28% ‐1.35% 0.46%

1997‐98 600 0.19% ‐0.04% 0.23% 0.46% ‐0.05% ‐0.08% 2002‐03 600 0.42% ‐0.06% 0.29% 0.77% ‐0.02% ‐0.12% 2007‐08 600 0.24% 0.03% 0.31% 0.73% ‐0.40% 0.22%

1997‐98 700 0.27% ‐0.18% 0.25% 0.20% ‐0.08% 0.12% 2002‐03 700 0.14% ‐0.11% 0.14% 0.48% 0.25% ‐0.05% 2007‐08 700 0.16% ‐0.08% 0.37% 0.22% 0.15% 0.05% 1997‐98 800 0.08% 0.02% 0.52% ‐0.02% ‐0.38% 0.69% 2002‐03 800 0.21% ‐0.03% 0.36% 0.06% 0.29% ‐0.25% 2007‐08 800 ‐1.23% 0.22% ‐0.15% ‐0.69% 0.21% ‐0.41%

1997‐98 900 0.47% ‐1.35% 0.82% 1.50% ‐0.40% 0.39% 2002‐03 900 1.44% ‐0.71% 1.25% 0.72% 0.52% 0.80% 2007‐08 900 2.44% ‐0.76% 0.90% ‐0.23% 0.02% 0.18%

211

C.3 - Summarization: Count of Interview Candidate Comments/Observations by

Interview Guide Grouping

Summarization: Count of Interview Candidate Comments / Observations by Grouping

"Topical Candidates: # 1‐71234567Total Grouping"

A Purpose 1010011 4 Function and Object B Overview 2 1 1 0 0 1 0 5 ADM and Expense C per ADM 1 4 21112 12

DObjects Statewide 8 12 8 6 7 12 6 59 Objects dollars (New Money & share of E $'s) 33374 33 26

Functions and sub‐ F functions 6 10 3 4 562 36 ADM's 50/50/50 G (District size)21175574 41 MVPI 50/50/50 H (District Wealth)18973122 42 JPSERS Dollar Est2122121 11 Objects: % share K Charters 231114 0 12 L Revenues2335112 17 Revenue: Basic Ed M Funding 1 1 3 3 0 63 17 N Policy Specific5000210 8 O Discussion / Other1050020 8

Totals: 37574841305926298

212

Appendix D – Definitions and Diagram for Policy and Funding Flow

D.1 Objects: Definitions (PDE Accounting manual)

Major Objects: Definitions:

This dimension is used to describe the object, which is the service or commodity obtained as the result of a specific expenditure. There are nine (9) major object categories, each of which is divided into sub-objects for more detailed accounting.

100 PERSONNEL SERVICES – SALARIES Gross salaries paid to employees of the LEA who are considered to be in positions of a permanent nature or hired temporarily, including personnel substituting for those in permanent positions. This includes gross salary for personnel services rendered while on the payroll of the LEA. (Expenditures may be charged to the following sub-accounts.) (See Job Classification Dimension for specific job titles and descriptions.)

200 PERSONNEL SERVICES – EMPLOYEE BENEFITS Amounts paid by the LEA on behalf of employees; these amounts are not included in gross salary, but are in addition to that amount. Such payments are fringe benefit payments; and, while not paid directly to employees, are part of the cost of personnel services. (Charges are not recorded to this account but to the following sub-accounts.)

300 PURCHASED PROFESSIONAL AND TECHNICAL SERVICES Services that by their nature require persons or firms with specialized skills and knowledge. Included are the services of architects, engineers, auditors, dentists, medical doctors, lawyers, consultants, teachers, accountants, tax collectors etc. Payments for services provided by Intermediate Units to LEAs should be recorded to this object, not object 560. (Expenditures may be charged to the following sub-accounts, if the breakout is desired by the LEA, with the following note: Objects 322, 323, and 329 for certain functions are required to be reported on the Annual Financial Report.)

400 PURCHASED PROPERTY SERVICES Services purchased to operate, repair, maintain and rent property owned and / or used by the LEA. These services are performed by persons other than LEA employees. (Expenditures may be charged to the following sub-accounts, if the breakout is desired by the school district.

213

500 OTHER PURCHASED SERVICES Amounts paid for services not provided by LEA personnel but rendered by organizations or personnel, other than Professional and Technical Services and Purchased Property Services. (Charges are not recorded to this account but to the following sub-accounts.)

600 SUPPLIES Amounts paid for material items of an expendable nature that are consumed, worn out, or deteriorate in use; or items that lose their identity through fabrication or incorporation into different or more complex units or substances. (Charges are not recorded to this account but to the following sub-accounts.)

700 PROPERTY Expenditures for the acquisition of fixed / capital assets, including expenditures for land or existing buildings and improvements of grounds; initial equipment; additional equipment and replacement of equipment. (Charges are not recorded to this account but to the following sub-accounts.)

800 OTHER OBJECTS Amounts paid for expenditures not otherwise classified in objects 100 through 700. (Charges are not recorded to this account but to the following sub- accounts.)

900 OTHER USES OF FUNDS This series of codes is used to classify transactions which record redemption of principal on long-term debt, authority obligations, fund transfers, and transfers to component units (as defined by GASB Statement #14). Charges are not recorded to this account but to the following sub-accounts.

214

D.2 Major Functions: Definitions (PDE Accounting manual)

Major Functions: Definitions

1000 INSTRUCTION Instruction includes all those activities dealing directly with the interaction between teachers and students and related costs1, which can be directly attributed to a program of instruction. Teaching may be provided for students in a school classroom, in another location such as a home or hospital, and in other learning situations such as those involving co-curricular activities. It may also be provided through some other approved medium such as television, radio, telephone and correspondence. Included here are the activities of aides or classroom assistance of any type (clerks, graders, teaching machines, etc.) that assist in the instructional process. Do not record administrative instructional support costs here.

1 Related costs include instructional expenditures for salaries, contracted services, travel expenses, equipment rental, supplies, books, maintenance costs directly attributable to instructional equipment and other expenses such as sabbatical leaves.

2000 SUPPORT SERVICES Support Services are those services that provide administrative, technical (such as guidance and health), and logistical support to facilitate and enhance instruction. Support Services exist as adjuncts for the fulfillment of the objectives of instruction, community services, and enterprise programs, rather than as entities within themselves. (Record expenditures to the following sub-accounts.)

3000 OPERATION OF NON-INSTRUCTIONAL SERVICES Activities concerned with providing non-instructional services to students, staff or the community. (Record expenditures to the following sub-accounts.)

4000 FACILITIES ACQUISITION, CONSTRUCTION AND IMPROVEMENT SERVICES Capital Facilities Acquisition, Construction and Improvements are capital expenditures incurred to purchase land, buildings, service systems and built-in equipment. Expenditures include the initial purchase of land and buildings; construction; remodeling, additions and improvements to buildings; initial installation, replacement or extension of service systems; and other built-in equipment, as well as improvement to sites, and activities related to all of the above.

215

Capital expenditures relate to costs benefiting more than one fiscal year and include all costs incurred to (1) bring the asset to a state of usefulness or (2) extend the useful life of an existing asset. All incidental costs associated with a fixed asset such as: sales tax, freight, insurance on freight, transfer fees, demolition costs, grading, and installation, as well as legal, contracted service fees and engineering fees associated with a capital expenditure should be included in the cost of the asset and recorded to the following sub-functions.

5000 OTHER EXPENDITURES AND FINANCING USES This category includes current debt service expenditures and other expenses (expenditures and other financing uses). Other financing uses represent the disbursement of governmental funds not classified in other functional areas that require budgetary and accounting control. These include the refunding of debt and transfers of monies from one fund to another and to component units. Other expenditures recorded to this account series include refunds of prior period receipts and revenues, and current debt service expenditures. (Do not record transactions here, but use the following sub-accounts.)

216

D.3 Diagram: Policy and Funding Flow to Function and Object Reporting (By author)

Local, State, and Federal Policies, Mandates, and Initiatives

Federal Revenue State Revenue Local Revenue

Total Funds

Func Obj Predefined

100's Instruction 200's

Support 300's

400's Non‐ Instructional 500's Capital Outlay 600's Debt / Transfers / 700's 800's

900's

Total Expenditures

217

References

Aarons, D. I. (2008). Districts Cut Back Busing, Seek Ways to Save Energy. Education

Week, 28(1), 1-16.

Anyon, J. (2005). Radical Possibilities: Public Policy, Urban Education, and a New

Social

Movement. New York: Routledge, 2005.

Baker, B. & Ramsey, M. (2010). What We Don’t Know Can’t Hurt Us? Equity

Consequences of Financing Special Education on the Untested Assumptions of

Uniform Needs, Journal of Education Finance, 35(3) 245-275 Winter 2010.

Berne, R. & Stiefel, L. (1993). Cutback Budgeting: The Long-term Consequences. The

Journal of Policy Analysis and Management, 12(4) 664-684.

Boyd, D., Lanford, H. & Wyckoff, J. (2002). School District Expenditures and Fiscal

Stress, Education Finance Research Consortium, Center for Policy Research,

University at Albany, Albany, New York. EA 032 350; ED 474 397.

Boyd, William (2000). The “R’s of School Reform” and the Politics of Reforming or

Replacing Public Schools, Journal of Education Change, vol. 1, No. 1, September

2000.

Chambers, J. G., (1999). National Center for Education Statistics, & Educational

Resources Information Center (US). Measuring resources in education: From

accounting to the Resource cost model approach. US Department of Education,

Office of Educational Research and Improvement, National Center for Education

Statistics.

218

Davare, David (2008). Rising Costs...Falling Funding, Pennsylvania School Boards

Bulletin, February 2008. P.18-23.

Denison, D. & Stiefel, L. & Hartman, W. & Deegan, M. (2009). Accounting for Resource

Use at the School-Level and Below: the Missing Link in Education

Administration and Policy Making, IESP Working Paper Series Number 09-06

May 2009, NYU Steinhardt School of Culture, Education, and Human

Development.

Fahy, C. (2011). Education funding in Massachusetts: the Effects of aid Modifications on

Vertical and horizontal Equity, Journal of Education Finance, 36, No. 3., Winter

2011.

Fuhrman, S.H. (1994). Issues and Data Needs in School Finance. In W. J. Fowler, Jr.,

(Ed.), Selected Papers in School Finance 1994 (NCES Publication No. 96-068,

pp. 29-49. Washington, DC: U.S. Department of Education. (ERIC Document

Reproduction Service No. ED397529)

Gabel, J., Claxton, G., Holve, E., Pickreign, J., Whitmore, H., Dhont, K., & Rowland, D.

(2003). Health Benefits in 2003: Premiums Reach Thirteen-year High as

Employers Adopt New Forms of Cost Sharing. Health Affairs, 22(5), 117-126.

Hanneway, J., McKay, S., & Nakib, Y. (2000). Reform and Resource Allocation:

National Trends and State Policies. In W.J. Fowler, Jr., (Ed.), Developments in

School Finance, 1999-2000. Fiscal Proceedings from the Annual State Data

Conference. (NCES Publication No. 2002-316, pp. 57-77). Washington, DC: US

Department of Education. (ERIC Document Reproduction Service No.

ED470015)

219

Hanushek, E.A. (2007). Incentive-Based Financing of Schools, The School Finance

Redesign Project, Working Paper Number Fourteen, Daniel J. Evans School of

Public Affairs, University of Washington.

Hess, F. M., & Downs, W. (2010). K-12 Budget Picture: The Lean Years Ahead.

American Enterprise Institute for Public Policy Research, (10).

Hill, P. & Roza, M. & Harvey, J. (2008). Facing the Future: Financing Productive

Schools, Final Report. Seattle: University of Washington, Center on Reinventing

Public Education, School Finance Redesign Project.

Hurley, R. E., & Felland, L., & Gerland, A., & Pickreign, J. (2006). Public Employee’s

Health Benefits Survive Major threats, So Far. Health Affairs, May 2006 vol. 25,

no, 3. p.195 - 203.

Kirst, Michael & Rhodes, Lori (2007). Two Alternative Yet Complimentary Conceptual

Frameworks for Financing American Education, The School Finance Redesign

Project, Working Paper Number Twenty-Two, Daniel J. Evans School of Public

Affairs, University of Washington.

Lewin, T., & Dillon, S. (2010). Districts Warn of Deeper Teacher Cuts. The New York

Times, 20.

Liebschutz, D. & Boyd, D. (1996). Education Spending Faces Demographic and Other

Pressures. New York: State university of New York, Center for the Study of the

States, State Fiscal Brief No. 38.

McNichol, E., Oliff, P., & Johnson, N. (2011). States continue to feel recession’s impact.

Center on Budget and Policy Priorities, 9.

220

Monk, David H. 2007. Out of the Box: Fundamental Change in School Funding.

Working Paper 12. Seattle: University of Washington, Center on Reinventing

Public Education, School Finance Redesign Project.

Neymotin, F. (2010). The Relationship between School Funding and the Student

Achievement in Kansas Public Schools. Journal of Education Finance, 36, No. 1.,

Summer 2010.

Pittner, N.A. & Carleton, M.M & Casto, C. (2010). School funding in Ohio: From

DeRolph to Evidence-Based Model (EBM) and Beyond. Journal of Education

Finance, 36, No.2., Fall 2010.

PSBA (2010). Pennsylvania School Boards Association (A White Paper). Pennsylvania

Charter Schools: Charter / Cyber Charter Costs for Pennsylvania School Districts;

Education Research and Policy Center publication, October 2010.

Reeher, Grant. (2003). Reform and Remembrance: The Pace of the Private Sector in the

Future of Health Care Policy. Journal of Health Politics, Policy and Law. Vol. 28,

No. 2-3: p355 – 386.

Rothstein, R. (1997). Where’s the Money Going? Changes in the Level and Composition

of Education Spending, 1991-1996. Economic Policy Institute. Washington, D.C.,

Economic Policy Institute. www.epinet.org.

Roza, M. (2007). Autonomy: How District Policies That Deploy Resources ca Support

(or Undermine) District Reform Strategies. The School Finance Redesign Project,

Working Paper Number Twenty-four, Daniel J. Evans School of Public Affairs,

University of Washington.

221

Roza, M. & Swartz, C. (2007). “School Spending Profiles: A Framework to Enlighten

Resource Allocation Decisions,” Public Budgeting and Finance: 69-85.

Sargut, G. & McGrath, R. (2011). “Learning to Live with Complexity,” Harvard

Business Review: September 2011 68-76.

Schaefer, Adam (2010). They Spend What? The Real cost of Public Schools. Cato

Institute Policy Analysis publication no. 662, March 10, 2010.

Shrom, T. & Hartman, W. T. (2008). Your Schools, Your Money: Spending Analysis and

Management Project, Working Paper, Education Leadership Program, College of

Education, Pennsylvania State University.

Stoicecu, C. D., & Hartman, W. T. (2004). Funding Elementary and Secondary Education

in Pennsylvania: Trends in State and Local Funding in the 1990’s. Journal of

Education Finance, 29, 337-357.

222

Unpublished Interviews

1. E1, Director of Research for the Pennsylvania School Boards Association

(PSBA), in discussion with the author September 14, 2010.

2. E2, Executive Director of the Pennsylvania Association of School Business

Officials (PASBO), in discussion with the author October 5, 2010.

3. E3, Executive Director of the Pennsylvania Association of School Administrators

(PASA), in discussion with the author October 19, 2010.

4. E4, Pennsylvania State Education Association (PSEA) and Former Executive

Director of the Pennsylvania Association of School Administrators (PASA), in

discussion with the author October 19, 2010

5. E5, Retired, member of the Pennsylvania State Board of Education and the

Pennsylvania Association of Rural and Small School Schools Board (PARSS), in

discussion with the author November 2, 2010.

6. E6, Executive Officer of Public Financial Management (PFM), in discussion with

the author November 2, 2010.

7. E7, Executive Director of the Pennsylvania Association of Rural and Small

Schools (PARSS), in discussion with the author November 11, 2010.

223

Vita

Timothy J. Shrom, PRSBA; (Pennsylvania Registered School Business Administrator)

Timothy (Tim) J. Shrom has served as the Business Manager at the Solanco School District in Quarryville, Pennsylvania since 1982. Prior to Solanco he served in the banking sector as both a loan officer and branch manager. Mr. Shrom holds a B.S. Degree in Business from Elizabethtown College, a Masters Degree in Educational Leadership from Pennsylvania State University, where he has also done graduate work in their MBA program.

He has served as President of the Pennsylvania Association of School Business Officials (PASBO), and represented the Association of School Business Officials International (ASBOI) as a member of the National Center for Education Statistics (NCES) School Facilities Task Force.

He has presented on school finance related topics statewide and nationally for over two decades for PASBO, ASBOI, the American Education Finance Association (AEFA / now AEFP for Finance Policy), and the National Education Finance Conference (NEFC).

Mr. Shrom was honored in 2005 as Elizabethtown College’s Outstanding Business School Graduate. This recognition is presented to one business graduate alumnus annually. In 2013 he was recognized as a distinguished Fellow by the National Education Finance Conference (NEFC). He has been recognized as the Outstanding School Business Official of the Year at both State (PASBO) and National (ASBOI) levels. Solanco School District has been recognized statewide and nationally for both student performance and effective fiscal operations. Recognition for district quality fiscal management has been awarded each year since 1997 inclusive of both the Certificate of Excellence in Financial Reporting and the Certificate of Achievement for Excellence in Financial Reporting.

224