Alternative Revenue Generation in Vermont Public Schools:

Raising funds outside the tax base to

support public education

An honors thesis for the Department of Economics

Jason Steinman

Tufts University 2005

Copyright © 2005 Jason Steinman

All rights reserved

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Introduction

Concerns of educational equity have motivated a series of education finance reforms at

the state level, often dramatically altering the funding streams available to schools. In the past 30

years, states including California, Vermont and Michigan have passed legislation linking

education reform and tax relief, weakening the autonomous capacity of municipalities to raise

revenue through property taxes to support education. School district adaptation to altered policy

incentives and the general fiscal strain of state financial crises, escalating special education costs

and No Child Left Behind compliance, juxtaposed against rising enrollments and rising

expectations for academic success and accountability, demands taking advantage of new

opportunities for revenue creation. Fiscal shortfalls present a noxious alternative, imperiling art

and music instruction, non-fee athletic participation and extracurricular programs, obstructing

purchases of new technology, depressing teacher and administrator salaries and placing upward

pressure on class sizes.

Nationwide, public school districts are increasingly seeking revenue from non-tax

sources, through a variety of direct fundraising strategies and through affiliated fundraising

organizations (local education foundations, parent teacher associations, parent teacher

organizations and booster clubs). The growing importance of private funds in public schools

raises a number of questions. This thesis uses the intriguing case of Vermont to shed light on

three of them: How sensitive is school district fundraising to policy changes that create

incentives for the generation of private revenue? Why do some school districts and supporting

organizations raise private funds more effectively than others? What are the most successful

strategies for the successful raising of private funds by both school districts themselves and the

affiliated fundraising organizations that support them?

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The existing literature on the questions of interest is relatively sparse. The only study

explicitly linking policy incentives to increased public school private revenue generation is

Brunner and Sonstelie’s (1996) analysis of the California case. Downes (2003) explores the

effect of the Vermont reforms as to their equalization of student performance, and Schmidt and

Scott (2004) examines the impact of the reforms on the tax prices for education faced by

Vermont towns, but there has been no work done explicitly linking these reforms to a change in

school fiscal behavior.

Addonizio (1998, 2000) identifies a variety of mechanisms for private resource

generations in public schools. The only academic investigation of fundraising performed directly

by schools and districts appears to be Zimmer, Krop and Brewer’s (2003) investigation of 6

Southern California districts. The literature on supporting fundraising organizations – local

education foundations, parent teacher associations/organizations and booster clubs is somewhat

broader. However, these studies generally leverage only a single source of information: either

quantitative IRS data, quantitative school district data or survey data, and rarely combine this

quantitative information with non-empirical interviews. In addition, none of these studies have

shed light on the relative efficacy of specific fundraising strategies.

A dramatic policy change and stable demographic conditions in Vermont provide an

excellent opportunity to gain unique insight into the sensitivity of public school alternative

revenue generation to policy incentives and determinants of its success. The Equal Educational

Opportunity Act (Act 60) of 1997 created tax prices (Hoxby, 2001) of more than $1 for

education in 125 of Vermont’s 253 towns, and tax prices of $2 or more in 37 towns. (Schmidt

and Scott, 2004). For Stratton, the most extreme case, taxpayers were required to contribute raise

more than $30 in local taxes for every $1 increase in local education spending above the

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foundation amount (Schmidt and Scott, 2004). Since Act 60 did not impose regulations on the

generation of non-tax revenue, (every dollar of non-tax revenue generated directly increased

local education spending by $1), towns with a tax price of more than one dollar faced an

incentive to generate all local revenue for education above foundation outside of the tax system.

Act 60 sought to simultaneously provide property tax relief and promote education

reform by mandating the direct redistribution of local property tax revenue from property rich

towns, so-called “gold towns,” to property poor towns. As other district power equalization

reforms, Act 60 sought to equalize tax bases and per pupil tax yields for education across the

state. Act 60 importantly diverges from traditional district power equalization in the financing of

the system. While state funds traditionally supplement the tax yields of the low property wealth

towns, Act 60 created a sharing pool that directly tapped local property tax revenues from

property-rich towns to supplement yields of property poor towns. The sharing pool’s

redistribution mechanism directly seized local tax dollars from gold towns in addition to

providing revenue to property poor towns, simultaneously increasing the marginal tax price of

education in some towns, while reducing the tax rate required for a given level of additional

education spending in others. The equalization was nearly complete, with two primary

exceptions. Tax adjustments were granted to residents of wealthy towns with incomes at or

below $75,000, and gold towns were permitted to retain a small percentage (0.5%) of

remittances to the state as a collection fee. Incremental implementation of the law began in fiscal

year 1999 with full implementation in fiscal year 2001.

Act 60 generated significant unhappiness and strong criticism from the property rich

towns1 and was repealed by the Vermont legislature in 2004 by the passage of Act 68. Act 68

1 The lawsuit filed by Wilmington and Whitingham challenging the legality of Act 60, and Killington’s threat of secession provide the most extreme examples of the disapproval of the property-rich towns.

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eliminated the sharing pool, and reduced the marginal tax rates for education to below $1 in the

gold towns. Implemented in fiscal year 2005, Act 68 entirely removed Act 60’s incentives for

financing public education through non-tax revenue.

To avoid Act 60’s recapture provision, community members in many gold towns formed

local education foundations (LEFs), 501(c)3 non profit organizations with the express purpose of

raising resources for the public schools. These LEFs served as the primary conduit for generation

of sharing pool exempt non-tax revenue in the gold towns. In one case, a community education

foundation served as the channel for non tax revenue from property owners to the schools. The

Freeman Foundation provided more than $14 million in grants to these organizations between

1999 and 2001 to facilitate start-up and to match taxpayer donations. Foundations in towns with

as few as 1000 residents raised more than a million dollars in annual revenue, with some towns

disdaining to use property taxes to fund education at all. Incentives for private fundraising were

strong enough that some towns used strong arm tactics, such as publishing names of non-

contributors or boycotting non-contributing businesses.

Nevertheless, local education foundations were not successfully established in all

Vermont towns where there was a financial incentive for their operation. Among foundations

successfully launched, some raised per pupil more revenue than others. Some easily cleared

fundraising goals, while others struggled, and still others failed.

This thesis seeks to explain the response of the Vermont gold towns to Act 60 and the

varying success of their private fundraising efforts. In particular, empirical analysis will

investigate the rapidity and extent of the gold towns response, and the role demographics,

physical district characteristics, selection of fundraising strategy, and foundation governance and

operation play in the fundraising disparities between districts.

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The study encompasses fiscal years 1996 through 2003, covering the pre Act 60 period

(1996-1998) in which a traditional foundation formula allocated state aid, the incremental

implementation of Act 60 (1999-2000) and the full implementation of Act 60 (2001-2003).

Detailed quantitative IRS data on affiliated fundraising organizations, school district financial

and administrative data, Census data, survey data and personal interviews provide a rich basis of

information.

The first section reviews the existing literature. The next section describes the data

sources analyzed. The third section describes the methods used for analysis. Results appear in the

fifth section. The sixth section concludes.

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Literature Review

That people respond to incentives is a core tenet of basic economic theory. Its extension

to school districts shifting towards private revenue generation in response to policy incentives is

most rigorously explored by Brunner and Sonstelie (1996). In 1978, California’s Proposition 13

limited local property taxes to 1% of assessed value, severely inhibiting the ability of

municipalities to raise revenue to supplement state foundation aid. Brunner and Sonstelie chart

the explosive growth of local educations in California, from 22 in 1978, to more than 500 by the

mid-90s. By 1992, LEFs, PTAs, PTOs, booster clubs and urban foundations contributed more

than $100 million in total to support public education. Brunner and Sonstelie demonstrate that

districts whose revenue generating capacity was most severely constrained by Proposition 13

generated much more average private revenue per pupil than districts that were less constrained.

Although publicly funded in the United States, K-12 education does not possess the

traditional characteristics of a public good. Neither non-excludable (private schools certainly are

able to permit non-paying children from attending class) nor nonrival2, primary and secondary

education is primarily funded through federal, state and local government disbursal of tax

revenue. Justifications for public provision include the positive externalities of an educated

populace and an ethical commitment to the equal provision of opportunity through universal

equal access to education.

The first breakdown of the traditional method of publicly financing education occurred in

California after the passage of Proposition 13. Brunner and Sonstelie (2003) describe the fallout

from Proposition 13 as a government failure to provide a level of service (education) for which

taxpayers were willing to pay, equating private support of public schools to a type of voluntary

2 Studies (Hoxby, 2000) do exist which find no impact of class size on educational outcomes, but it is clear that, given one student, one classroom, one teacher and one textbook, the addition of an additional 50 students would negatively impact the first student’s learning.

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fiscal federalism. Collective action filled the taxation void generating new fundraising

organizations and strategies to privately supplement public schooling.

Devoid of the legally binding authority to compel cooperation, collective action is

perpetually haunted by the specter of free riding. In private fundraising efforts, school districts

and supporting organizations must be cognizant of the psychological and social forces that impel

charitable donations. Efforts to explain voluntary contributions include a pure altruism model

(Andreoni, 1988), in which a desire to increase the public good motivates generosity and a joint

product model (Cornes and Sandler, 1984), in which a desire to increase the public good is

coupled with the production of a private good – personal satisfaction (Steinberg, 1987 and

Andreoni, 1989), a signal of wealth (Glazer and Konrad, 1996) or prestige (Ledyard, 1995).

Other motivations identified include a desire to belong to a group or be identified with a cause, a

desire for acceptance, a desire for influence, appreciation, the pleasure of being asked to

contribute, a belief in a cause or organization, a desire to suppress further solicitations,

sympathy, (Brackley, 1980), religious beliefs, guilt, a desire for recognition, tax rewards, peer

pressure and a sense of personal obligation (Gunnin, 1991).

Brunner and Sonstelie posit that social interaction, through the creation of a group

identity among families, enhances individual generosity3. The depressing effect of face to face

encounters on free riding explains the greater success of foundations in Vermont towns with a

defined town center as compared to Vermont towns with no defined center (Fischel, 2003).

Fundraising organizations set annual fundraising goals, institutionalizing the collective

philanthropic decision-making process (Brunner and Sonstelie, 1996). To promote a sense of

3 The authors explicitly reject Sugden’s (1985) theory that social interactions reduce generosity through fear of one individual’s contributions encouraging others to free ride.

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sustainability and collective responsibility, many Vermont LEFs set a minimum monetary goal

and a minimum contribution rate for the initiation of a fund.

Social norms condemning free riding (Fehr and Gachter, 2000) on the contributions of

others encourage successful collective generosity, while individual differences in demand for

school quality, income level and attitudes toward free riding discourage equal contributions. The

authors hypothesize that individuals contribute up to where the price of cooperation equals the

cost of free-riding. In Vermont, foundations attempted to enhance contributions by raising the

cost of free-riding. Manchester’s LEF publicized names of non contributors, and some residents

refused to patronize businesses which were not scholastic benefactors (Fischel, 2003).

The literature has identified a myriad of fundraising strategies to channel philanthropic

forces towards contributions in support of public schools. Several single and multi-state surveys

have been undertaken to more closely examine the prevalence and success of various alternative

revenue generation mechanisms. Ron Zimmer, Cathy Krop, Tessa Kaganoff, Karen Ross and

Dominic Brewer (2001) studied the mechanisms used to raise support, the type of support raised,

profiles of donors, resource allocation between and among schools and the types of programs

private resources funded in six school districts in Los Angeles County diverse across district and

school size, academic performance, socioeconomic status and demographic characteristics4.

Carol Merz and Sheldon Frankel (1995) surveyed school districts in nine states with a variety of

equity requirements, tax systems and demographics5 to examine formation, governance,

fundraising, spending, community connections and equity effects of local education foundations.

The Massachusetts Department of Education and Education Policy Research, a Boston-based

4 The authors interviewed superintendents or members of the superintendent’s staff in each district, principals of ten elementary schools (at least one in each district) and three representatives from local education foundations. 5 The states selected were Washington, California, Oregon, Colorado, Texas, Illinois, Michigan, Georgia and New Jersey.

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educational consulting firm, undertook a survey of 26 LEFs in Massachusetts in 1988. Two

recent books on LEFs leverage personal experience as a consultant to LEFs (McCormick, Bauer,

and Ferguson, 2001) and qualitative research (Muro, 1995). To examine leadership of local

education funds, a subset of LEFs based in communities with a significant low-income

population and which have advocacy and reform as important components of their missions, an

independent standing from school systems and paid professional staff6 the Public Education

Network surveyed executive directors in 1987 and 2001.

Researchers have also leveraged the IRS 990 forms which every 501(c)3 nonprofit

organization with more than $25,000 in annual revenue must file every year, to gain further

insight about public school fundraising. Eric Brunner and Jon Sonstelie (1996) used 990s to

study LEFs, PTOs, PTAs, booster clubs, urban foundations and other school oriented local

foundations.

Schools solicit restricted and unrestricted monetary and in-kind donations directly or

indirectly through local educational foundations, booster clubs, parent-teacher associations

(PTAs) and parent-teacher organizations (PTOs). Parent-teacher associations are the most

prevalent organization, with more than 26,000 local chapters of the national PTA. PTAs tend to

become involved in state and national educational issues and provide services, while PTOs and

booster clubs focus almost solely on fundraising. Booster clubs generally target their revenue-

generating efforts towards a specific area, while PTOs tend to have a broader focus. Local

educational foundations are tax exempt, generally 501(c)3 nonprofit community organizations,

which raise funds to support a school, district or multiple districts. Districts also attract grants

6 Local education funds serve entire districts or several districts and tend to be much larger than other school foundations in terms of revenues, expenses, net assets and number of employees (Lampkin and Stern, 2003).

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from the federal and state governments, nonprofit organizations and private and corporate

foundations.

In Zimmer, Krop and Brewer (2003), private givers generally channeled their

contributions through an organization or individual associated with the school or district. The

principal was a channel for funds at all of the schools, and, in general, took leadership in

communicating and developing relationships with the community and private sponsors. Funds

frequently flowed through PTAs and local education foundations, and all districts had staff

responsible for private giving.

Enterprise activities include the levying of user fees, charging participants for school

provided programs or services such as driver-education programs, swimming lessons, athletics,

transportation to and from school and school supplies7. Wassmer and Fisher (2002) note that

school districts rely on user fees to a much lesser extent than state and local governments and

argue for their expansion8 to fund public education, particularly for “auxiliary services” -

schooling components with small or negligible positive externalities, such as transportation,

meals and after school childcare. Wassmer and Fisher find that user fees are more often levied in

areas with a lower percentage of school-age children, districts small in area, districts with high

enrollments and non-metropolitan or rural areas. Downing and McCarthy (1992) call for user

fees to fund 30-50% of the cost of public education.

7 Certain types of fees, including fees for transportation, extracurricular activities, field trips, athletics are illegal in many states. Many districts waive fees for low-income students, however the shame associated with seeking waivers erects a barrier to for low-income families (Dayton and McCarthy, 1992) 8 The equity considerations of this argument are somewhat complex. Increased reliance on user fees may curb participation in fee-associated activities by low-income students (regardless of whether the school offers to subsidize these fees or not) or impose additional hardship on low income families. However, the authors note that local property taxes also burden low-income property owners, and their alleviation through increased reliance on fees would somewhat lift that burden. Additionally, under the current system, low-income families pay for extracurricular activities and auxiliary services whether their children participate in them or not.

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Schools lease facilities or entire buildings to businesses, community organizations and

professional sports teams. Schools also freely provide space to public and nonprofit agencies in

exchange for social and other services to students, staff and local residents.

Districts sell services, such as food services and computer support to private nonprofit

organizations and private schools and transportation to public nonprofits and governmental

agencies. Schools sell products directly (Zimmer, Krop, Kaganoff, Ross and Brewer, 2001) and

also sell access to school markets, collecting revenue from advertising on school property or in

school publications and from selling concessions for services including student pictures,

beverage concessions, vending machine operations and ATM machines.

Schools partner with governmental agencies, using public buildings for instruction,

sharing the use and cost of recreational facilities, sharing transportation vehicles, and co-

managing local parks and recreation departments. Schools partner with local nonprofit

organizations, often exchanging space for services. Partnerships with colleges and universities

allow high school students to take classes at local institutions of higher learning, and allow local

schools to take advantage of collegiate interns and volunteers. School-business partnerships

provide for work-study and job placement programs, career guidance, skills training, tutoring,

privately funded curricula, remedial education, curriculum development, program evaluation,

targeted endowments, in-kind contributions of goods and services, extracurricular support and

guest speakers9. Some schools engage in scrip programs, in which businesses sell certificates for

popular goods to schools at a slightly discounted price and the schools resell the certificates at

9 Monk and Brent (1997) describe business’ benefits from a partnership with schools: access to students and the opportunity to tailor educational programs to best suit the business community. These partnerships are often directly related to a business’ line of work. An example of a particularly extensive school-business partnership is school adoption, where business employees donate money and/or volunteer service for training in teaching techniques, the use of athletic facilities and access to students for marketing research.

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full face value10 (Zimmer, Krop, Ross, Kaganoff and Brewer, 2001) or in which schools receive

a certain percentage of constituent purchases at participating retailers. The eScrip program

registers participating merchants and constituents electronically and automatically funnels a

given percentage of credit card, debit/ATM cards and grocery loyalty card purchases at

participating stores by participating consumers to the PTO or education foundation that they

have registered to support11.

Schools generate further revenue through investment (Monk and Brent, 1997). A short

window of time between the collection of property tax revenue and its expenditure creates an

opportunity for schools to create savings accounts and money market accounts, purchase

certificates of deposit, and invest in fixed income securities, generally United States Treasury

bills and government agency securities. Some schools have also pursued repurchase agreements,

arrangements between a school district and a local lending institution with a cash shortage

offering relatively high rates of return over a limited time period. In a repurchase agreement, the

school district agrees to buy Treasuries or agencies from the lending institution and sell the

bonds back at a later date. School cash management prioritizes loss prevention and liquidity

above yield, but even limited returns on investment can enhance a district’s revenue stream.

In Zimmer, Krop and Brewer (2003), the preeminent mechanisms for soliciting

contributions were personal contacts and relationship building, product sales, and special events.

Nearly every principal emphasized personal contacts and relationship building as crucial in

raising funds. Fewer schools used mail solicitations, grant applications and school-business

partnerships, but these were important components of the fundraising strategies of the schools

10 Participating businesses have sold products including groceries, clothes, meals, toys, flowers and airline tickets. 11 Raising $80 million for schools nationwide since its inception in 1999, the program is provided through the Electronic Scrip Incorporated corporation and is run through the www.escrip.com website. A 15% administration fee is deducted from total contributions in all states except Connecticut.

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that utilized them. Nearly all of the schools raised money through donations and more than half

through product sales. At the district level, personal contacts and relationship building was the

most prevalent strategy, with grant applications and school-business partnerships as next most

prevalent. Principals in districts which actively fundraised consistently reported that the share of

private resources they attract at the school level is greater than private resources they receive

from the districts. At neither level were endorsements or advertising revenue sought.

Donations to schools derive from a diversity of sources (Zimmer, Krop, Ross, Kaganoff

and Brewer, 2001). Parents, particularly higher income parents, volunteer at schools and/or make

monetary contributions. School alumni often contribute with particular generosity. Local

businesses, corporations, large philanthropic foundations, community-based organizations, and

institutions of higher education are also common benefactors of public schools.

The surrounding socioeconomic and community context alters the fundraising dynamic,

as the success of various strategies and the generosity of different types of donors vary by

community type. In Zimmer, Krop and Brewer (2003), parental support was strongest in the

communities on the highest end of the socioeconomic scale. Wealthier schools relied more on

parents for in-kind as well as monetary contributions, while poorer schools, which did not have

strong parent associations, received more in-kind donations from local businesses and

community organizations. Poorer districts reached out to more donors than wealthier districts,

and enjoyed greater access to corporations, philanthropic foundations and community-based

organizations than wealthier schools.

Wealthier schools, despite relying on relatively fewer donors, employed more

mechanisms to attract their support. Only wealthier schools participated in web based donation

programs and scrip programs and only the two wealthiest schools used mail and/or phone

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solicitations. Personal contacts and relationship building as well as a dynamic principal were

particularly important in middle and low income schools. Wealthier schools and districts

strongly focused on securing monetary donations, while poorer schools and districts focused

relatively more on in-kind contributions.

The inconstancy of fundraising across community types raises equity concerns. If

wealthier schools and districts can more successfully attract private resources than the poorer

ones, private fundraising threatens to undo state resource equalizing mechanisms. Thus far,

private fundraising has been sufficiently small in relative terms so as to not severely alter the

distributions of resources, but relative advantages in fundraising in wealthy communities pose

long-term concerns if a scholastic reliance on private funds continues to increase. In Zimmer,

Krop and Brewer (2003), wealthier districts attracted more monetary support than poorer

districts12, and, as district wealth increased, so did the number of programs, services and

materials purchased with private monetary donations. However, middle and lower income

schools attracted at least as much in-kind support as the wealthier schools and, even for the

wealthiest schools, private monetary contributions accounted for less than 5% of the school

budget.

Of the various public school fundraising strategies, one of the most intriguing, and

perhaps the most relevant to the Vermont context is the creation of local education foundations.

Nationwide, most local education foundations came in existence after 1989 (Merz and Frankel,

1995). Most were formed with specific goals in mind and a majority in Massachusetts (Medina,

1988), California13, Illinois, Oregon, and Washington were formed because of declining public

12 This was partly attributed to increased revenue generation through LEFs. 13 Brunner and Sonstelie (1996) found that school fundraising organizations in districts whose revenue generating capacity was most negatively impacted by reform generated the most revenue per pupil. However, because these

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school revenue. In Vermont, the passage of Act 60 in 1997 provided the impetus for the state’s

LEF boom.

The district administration most often initiated LEFs, but broad community support

tended to be crucial for their survival (Merz and Frankel, 1995). The Massachusetts study

recommended the expectation of one year of planning before a foundation becomes operational

and the involvement of school officials in the planning process. McCormick observed that LEFs

should return more revenue than their start-up and development costs after one and a half to two

years. Foundations tend to develop faster in cohesive communities as well as single, smaller

school districts as compared to large districts or multiple districts (McCormick, 2001).

The Public Education Network’s Leading Ways (Raphael and Anderson, 2002) study of

founding directors of local education funds14 sheds light on the initiation of foundations. While

Vermont LEFs are generally much simpler and smaller in scope than the local education funds

surveyed, the study merits inclusion from a best practices perspective, since local education

funds are generally older than other LEFs and generate much more revenue – by more than a

factor of 6 at the median level. The study describes founding directors as financially secure at the

fund’s launch, with many re-entering the workforce after raising children and many engaged in

community politics. Founders were perceived as reliable, trustworthy and civic-minded. Most

founders sought community input when determining the foundation’s objectives and activities

and endeavored to rally around existing issues to mobilize support for the fund. The creation of a

broadly representative steering committee also facilitates LEF formation (Muro, 1995).

districts are the high income districts in the sample, it is unclear how much of the fundraising success can be attributed to the limitations of public revenue. 14 Local education funds are a subset of LEFs based in communities with a significant low-income population and which have advocacy and reform as important components of their missions, an independent standing from school systems and paid professional staff. Local funds serve entire districts or multiple districts and tend to be much larger than other school foundations in terms of revenues, expenses, net assets and number of employees (Lampkin and Stern, 2003).

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Founders described the teacher mini-grant program (small curricular development grants

distributed to teachers through an application process) as a crucial initial effort to forging a good

relationship with the schools and districts. These mini-grants were also found to be the dominant

distributional mechanisms in the early years of the Massachusetts foundations.

Foundations serve a variety of purposes, generating resources for schools by soliciting

monetary and in-kind support and providing grant-writing assistance, improving communications

between the community and public schools by disseminating information and advocating on

behalf of the schools, training school staff, increasing parent involvement in schools, and

promoting school reform (Medina, 1988, Muro, 1995). In Merz and Frankel (1995), almost every

respondent cited the inadequacy of state education funding and believed that foundation money

was necessary to provide a basic education. The researchers also found that in about 25% of the

districts’ foundations self-reported raising public awareness of the schools, and many believed

that the foundation helped the school district pass local tax measures. Given the incentives of Act

60, an inverse relationship between local taxes and foundation activity would be expected in

Vermont.

Merz and Frankel found that most LEFs have between ten and twenty members on their

boards of directors. Controlling for the size of the district, foundations with larger boards raised

more money. The involvement of “a number of citizens” on the board was crucial for continuing

success. Parents involved in LEFs have been found to be generally white and non low-income

(Medina, 1988)15.

McCormick emphasizes talent and skill needs when composing the foundation board

including influence, sales and marketing skills, public relations skills, accounting/tax expertise,

15 Executive directors of local education funds, even in predominantly minority communities, also tend to be white and non low-income (Raphael and Anderson, 2001). The Massachusetts study includes among its recommendations encouraging the involvement of minority and low income parents and community members.

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legal expertise, community expertise, education expertise, technical expertise and fundraising

expertise. Financial resources and the ability to commit significant time and energy to the

foundation are also important. People with relations with local media, corporate executives,

certified public accountants, certified life insurance underwriters, advertising professionals,

attorneys, community leaders, financial experts, city officials, and bankers16 are often

particularly attractive candidates. Massachusetts LEF boards also contained university

representatives, state officials and students. Local religious leaders may also be included (Muro,

1995). Commonly, board members serve rotating, rather than perpetual, terms of office (Muro,

1995).

McCormick recommends beginning with smaller boards (17 to 18) for easier consensus

on a LEFs mission, goals and strategy and the relative ease of adding members as compared to

subtracting them. A rapid expansion, however, to 25-27 members spreads out the board’s

workload and increases resource-generating capacity. McCormick recommends board

subcommittees focusing on different fundraising mechanisms: annual support,

foundation/corporation grants, planned giving, selected (major) donors, alumni relations and

retired community as well as a public relations committee. This recommendation is echoed by

the Massachusetts study. Board members should be expected to contribute to the foundation

(McCormick, 2001, Muro, 1995) and this expectation should be communicated during their

recruitment (McCormick, 1995).

Power-sharing between foundations and school administrations can pose a daunting

governance challenge. Among Massachusetts LEFs, control over the disbursement of funds was

identified as a major source of tension between school officials and LEFs, despite general

16 Banks tend to contribute to charitable causes a higher percentage of their operating revenue than other corporate entities.

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consultation of the superintendent and school committee about foundation funding decisions.

Strategies to diffuse this tension included yielding complete control to the superintendent over

foundation grants, placing school representatives on LEF governing boards, placing school

personnel in formal or informal advisory roles in the disbursement of mini-grants, allowing the

school committee to review all mini-grant awards and requiring regular meetings between

foundation staff or board members and school administrators. The study recommended

governance mechanisms which involve school administrators, providing formal and informal

avenues of communication and collaboration on programming and policy decisions. McCormick

identifies an embedded model of LEF governance as the most successful, establishing the

foundation as an autonomous entity from the school district, but providing for a permanent

relationship for school board members and school administrators on the LEF board. He finds that

this structure avoids the potential problems of disconnect, lack of trust and coordination of an

entirely autonomous board and the liability concerns and the fundraising obstacles posed by

perception of the LEF as an agency of the school board that derive from a LEF entirely

controlled by the school administration. He recommends that less than 50% of board members be

school administrators or board members. Merz and Frankel found that foundation boards tended

to determine allocations to the schools, although school officials frequently had input into the

process in either a voting or an advisory capacity. Muro additionally recommends housing the

LEF in a chamber of commerce, corporation or non-profits as opposed to on school property to

provide physical distance from school administration.

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Few foundations pay employees (Merz and Frankel, 1995), but those with paid

fundraisers and/or paid staff raise significantly more revenue17 (Merz and Frankel, 1995, Muro,

1995) more quickly (Muro, 1995) than LEFs entirely reliant on volunteers. Most staff positions

were found to be part-time and very low paying. In addition to staff support18, a paid executive

director facilitates fundraising (McCormick, 2001, Muro, 1995), because of the director’s broad

responsibilities - brokering relations, implementing programs, raising organizational visibility -

and the dramatic increase in volunteer time necessary to compensate for the absence of a paid

director (Muro, 1995)19.

Successful collaboration with other school fundraising organizations importantly

facilitates the successful operation of a LEF. To diminish conflict between other school

fundraising organizations, booster club, PTA and PTO leaders have been included as LEF board

members, have been asked to serve as formal or informal advisors to the foundation, and have

been included in LEF activities, such as fundraising and mini-grant programs. LEFs have also

invited these organizations to serve as joint sponsors for LEF activities and projects (Medina,

1988). Contributions to these other organizations can be channeled through a tax-deductible

dedicated or donor-advised fund in the LEF (McCormick, 2001). Coordination with alumni

associations, particularly important to avoiding conflict and overlap, can often be accomplished

by an ex-officio position on the LEF board for the alumni association president. Other strategies

for collaboration include foundations asking businesses for a larger contribution to be distributed

in part to booster clubs, awarding donor businesses stickers to post in their windows noting their

17 Merz and Frankel acknowledge that LEFs which employ paid fundraisers and staff tend to be larger and older. At least some of their increased revenue generating potential, then, is likely explained by reach and experience as opposed to hired staff. 18 In the first two years, generally 6 hours a week of secretarial support is sufficient for foundations, which in many cases, can be provided by the school district (McCormick). 19 It is not recommended that the school district superintendent be selected as executive director (Muro).

20

contribution to the LEF, and co-hosting special events with other fundraising organizations20.

Close relations with other community fundraising organizations, including universities, hospitals

and service clubs can also be beneficial (McCormick, 2001).

Donors contribute to LEFs out of a belief in the importance of education for the

economic viability of the community, a belief in the educational mission of the schools, or

business ties with the schools (McCormick, 2001). Donors include parents, alumni21, civic

organizations, churches and clubs which use school facilities, local philanthropists, local

businesses and corporations22 (particularly those who have traditionally been generous to the

community), school board members, teachers and staff23, and school districts24 (McCormick,

2001, Muro, 1995).

Volunteers and past donors constitute two particularly rich donation sources. Promoting

foundation involvement crucially increases a LEF’s volunteer base (McCormick, 2001, Muro,

1995). Non-trustee committees and special event leadership positions offer opportunities to

increase a volunteer’s stake and involvement in the foundation (McCormick, 2001). The

Massachusetts study similarly recommends governance mechanisms such as subcommittees, task

forces and advisory committees25 to more closely connect interested person with the foundation.

With past donors as the most likely group to make future contributions, acknowledgement

(especially visible acknowledgement) of gifts (McCormick, 2001, Muro, 1995) and monitoring 20 Events can also be co-hosted with other districts to exploit athletic and scholastic rivalries. 21 In communities where post-secondary education attendance and completion rates are not high, alumni may be particularly promising donors, because attending local schools was their last formal educational experience (Muro, 1995). Wealth effects of lower levels of education may limit their giving potential. 22 For a business or corporation to have a significant interest in the local community, conducting substantial business in that community even in the absence of a local physical plant may be sufficient. 23 Merz and Frankel note that soliciting contributions from staff and faculty is common practice, but warn that this must be done carefully to avoid ill will derived from staff and faculty expectations of being solely beneficiaries and not benefactors of the foundation. 24 School districts frequently provide in-kind assistance, such as office space, telephones, postage and clerical assistance (Merz and Frankel). 25 Muro cautions against creating advisory boards just to facilitate greater foundation involvement (soliciting counsel from busy stakeholders and ignoring what they have to say).

21

of existing donors (Muro, 1995) are important. It is ten times more costly to acquire a new donor

than it is to successfully solicit a repeat gift (Muro, 1995). Fostering the formal organization of

groups of donors as a general benefactor club or a specialized group supporting one facet of

schools can also be effective (Muro, 1995).

Merz and Frankel found that virtually all foundations raised funds through special events.

Special events increase community awareness of a foundation, may attract new volunteers and

often generate a significant amount of unrestricted revenue, but are labor intensive (McCormick,

2001, Muro, 1995).

Foundations which directly solicited contributions raise on average more than one and a

half times as much as those which did not (Merz and Frankel, 1995). Merz and Frankel found

that most foundations used mail or phone solicitations and ask for annual contributions. Direct

mail solicitation is costly, but builds a donor list (McCormick, 2001). More than 70% of U.S.

non-profits employ direct mail as their primary approach (Muro, 1995). Successful direct mail

campaigns are audience-driven, personalized, creative and subject to extensive and ongoing

testing (Huntsinger, 1989). Telemarketing is more successful for better known foundations,

generally requiring many volunteers and yielding small donations (McCormick, 2001).

Phone/mail campaigns combine phone solicitation with direct by the use of individually

addressed letters informing a prospective donor of an upcoming solicitation phone call (Muro,

1995).

Annual drives are the primary sources of support for most LEFs (Muro, 1995) and were

found to be particularly important for foundations with a fairly long record of success (Merz and

Frankel, 1995). In addition to raising funds, annual drives also promote the training and

development of volunteers, raise public awareness and recruit eventual major donors (Muro).

22

Personal solicitation, especially of major donors, poses almost no upfront cost and can generate a

large amount of revenue (McCormick). Muro notes a steep plunge from the 70% success rate in

the United States for personal solicitations to the 25% success rate for phone solicitations to the

2% success rate for mail solicitations. Cost effective, planned giving is particularly useful in

building endowments, has the potential to attract contributions larger than those that would have

been contributed through a standard donation process and uncovers new donor prospects (Muro,

1995). Tribute gifts may be made in honor of teachers26, alumni or anyone impacting teachers

and students. Teacher tributes tend to be more effective when students are younger. Individual

donations may involve cash, real estate, securities, life insurance, goods and services.

Corporate grantwriting and capital campaigns tend to be more successful for more

established foundations, because corporations are more generous to established foundations and

the major donors required for a successful capital campaign are cultivated over time

(McCormick, 2001). LEFs also broker partnerships between local schools and local business and

corporations (Muro, 1995), and participate in low-cost Scrip programs (McCormick, 2001).

Other strategies include applying for charitable grants and collecting membership dues

(Medina, 1988) as well as, and memorial giving. Muro recommends soliciting specific types of

gifts, including class gifts,27 reunion gifts28, challenge gifts, where donors offer to match or

exceed contributions from other sources, and leaderships gifts, donated to launch a fundraising

campaign. Creating several funds (i.e. college scholarship fund, technology fund) offers donors

options as to the destination of their contributions.

26 Ethical concerns preclude revealing to teachers the amount of their tribute gifts. 27 This strategy segments the donor pool and draws on class loyalty (Muro, 1995). 28 Muro notes that the expected value of these gifts should increase over time i.e. expectations for a 25th year reunion gift should be larger than for a 10th year reunion gift.

23

McCormick recommends an “inside-out top down” approach to raising funds, focusing

on donors with the closest relationship to the schools and the largest giving capacities. Muro

endorses a broader concept of development which encompasses fostering a positive climate for

resource generation and establishing ties with individuals and organizations in addition to solely

fundraising.

Effective communication is an important element of LEF fundraising (Muro, 1995,

McCormick, 2001). Merz and Frankel found foundation publicity efforts ranging from

anonymity to the distribution of community newsletters, placing banners around the community,

and making school book covers. Muro emphasizes a marketing of education excellence as

opposed to a deficit-based appeal, and the solicitation of an investment rather than a charitable

contribution. A foundation should clearly communicate its mission, successes, future plans and

priorities, while making a case for contributing to the local schools (Muro, 1995).

Merz and Frankel found that most foundations have very little overhead cost and return a

very large percentage of funds raised immediately to the schools. For most LEFs, reporting how

last year’s donations were used was a crucial component of fundraising efforts. Only large, well-

established foundations had paid staff, and these do not earn much. Only a few foundations had

attempted to build endowments, but had not, at the time of the survey, had particular success.

A majority reported a consistent increase in revenue over the foundation’s lifespan (Merz

and Frankel, 1995). Describing a maturation process potentially applicable to Vermont LEFs,

PEN’s Leading Ways study identified four developmental stages for local education funds: start-

up, growth, establishment and full institutionalization, with leadership roles, resource capacity,

staffing and programming varying at different stages. In the Merz and Frankel study, foundations

24

originating prior to 1990 raised more than four times the amount of foundations originating after

1990 and nearly six times the amount of foundations originating after 1991.

Elementary and K-12 foundations raised more than high school foundations (averages of

$41,267 and $37,817 as compared to an average of $12,950)29. Brunner and Sonstelie (1996)

discovered that California education fundraising organizations associated with elementary

schools raised more revenue per pupil than organizations associated with other grade levels.

Brunner and Sonstelie (2003) find that parental philanthropic collaboration deteriorates, albeit

slowly, as schools increase in size. McCormick noted that funds are generally more successfully

raised at the district level because of the ability to directly involve the superintendent and school

board and more successfully attract grants from foundations and corporations, but individual

school foundations may be more successful at generating alumni contributions.

The community context exerts a similar formative effect on LEF fundraising as on other

school and district revenue generation efforts. Distinct differences arise between foundations in

smaller communities and those in urban areas (Medina, 1988, Addonizio, 1998). Of

Massachusetts LEFs which awarded membership, parents and community members dominated

membership in small communities, while businesses, community organizations and universities

dominated membership in urban areas. In Massachusetts, direct solicitation of local businesses

was found to be the most successful fundraising strategy in large, urban areas, while direct mail

solicitation of parents and community members was found most successful in small

communities30. Smaller community LEFs tended to be governed more informally, while LEFs in

large urban areas tended to have large boards of directors and advisory committees. While

29 Personal interviews attributed this to lesser parent involvement in the education of high school children, a shorter time frame (4 years) for developing a relationship with parents and parental focus on saving for college. 30 Medina et. al., advised small community LEFs to replicate the successful involvement by urban LEFs of universities, businesses, cultural institutions and social service organizations, and raising of funds from businesses, especially banks and insurance companies.

25

parents powerfully impacted LEF operations in small communities, businesses had far greater

influence and parents far less in large urban areas. Large urban LEFs were more likely to

produce publications.

If there are economies of scale in managing a foundation, LEFs in smaller towns are

relatively more expensive to run. In the Massachusetts survey, only LEFs in large urban areas

used professional paid staff, and in Michigan, urban foundations raised more than three times the

amount of gross revenue as suburban foundations. Michigan suburban foundations raised about

75% more gross revenue than rural foundations. This may be offset however, by a relative

decline in the ease of free-riding. In Vermont, no town larger than 4500 in population had a

successful foundation (Fischel).

LEFs have generally enjoyed greater success in wealthier communities, but have not have

raised sufficient revenue to significantly affect a state-mandated equitable distribution of

revenue. Merz and Frankel discovered foundations to be more successful, but not more

prevalent, in high-income communities, and noted that successful LEFs were formed in

communities of all income levels. Addonizio found Michigan foundations to be more prevalent

in high income communities, as well as in predominantly white communities, larger school

districts, and school districts with more unrestricted revenue per pupil and higher levels of

scholastic achievement. Urban foundations in Michigan were less prevalent. In progressively

wealthier districts in California, there were more non-profits reporting more than $25,000 in

revenues, and a better chance that an LEF would be found in the district (Brunner and Sonstelie,

1996). These progressively wealthier districts also raised increasing amounts of revenue per

pupil. In Zimmer, Krop, and Brewer, (2003) LEFs were found at districts of all income levels,

but were particularly active in the two wealthiest districts.

26

Other community factors have also impacted foundation success. The presence of a high

number of professionals, highly correlated to overall wealth, almost doubled fundraising levels

(Merz and Frankel, 1995). Modeling school expenditures as a function of the marginal tax price,

district median income, enrollment, percentage of students receiving free or reduced price lunch

and a dummy variable indicating a preference for higher levels of education expenditure,

Addonizio found that foundation district residents have a revealed preference for higher public

spending on education. A large corporate base, a large base of independent, individual wealth, a

high percentage of professionals, finite borders and well-attended class reunions additionally

facilitate a foundation’s success (McCormick, 2001). Foundations established in conjunction

with significant construction have also tended to have high rates of success.

Surprisingly, the existence of many retired people or many families without children in

foundation districts did not affect fundraising success (Merz and Frankel). This result was

confirmed in Vermont, where nonresidential property owners were the least likely to free ride

and owners of vacation homes in ski resorts contributed reliably (Fischel, 2003). In Killington,

where most of the condominiums are managed by full time employees, the managers collectively

convinced the owners’ associations to bill members for LEF contributions. Because of the tax

rebates awarded to families with less than $75,000 in adjusted gross income, successfully

organizing a foundation was more difficult for property-rich towns with many low-income

families (Fischel, 2003).

Addonizio (1998) concluded that LEFs did not “measurably negate” Michigan’s

education equalization efforts. Analyzing private contribution levels by district type and linking

contribution levels to school inputs and resource levels in California, Brunner and Imazeki

(2003) concluded that more than 99% of elementary school students attended schools where

27

private contributions “have almost no effect on inputs.” A few schools, small in number and

generally small in size, raised large amounts of private funds (more than $500 per pupil) which

were used to purchase more computers and teacher aides and reduce class-sizes and teacher-

pupil ratios, but these were isolated instances and appeared to offset lower levels of government

revenue that the higher income districts receive31. Merz and Frankel also found LEF funds to be

decidedly supplemental, with foundations raising an average of 0.3% of the district budget.

Despite the wide range of educational resources made possible through the raising of

private funds, it can be argued that an uncertain link between educational inputs and outcomes

calls into question the relevance of private revenue generation in public schools. Despite

continual increases in educational spending and improvements in educational technology,

Scholastic Achievement Test scores fell between 1966 and 1993 and National Assessment of

Educational Progress scores between 1970 and 1990 failed to show improvement. Many scholars

argue that monetary and other resources make no difference in terms of either scholastic

performance or labor market outcomes. Hanushek’s analysis (1986) of 377 studies on the link

between educational resources and outcomes further supports his null hypothesis. Betts (1995)

finds that labor market payoff enhancement dissipates after 1960.

A substantial body of research also supports the argument that educational inputs do

matter. Prominent research in this camp includes Hedges and Greenwald’s (1994) successful

linkage of resources and educational performance through formal meta-analysis on

comprehensive universes of input/outcome studies and Card and Krueger’s (1992) findings that

additional resources increase educational attainment and augment the labor market payoff to

education.

31 The authors add that equalizing educational inputs may have a disequalizing effect on outcomes, because low-income students may need more educational inputs to achieve similar performance as their wealthier counterparts.

28

With a thorough formal analysis of the impact of educational resources and outcomes

beyond the scope of this paper, it suffices to say that there is a vibrant scholarly debate on

whether resources generally do matter. There is some consensus that properly targeted resources

improve education. However, what is most relevant for the Vermont context is that people

believe that resources matter, and this belief impelled intensive private fundraising to raise

revenue for schools above the basic state foundation level.

29

Data

The quantitative data analyzed in this research are comprised of financial data on

Vermont non-profit organizations acquired through the purchase of data sets from the Urban

Institute’s National Center for Charitable Statistics (NCCS), school district data available

through the Common Core of Data maintained by the National Center for Education Statistics,

town level school expenditure and administrative data obtained from the Vermont School

Report, publications of the Vermont Tax Department, and the Vermont Department of

Education, town level property wealth, income and demographic data from the Vermont Online

Indicators database maintained by the Center for Rural Studies at the University of Vermont,

migration and age demographic data from the Census, and data on revenue generating activities

of Vermont School Business Officials collected via web-based survey.

The NCCS data contain detailed financial information gleaned from the form 990 that all

501(c)3 non-profit organizations generating more than $25,000 in annual revenue must file with

the Internal Revenue Service. Included among these organizations are the local education

foundations, parent teacher organizations and booster clubs which this thesis substantially

investigates. Nineteen NCCS data sets of three different types were acquired. Core data sets for

Vermont public charities for the years 1995-2002 contain data on from 73 (1995) to 124 (2002)

variables from the Form 990 including classification and identifying information, revenue,

expenses and net income, balance sheet and net assets information, and organizational activities.

Core data sets for Vermont private foundations for the years 1995-2002 contain data on from 60

(1995) to 154 (2002) variables from the Form 990 including classification and identifying

information, revenue, expenses and net income, balance sheet and net assets information, and

foundation activities. Digitized data sets include 178 variables from the functional expenses

30

portion of the 990 form, 121 variables from the balance sheet portion of the 990 form and 52

variables from the other information portion of the 990 form for public charities for the years

1998-2001.

The subset of nonprofits wholly or substantially supporting local public schools was

drawn in an iterative manner. Searches were performed on Guidestar, an online national database

of nonprofits. For public charities, searches were performed for Vermont nonprofits for the

following key words in field “non-profit name”: “school and foundation,” “education and

foundation,” “education and fund,” “school and fund,” “educational and foundation,” “friends

of,” “parent and teacher,” “parents and teacher,” and “PTO.” Keyword searches for “school” and

“public education” were also performed.

The general National Taxonomy of Exempt Entities major group codes for education (B)

and Philanthropy, Voluntarism and Grantmaking Foundations as well as the NTEE core codes of

each organization in the subset were searched in the Core 2002, Core 2000, Core 1998 and Core

1996 data sets. Since the core data sets contained the most recent return year for each

organization that filed in the last three calendar years and covered one of the last three fiscal

years (the core 2001 data sets cover fiscal years 1999 through 2002, for example), this search

encompassed all organizations filing within the time period.

For the private foundations, a search was performed for all Vermont private operating

and non-operating foundations in the category of education and as well as for public, society

benefit. Name and keyword searches for “school” and “education” were also performed. In Stata,

the NTEE major group codes for education (B) and Philanthropy, Voluntarism, and Grantmaking

Foundations (T), as well as the Z99 NTEECC code (unknown) were searched in the Core 2002,

Core 2000, Core 1998 and Core 1996 data sets.

31

The profiles of the organizations were in most cases sufficient to determine the primary

purpose of the organization, but when profiles did not contain sufficient information, PDF

versions of the 990 forms were examined32 and Google searches were used to find out more

about the organizations. Those organizations with a primary purpose of providing support to

local public schools were included in the local education foundation subset. These organizations

were then compared against a spreadsheet of private contributions reported from each town in

Vermont compiled by the Vermont Department of Education. In cases where towns reported

receiving significant (greater than $25,000, and, in some cases, greater than $10,000) private

donations and the public contribution figures from education foundations located in the town did

not closely match, or no local education foundations with the same zip code as the town were

represented in the local education foundation subset, city and county searches were then

performed to identify any organizations missed by the initial search.

The Common Core data provide information on school district revenues and expenses,

including data on tax revenue and non-tax income sources such as user fees, capital gains,

tuition, and miscellaneous alternative revenue as well as enrollment figures, per pupil expenses

and test score information. The Vermont Indicators Online database, Vermont Education Report,

Vermont Department of Taxes publications, and Vermont Department of Education data provide

measures of school district demographics and administrative expenses, including property

wealth, income, school inputs, and salaries.

Migration and age demographic data from the 2000 Census decomposed by school

district were obtained from the National Center for Education Statistics. From the migration data,

a simple population stability variable was calculated, providing the ratio of district residents who

had lived in either the same house or the same town for at least five years. From the age 32 For the private foundations, the “direct charitable activities” section was particularly useful in this regard.

32

demographic data, variables were created calculating the percentage of school district’s

population under the age of 19 and above the age of 64.

There are several important limitations to the data. The affiliated organizations data

exclude organizations raising less than $25,000 in annual revenue. Some organizations raising

more than $25,000 in annual revenue are also excluded from the analysis either due to a lack of

enrollment information for the school district (Stratton Winhall Education Foundation, Pittsfield

Education Foundation, Craftsbury School Corporation) or due to their absence in the underlying

data (the North Hero Education Fund). Additionally, the $14 million in Freeman Foundations

contributions represents a potentially significant confounding factor. As Freeman grants were

awarded to virtually every major local education foundation, with most receiving matching

grants, their effect is mostly one of magnitude, not distribution.

The membership figures from the Common Core of Data do not include students that are

tuitioned out of a district to attend public school in a neighboring district. These data provide an

accurate picture of the number of students educated in a district in a given year, but may not

accurately indicate the number of pupils for whose schooling a district is required to pay. For the

affiliated organizations, models are additionally run using the average daily membership figures

from the Vermont Department of Education for the years 2000-2003. Average daily membership

figures for 1999 were interpolated from the fiscal year 2000 data using the percentage change

from 1999 to 2000 in CCD membership figures.

The survey of Vermont school business officials asks a detailed series about private

resource generation at the school district level. The survey aims to cover to the greatest extent

possible a complete menu of private resource generating activities with the goal of identifying

which activities occur at the school district level, generating estimates of the amount of revenue

33

in excess of expenses and, in some cases, in kind contributions received from these activities and

detecting changes in these activities and their success at raising private resources during the Act

60 period. Questions cover affiliated fundraising organizations, user fees, grantwriting,

advertising, direct private monetary donations, adult education classes, day care services,

summer school programs, summer camp programs, rental/provision of school space, selling of

school services, concessions, scrip programs, partnerships with businesses and universities and

the arbitraging of funds not immediately spent.

Existing surveys of schools and school districts, conversations with superintendents and

school business officials in Massachusetts and Vermont and the private resource generation in

public schools literature informed the development of the survey. Several questions were adapted

from the published interview protocols of Zimmer, Krop and Brewer (2003). In Vermont,

interviews were conducted with Jeff Francis, executive director of the Vermont Superintendent’s

Associations, Brenda Fleming, president of the Vermont Association of School Business

Officials, Dave Wolk, a former principal of Barstow Memorial School and Rutland High School,

former superintendent of schools in Rutland City, former Vermont Commissioner of Education

and the Governor’s chief of policy during the writing and passage of Act 60. In Massachusetts,

interviews were conducted with John Crafton, the executive director of the Massachusetts

Association of School Business Officials, James Marini, superintendent of the Winchester Public

Schools, James Conry, chief financial officer of the Cambridge Public School District, Marie

Ferrari, assistant superintendent of finance and administration for the Somerville Public Schools,

and Tom Birmingham, former president of the Massachusetts State Senate and chair of the Joint

Committee on Education during the passage of the Education Reform Act.

34

The survey was programmed on-line using Websurveyor, with a split battery of questions

for districts impacted by Act 60 and for those unaffected. The survey was distributed via email to

all Vermont supervisory union school business officials by Ms. Fleming. Follow-up letters were

mailed to the 55 supervisory union business managers who did not respond to the initial survey.

Conversations with Professor Kent Portney in Political Science and Dr. Dawn Terkla in

Institutional Research as well as Don Dillman’s Mail and internet surveys: the tailored design

method (2000) informed the technical construction and question formation of the survey.

Professor Portney reviewed the survey for quality of question formation, and the survey was

piloted before dissemination.

Qualitative information on the operation, governance, fundraising activities, funding

streams, communication strategies and external relations of the Vermont local education

foundations was obtained through several phone interviews and email questionnaires. Existing

surveys of local education foundations, conversations with local education foundation executive

directors and board members, teachers and state education officials in Vermont, Massachusetts

and California and the local education foundation literature informed the development of key

questions. Model surveys included those of Merz and Frankel (1995), Medina (1988) and the

National Center for Public and Private School Foundations School Foundations survey.

Interviews were conducted with Bill Talbott, Chief Financial Officer at the Vermont Department

of Education, Dave Wolk, Paula Goodwin33, former president of the Educational Foundation of

Orinda, California, David Weiner, executive director of the Boston Latin Schools Foundation34,

Judy Freeman, a teacher at Boston Latin School, Skye Kramer, executive director of the

Brookline Foundation, Clare McCully, executive director of the Newton Schools Foundation and

33 Ms. Goodwin was featured on the Merrow Report on PBS for her work with the foundation. 34 The Boston Latin Schools Foundation generates the largest amount of annual private revenue for a single public school in the United States.

35

of the Massachusetts Education Foundations Association, and Susan Sweeney, executive director

of the California Consortium of Education Foundations. Contact information for foundation

leaders was obtained through a targeted mailing to 36 school board chairpersons with local

education foundations in their districts. Phone conversations were held with former leaders of the

Ludlow Education Assistance Fund, the Landgrove Education Foundation and the North Hero

Education Foundation, with detailed written correspondence from the Stratton Winhall

Education Foundation, the Andover School Assistance Plan, and the Milton Education

Foundation.

The school district data reveals several trends (see table 1). Student enrollment in

Vermont declined for each year of the data set. School achievement, as measured by fourth grade

test scores, is on the rise, and the pupil teacher ratio is consistently declining. Total school

district revenue consistently increases as do per pupil expenses and teacher salaries. The steady

increases in nominal dollar revenue as shown in the table are sufficiently large to translate in

yearly constant dollar increases, the most significant of which occur between the years 2000 and

2003. Most relevant to this study, alternative revenue, composed of interest earnings, revenue

from other school districts, revenue from other sales and services, tuition and fees, transportation

fees, revenue from book sales, activity fees, federal math, science and professional development

grants, and miscellaneous revenues, increases constantly, registering a dramatic increase in 2000

and 2001. The mean per pupil level of alternative revenue increases more than 25% between

fiscal years 1999 and 2001.

The bulk of this increase is derived from two sources: miscellaneous revenues and

revenues from other school districts. Miscellaneous revenues, a catch-all alternative revenue

category including contributions from the local education foundations, increased the most

36

dramatically. After an unexplained decline to about $75 per pupil in fiscal year 1999,

miscellaneous revenues essentially quadrupled by fiscal year 2001. Revenues from other school

districts, comprised most significantly of tuition monies received to educate students from other

towns, jumps from $1270.27 per pupil in fiscal year 2000 to $1442.91 in fiscal year 2001 and

$1634.82 by fiscal year 2003.

Further trends present themselves in the affiliated fundraising organizations data (see

table 2). The number of affiliated organization dramatically increases from 9 in fiscal year 1998

to 28 in fiscal year 1999, the first year of Act 60’s implementation. By the full implementation of

Act 60, the number of organizations had increased to 40. Beginning in 1998, the organizations

generated much greater per pupil contribution levels and much more total revenue. Mean real per

pupil contributions increased from $10.32 in 1997 to $445.12 in 1998 to $1,786.02 in 1999.

After 1999, contributions decline and flatten out at between $1,200 and $1,400 per pupil35.

Beginning in 1998, contributions account for virtually all revenues received by the organizations.

Solicitation expenses and compensation do not significantly detract from overall

affiliated organizations revenue, with solicitation expenses never exceeding 1.5% of mean total

revenue and total compensation never exceeding 2% of mean total revenue. No organization

reported any rental expenses and $100 in lobbying expenses by the Edmunds Elementary School

Parent Teacher Organization was the only reported political expenditure. This largely

corroborates Merz and Frankel (1995) in that, for local education foundations, there is very little

leakage of revenue targeted for schools.

35 The decline of contributions in 2003 is attributable, at least in part, to the cessation of Freeman Foundation grants. Data from the Vermont Department of Education indicate that these foundation contributions virtually disappeared after the passage of Act 68. Donated dollars reported by the schools dropped from more than $11million in fiscal year 2004 to less than $80,000 in fiscal year 2005.

37

Beginning in 1998, solicitation expenses and their component parts indicate no particular

trend over time. The presence of paid staff hovers between 21% and 29% between 1998 and

2003, with the exception of 1999 where this falls to 7.7%. The concurrence of a decline in paid

staff and the initial filings of a majority of the Act 60 local education foundations indicates a lag

between organization start-up and hiring staff. Other compensation increases dramatically from

1999 to 2003, rising in real dollars per pupil every year, with a particularly large increase

between 2000 and 2001 and between 2002 and 2003.

For the survey, responses were received from 17 of Vermont’s 61 supervisory union

districts, as the survey achieved 27.9% coverage. The responding supervisory unions appear to

be a largely representative sample (see Table 3). District enrollment levels, poverty and income

measures closely approximate statewide means. The responding districts do differ in per pupil

current expenditures, spending $689 less per pupil than the statewide average.

On average, there were 4.1 PTAs/PTOs and 1.6 booster clubs per district, and about one

LEF per every two districts (see table 4). There were attempts to start LEFs in four supervisory

unions during Act 60, and at least one LEF was started successfully in three of those supervisory

unions. LEFs were not successfully founded in supervisory union which attempted to found

them.

Twelve out of the 17 supervisory unions received competitive grants last year, with a

mean receipt of $489,151 (see table 5). Five supervisory unions reported grantwriting salary

expenses, with a mean expense of $7,154. These five districts received an average more than

four times the grant revenue of grant receiving districts that did not pay grantwriters.

38

More than 75% of supervisory unions reported collecting at least some type of user fee

from students (see table 6). Fees for athletics were the most common, while fees for after-school

programs were the most lucrative. No district collected fees for school supplies.

Nine supervisory unions in the sample independently operated vending machines, five

sold services to other organizations, five sold concessions to exclusive rights to vendors and

three permitted advertising (see table 7). Fifteen districts operated summer school programs,

thirteen provided school space to other organizations, nine operated summer camp programs,

seven operated adult education programs and four operated day care services. However, only

three districts reported generating more than nominal revenue from any of these strategies. This

revenue was quantitatively large in only one case - that of Chittenden South Supervisory Union –

which generated $155,000 last year through a facilities rental program.

Thirty-one percent of supervisory unions had partnerships with businesses and about 35%

had forged partnerships with institutions of higher education (see table 8). Benefits of these

partnership included early education and intervention for at-risk future students, employment and

vocational training, professional development, mentoring, and access to college classes.

Nine of the 17 supervisory unions were permitted to arbitrage funds not immediately

spent, and all that were permitted took advantage of the opportunity (see table 9). Certificates of

deposits, repurchase agreements and money market accounts were the most popular vehicles for

investment. Most districts that invested funds not immediately spent generated between $15,000

and $25,000 in investment revenue last year.

Three of the 17 supervisory unions indicated they had been significantly impacted by Act

60. None of these districts indicated increased efforts during the Act 60 time period to generate

alternative revenue through any of the above means.

39

Methods

Fixed effects regressions performed on two longitudinal data sets examine the sensitivity

of school districts to policy changes creating incentives for the generation of non-tax revenue and

the efficacy of the response of the non-profit organizations raising non-tax revenue for the

schools. Tracking changes in values of a given variable across time for individual observations,

the fixed effects model allows for robust estimation of parameters when observed independent

variables are correlated with unobserved factors constant over time (Wooldridge, 2003).

The fixed effects regressions use a fixed effects transformation of the independent and

dependent variables (Wooldridge, 2003). This transformation is most simply illustrated in a

model with a single explanatory variable. Let yit = β1xit + ai + uit, t= 1,2,…,T where yit is the

independent variable for observation i and time period t, ai is the fixed effect capturing all

unobserved time constant factors that effect yit, and uit is the idiosyncratic error representing time

varying unobserved factors that effect yit. The equation is averaged over time for each

observation i, producing

iii1i u a x y ++= β . Subtraction of the averaged equation from the initial equation produces

ÿit - iy = β1(xi - itx ) + ui - itu , t = 1, 2,…,T or ÿit = β1 x&& it + u&& it, t = 1, 2,…,T where ÿit = yit – iy

is the time-demeaned data on y, itx&& is the time demeaned data on x and itu&& is the time demeaned

data on u. The pooled OLS estimator β1 is the fixed effects estimator. The demeaning process

removes the fixed effect ai, leaving only the time varying unobserved factors uit in the equation.

The estimators of the fixed effects regression are unbiased as long as uit remains

uncorrelated with the explanatory variables. While accounting for time constant unobserved

factors, the fixed effects model generally sacrifices variation in the independent and dependent

variables relative to standard ordinary least squares analysis by measuring changes within rather

40

than among observations. In this case, the eight years of longitudinal data for the school district

regressions and nine years of longitudinal data for the affiliated organization regressions create

sufficient variation to make the trade-off worthwhile.

For the school district regressions, the fixed effects model takes the form:

ÿit = β0 + α1xit1 +…+ αkxitn + β1xit-1n+1 +…+ βkxit-1k + δ1act60 + δ2incentact60i + δ3interact1i

+…+ δkinteractki + δk+1dk+1 +…+ δndn + uit, t = 1, 2,…,8

ÿit is the time demeaned log of various forms of real non-tax per pupil revenue raised by school

district i in year t. xit1,…, xitn are the time-demeaned value of a series of contemporaneous school

district characteristics including income, poverty and enrollment levels. xit-1n+1,…, xit-1k are the

time demeaned values of a series of school district quality and performance characteristics

lagged one year including per pupil expenditures, 4th grade test scores and pupil teacher ratios.

Act60 is a dummy variable indicating the presence of Act 60’s alternative revenue generation

incentives. It is equal to one for the fiscal years 1999-2003 and equal to zero otherwise.

Incentact60i is an interaction term between the Act 60 dummy and a measure of per pupil

equalized school district property wealth in 1999. Interact1i,…, interactki are a series of

interaction terms between the Act 60 dummy variable and 2000 census demographic variables

Dk+1,…,Dn are dummy variables for all years except 1995, 1996 and 2003, which were dropped

to avoid issues of perfect multicollinearity. All financial variables as well as school district

enrollment, calculated using the membership variable in the Common Core data are calculated in

logs.

41

The simplest model explores the impact of Act 60, regressing the log of real per pupil

alternative revenue generated in a district on year dummies and the incentact60 interaction term,

taking the form:

Lipaltrevit = β0 + δ1year97 + δ2year98 + δ3year00 + δ4year01+ δ5year02+ δ6incentact60i + uit, t =

1, 2,…,8

More complete models add contemporaneous and lagged school district characteristics,

namely the poverty rate, the log of real income from households filing joint returns, the log of

district membership, lagged real per pupil current expenditures, lagged proficiency rates on 4th

grade math tests, lagged pupil teacher ratios, as well interaction terms between the percentage of

the population over the age of 64, the percentage of the population under the age of 19 and the

percentage of the population who had lived in the same town five years prior and the act 60

dummy. The most complex regression of this type takes the form:

Lipaltrevit = β0 + δ1year97 + δ2year98 + δ3year00 + δ4year01+ δ5year02+ δ6incentact60i + ppovit

+ limedjhhit + lmemberit + lagippcurexpit + lagm4cit + lagpupteachit + act60over64i +

act60under19i + act60popstabi +uit, t = 1, 2,…,8

Parallel regressions are run with the log of real per pupil miscellaneous revenue (miscrev)

and the log of real per pupil revenue from other school districts (lipothschrv) as dependent

variables.

The affiliated organizations regressions take the form

ÿit = β0 + α1xit1 +…+ αkxitn + β1xit-1n+1 +…+ βkxit-1k + θ1fit1 +…+ θkfitk+ θk+1exec + θk+2fitk+2 +…+

θnfitn + δ1act60 + δ2(incentact60i) + δ3d3 +…+ δkdk + uit, t = 1, 2,…,T

ÿit is the time demeaned real per pupil contributions received by the organization. xit1,…, xitn are

the time demeaned values of a series of contemporaneous school district characteristics including

42

income, poverty and enrollment levels. xit-1n+1,…, xit-1k are the time demeaned values of a series

of school district quality and performance characteristics lagged one year such as per pupil

expenditures, 4th grade test scores and pupil teacher ratios. fit1,…,fitk are the time demeaned real

per pupil values of other foundation revenue sources. Exec is a dummy variable indicating the

presence of compensation to directors and staff. fik+2,…,fitn are the time demeaned real per pupil

values of foundation expenses. Act60 is a dummy variable indicating the presence of Act 60’s

alternative revenue generation incentives. Incentact60i is an interaction term between the Act 60

dummy and a measure of equalized school district property wealth in 1999. d3,…,dk are dummy

variables for all years except 1995, 1996 and 2003, which were dropped to avoid issues of

perfect multicollinearity. The existence of a significant number of zero values for the dependent

and independent variables necessitate a level-level model.

The simplest model, regressing the real per pupil contributions received on incentact60

and year dummies takes the following form:

ipcontit = β0 + δ1year97 + δ2year98 + δ3year00 + δ4year01+ δ5year02+ δ6incentact60i + uit, t = 1,

2,…,8

The most complex model, regressing per pupil contributions on incentact60, year

dummies, the poverty rate, real median income for households filing joint returns, district

membership, lagged real per pupil expenses, lagged 4th grade math scores, the lagged pupil

teacher ratio, the exec dummy, real per pupil compensation to officers and directors, real per

pupil compensation to other paid staff, real per pupil solicitation expenses, and the real per pupil

total and fundraising expenses from phone use, mail and printing. A model including all of the

above measures would take the form:

43

ipcontit = β0 + δ1year97 + δ2year98 + δ3year00 + δ4year01+ δ5year02+ δ6incentact60i + ppovit +

limedjhhit + lmemberit + lagippcurexpit + lagm4cit + lagpupteachit + act60over64i +

act60under19i + act60popstabi + exec + icompensit + iothsalit + isolicitit + ip2tphoneit +

ip2fphoneit + ip2tpostgit + ip2fpostgit + ip2tprintit + ip2fprintit + uit, t = 1, 2,…,8

All district variables for foundations which cover multiple districts are enrollment-

weighted sums.

For the affiliated organizations, generalized least squares random effects models allow

for differences across and within observations over time to impact contribution levels. Random

effects models assume that the unobserved effect ai as well as uit is uncorrelated with each

explanatory variable (Wooldridge, 2003). A GLS transformation of the basic unobserved effects

model yit = β1xit + ai + uit, t= 1,2,…,T eliminates serial correlation in the errors. The random

effects models are quasi-demeaned versions of the time-demeaned fixed effects models. The

same explanatory variables are used as in the fixed effects models, with the addition of one-time

census variables. An iprop variable contains per pupil property wealth information for the years

1999-2002.

44

Results

All school district regression results indicate that towns did respond to the policy

incentives of Act 60 to increasingly finance K-12 education through alternative revenue (see

table 10). The coefficient on incentact60 is positive and highly significant in all four models. In

the simplest case, a 1% increase in a school district’s per pupil real equalized property wealth

increased the district’s Act 60 response by 0.3%. This elasticity fluctuates between .25 and .33.

Income levels of a school districts residents and variation in these income levels do not

significantly impact the alternative revenue response. In no specification were the poverty rate or

real median income for households filing joint returns significant36. This result is somewhat

surprising to both general intuition and to the specific case of Act 60. Evidence that more

wealthy school districts more successfully generate alternative revenue is not supported in these

data. Property wealth does to some extent measure district wealth, but income is a much better

measure of a community’s liquid assets. One potential explanation is that the two income

measures available in the data set are imperfect. They do not capture the income levels of the out

of state residents owning property in gold towns. The selection of a fixed effects model

constitutes an additional potential explanation – it is possible that there is not significant

variation within districts over time to detect a noticeable effect. It is also possible that income did

not affect a district’s capacity to generate alternative revenue.

In the Act 60 case, Vermont residents earning less than $75,000 in annual income were

essentially exempt from the incentives of the sharing pool. While high income residents of gold

towns enjoyed tax savings in excess of fair share contributions37 if a town were to successfully

raise above foundation education revenue outside tax base, low income residents did not.

36 Coefficients on real average adjusted growth income (not included in the table) were similarly insignificant. 37 Fair share contributions were the contributions requested by local education foundations of Vermont taxpayers based on property values.

45

Anecdotal evidence that gold towns with higher proportions of low-income residents had more

difficulty generating non-tax revenue in response to Act 60 is not directly supported by these

data. An interaction term between the percent of school district residents earning less than

$75,000 in annual income as measured by joint/ head of household returns in 2001 (the earliest

year for which there was readily available data) and the act 60 dummy was not significant in any

specification run.

The coefficient on enrollment is consistently negative and highly significant. Increases in

district enrollment implied increasing difficulty or decreasing willingness to generate alterative

revenue. In the second regression, a 1% decline in membership implies a .7% increase in

alternative revenue generation. This result concurs with the hypothesis that smaller community

size mitigates the free rider problem inherent in alternative revenue generation efforts. There is

one important caveat to the interpretation of this coefficient. Declining membership may imply a

rising out of state population. Due to a real estate boom, the proportion of out of state residents

of many gold towns increased during the period. The exemption of out of state residents from the

low-income prebate implies that all else equal, out of state residents enjoyed greater economic

benefit from successful school district alternative revenue generation than in state residents.

Lagged test scores were not significant in any specification examined, providing no

evidence that improvement in student performance enhanced or inhibited a district’s capacity to

generate alternative revenue. Due to the dominance of tax savings rather than educational

improvement efforts in motivating alternative revenue generation in Vermont, this result is not

altogether surprising. Lagged values of the pupil teacher ratio proved similarly unpredictive, as

46

did lagged measures of real per pupil expenses38. These data provide no information on an effect

from school quality or public financial effort on alternative revenue generation.

The positive, significant coefficient on the act60over64 interaction term indicates that the

presence of many senior citizens enhanced a town’s Act 60 response. A potential explanation

involves the surplus of volunteer hours implied by a large elderly population. The Vermont

foundations relied heavily on volunteers, and anecdotal evidence indicates that many older,

retired residents were particularly active on foundation boards. The relatively greater sensitivity

of senior citizens to property tax increases and their corresponding increased eagerness to

contribute to local education foundations and avoid the tax increases of the sharing pool is

another potential explanation. This result may also corroborate past studies which have shown

senior citizens to have a relatively higher demand for school quality than the population at large.

The negative significant coefficient on the act60popstab interaction term implies that less stable

populations raised more revenue during the Act 60 period. It was hypothesized that with towns

more stable populations would be more cohesive, contain more school alumni and therefore be

able to generate alternative revenue more effectively. This coefficient indicates a relationship in

the opposite direction.

A series of regressions analyzing each component category of the umbrella alternative

revenue indicated that two types of alternative revenue increased significantly during the Act 60

period: miscellaneous revenue and revenue from other school districts. The jump in

miscellaneous revenue is largely due to school district receipt of contributions by local education

foundations. The increase in revenue from other school districts indicates an increase in tuition

38 In the case of Ludlow, a school budget increase actually complicated fundraising efforts. Taxpayers contributed to the Ludlow Education Assistance Fund out of a desire to reduce their tax bill. When the school budget increased, many perceived a misallocation of their contributions to increasing school funding instead of reducing taxes. Popular discontent motivated a LEAF advertisement in a local newspaper opposing the budget increase.

47

payments from out of district students. This increase, robust to a parallel increase in real per

pupil expenditures, may indicate an increased effort by school districts to attract out of district

students.

In the all three specifications of the model with miscellaneous revenue as the dependent

variable, the incentact60 coefficient is highly significant (see table 11). The simplest regression,

involving only the incentact60 term and year dummies, implies a .65% increase in miscellaneous

revenue raised for every 1% increase in per pupil equalized property wealth during the Act 60

period. The coefficient rises to .775 with the inclusion of demographic interaction terms and

contemporaneous school district characteristic variables. The coefficient on member is negative,

significant and quantitatively large, implying a positive relationship between smaller enrollments

and increased generation of miscellaneous revenue. A 1% decrease in enrollment implies a larger

than 1.4% increase in the generation of miscellaneous revenue, corroborating Fischel (2003) that

smaller community size reduces free-riding. The coefficient on 4th grade math proficiency is

negative and significant at the 10% level, implying a relationship between worsening test

performance and improved generation of miscellaneous revenue. To the extent that district

residents believe that additional resources will improve educational quality, and to the extent that

the miscellaneous revenue category captures contributions of these additional resources, this

indicates a monetary response to academic underperfomance. Median adjusted income for

households filing joint returns is marginally significant, with a positive coefficient implying a

weak impact of rising real incomes and the increased generation of miscellaneous revenue. The

disincentact60 term (an interaction between joint/head of household returns reporting less than

$75,000 in annual income and the Act 60 dummy) is negative and highly significant in the

simplest regression, but this significance fades as more variables are added. This provides some

48

evidence that towns with a higher percentage of residents with less than $75,000 in annual

income had greater difficulty generating miscellaneous revenue during Act 60. The act60over64

interaction term is positive and significant at the 5% level, indicating that an older population

amplified the generation of miscellaneous revenue during Act 60.

In the revenue from other school district regressions, the coefficient for incentact60 is

positive and highly significant in a variety of specifications (see table 12). In the simplest

regression, the coefficient implies a .14% increase in revenue from other school districts raised

for every 1% increase in per pupil equalized property wealth during the Act 60 period. However,

this relationship is not robust to the inclusion of lagged test scores. Unsurprisingly, the

coefficient on the lagged pupil teacher ratio is negative and significant. This may indicate that

schools with lower pupil teacher ratios are perceived as offering a better education and more

easily attract tuition-paying students from other districts or that these schools have excess

capacity and can more easily accommodate more tuition paying students. The act60over64

coefficient is positive and significant at the 5% level, indicating that school districts with older

populations raised more revenue from other school districts during the Act 60 period.

Other sources of alternative revenue were either negligible or did not respond to Act 60’s

incentives.

In the fixed effect affiliated organizations regressions, the incentact60 interaction term

was not significant in any specification run (see table 13). This may be due in part to the

overwhelming concentration of foundation observations during Act 60 (i.e. there are very few

values of act60 = 0). The iprop variable, containing time-varying property wealth information

from 1999-2002, was similarly insignificant in every specification run. There is correspondingly

no evidence of the significance of the contemporaneous or lagged school district characteristics

49

in impacting foundation success, as coefficients for district poverty rates, income levels,

enrollment figures, lagged test scores, lagged pupil teacher ratios and lagged school expenditures

were insignificant in every specification run.

Real solicitation expenses were consistently positive and significant, with a consistently

quantitatively large coefficient. A one dollar increase in solicitation expenses implied at least an

$11 increase in contributions. That a greater foundation fundraising effort would generate a

greater level of contributions, particularly at a sufficiently low level of fundraising expenses to

mitigate concerns of contributions funding solicitation rather than the organization’s charitable

purpose is a logical result. The magnitude of the impact of additional solicitation expenses is

impressive.

The digitized data available for the years 1998-2001 pinpoint the key source of these

impactful solicitation expenses as postal expenditures. The coefficient on fundraising postal

expenditures is large, positive and highly significant. The Vermont local education foundations

primarily solicited contributions through mailings to district taxpayers. These mailings generally

explained the purpose of the foundation, listed the taxpayer’s fair share contribution based on the

value of his/her property, and provided the amount the taxpayer would save should the

foundation meet its fundraising goal. Some foundations sent follow up mailings to those who had

not yet contributed, with some primarily targeting the top taxpayers, and some foundations only

sent a single mailing. The large, negative quantitatively significant coefficient on fundraising

phone expenses lacks a clear explanation.

Parallel fixed effects specifications were run using average daily membership figures

from the Vermont School Report as the right hand side enrollment variable and the weighting

50

variable for per pupil calculations in place of Common Core membership. These regressions

yielded essentially the same results those relying on Common Core membership (see table 14).

Random effects specifications used average daily membership as the enrollment and

weighting variable and encompassed the years 1999-2003. The random effects affiliated

organizations models (see table 15) concurred with the fixed effects conclusions of the

significance of solicitation and postal expenses. In addition, the coefficient for per pupil property

wealth was positive and highly significant in every specification, providing evidence of the

impact of Act 60 incentives on foundation activity.

Random effects specifications run solely on local education foundations (about three

quarters of the affiliated organizations subset) yielded similar results with one additional finding

(see table 16). The coefficient for the poverty rate was negative and significant at the 5% or 10%

level in every specification run, indicating that local education foundations had more difficulty

generating revenue in areas with more poverty.

As to the appropriateness of the random effects model, the Breusch-Pagan Lagrange

multiplier test for random effects provided no indication that the random effects (ui’s) were

heteroskedastic in any of specifications run. The Hausman test provided no indication (failure to

reject the null hypothesis that random effects are appropriate at any conventional significance

level) that random effects were inappropriate for simple random effects models with the

inclusion of enrollment, property wealth, solicitation expenses, poverty rates, income levels, the

exec dummy and year dummies on the right hand side. The Hausman null was rejected at the

10% level with the inclusion of lagged variables in the affiliated organizations regressions, but

not the local education foundation regressions. The null was rejected at the 1% level with the

inclusion of digitized data (postage, printing and phone expenses).

51

The specifications run offer no evidence that compensation of officers, executive

directors, and support staff significantly impacts contributions, despite a significant correlation

between the exec dummy and real per pupil contributions received. This may be due to the

selection of the fixed effects model in that most variation in pay and the presence of paid staff

occurs among foundations rather than within foundations over time. The Freeman Foundation

contributions may also have confounded this result.

52

Conclusion

Vermont taxpayers significantly responded to the incentives latent in Act 60 to generate

revenue outside of the tax base, as Vermont school districts generated more non-tax revenue

through the channels of miscellaneous revenue and revenue from other school districts. As

indicated by the school district data and the survey results, other avenues of alternative revenue

generation were largely unpursued.

The data reveal certain district characteristics to dampen or magnify the alternative

revenue generation response. In particular, higher property values (a stronger Act 60 incentive),

lower district memberships and a large percentage of senior citizens facilitated raising alternative

revenue during Act 60. A more stable population weakened alternative revenue generation

efforts. Higher property values, lower district memberships, a large percentage of senior citizens

and academic struggles facilitated raising miscellaneous revenue, while higher property values

and low pupil teacher ratios facilitated generating revenue from other school districts. Survey

results provide preliminary evidence of a tremendous return to grantwriting expenses.

For the Vermont affiliated organizations, solicitation expenses – postal expenses in

particular – powerfully enhanced real contributions received. The Act 60 incentive, measured by

property wealth, and the poverty rate also appeared to significantly impact contributions. The

data did not reveal other school district characteristics and other foundation activities to

significantly impact revenue generating efforts. More detailed data on the composition and

operation of the foundations, as well as other analytical models, may yield further conclusions in

this regard.

The policy incentive of Act 60 was unequivocally unique, but the issue of alternative

revenue generation in public schools is broadly relevant. Several of the lessons learned from

53

Vermont should be applicable to school districts and local education foundations in other

states facing similar demands for non tax funds to support K-12 education. A crucial

direction of future study is to determine the applicability of these lessons.

54

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60

Appendix

Table 1: School District Summary Statistics

1996 1997 1998 1999 2000 Mean

Standard Deviation Mean

Standard Deviation Mean

Standard Deviation Mean

Standard Deviation Mean

Standard Deviation

School District Characteristics Enrollment 362.24 495.65 362.80 488.18 360.90 489.46 355.84 492.55 353.22 491.42

17.97 16.50 32.47 19.05 37.94 17.83 38.14 18.85 Percentage of 4th graders achieving math proficiency Retention rate 1.31 1.86 1.61 2.12 1.76 2.50 1.62 2.24 1.74 3.30 Pupil teacher ratio 21.28 60.67 13.00 2.35 12.73 2.25 12.28 2.28 12.15 2.25 School District Demographics Poverty rate 12.44 7.86 10.91 8.04 11.60 7.39 10.74 7.48 11.81 7.70

35664.42 8906.04 36882.25 9106.36 38556.31 9509.51 40653.90 9741.62 42073.13 9884.30 Median income for households filing joint returns School District Revenues Total revenue per pupil 10519.64 4017.10 11018.39 4145.52 11677.25 4566.19 12586.88 5046.88 13058.82 4719.76 Total alternative revenue per pupil 1353.78 2225.50 1423.11 2320.55 1491.05 2442.89 1571.31 2645.02 1725.53 2642.00 Total miscellaneous revenue per pupil 93.31 98.92 137.43 162.69 131.72 153.25 75.41 127.55 223.61 626.08 Total other alternative revenue per pupil 1260.46 2227.36 1285.67 2316.99 1359.33 2425.57 1495.90 2632.55 1501.92 2597.96 Total interest earnings per pupil 97.09 87.78 95.65 94.38 94.19 82.97 99.52 94.79 106.62 111.23

Total revenue from other school districts per pupil 1033.42 2186.77 1070.64 2290.96 1147.74 2407.93 1264.39 2613.10 1270.27 2578.61

Total revenue from other sales and services per pupil 0.41 4.00 2.39 16.45 0.60 7.30 1.48 12.02 0.04 0.66 Tuition and fees per pupil 33.32 174.49 15.78 77.90 14.39 73.60 19.66 88.96 12.57 52.01 Transportation fees per pupil 3.49 30.63 4.14 38.03 3.29 28.99 2.28 29.25 1.11 10.12 Revenue from the sale of books per pupil 0.48 7.48 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.98 Activity fees per pupil 5.86 25.59 5.81 25.70 4.44 22.26 4.86 22.90 2.40 10.14

0.60 3.08 0.55 2.51 0.55 2.60 1.18 4.57 0.75 3.44 Federal math, science and professional development grants per pupil School District Expenses Expenses per pupil 5099.91 2341.41 5952.09 1025.37 6199.39 1119.04 6701.18 1482.33 7601.18 1330.29 Teacher salaries 33530.49 4487.78 33948.62 4495.41 34902.08 4880.44 35455.79 4881.99

61

2001 2002 2003 Aggregate Mean St. Dev Mean St. Dev Mean St. Dev Obs. Mean St. Dev. Min. Max. School District Characteristics Enrollment 344.62 490.00 339.99 484.75 330.99 481.12 2253 351.27 488.50 0 3840

42.06 19.04 45.65 20.42 1209 35.52 20.66 0 100.00 Percentage of 4th graders achieving math proficiency Retention rate 1.65 2.18 1.42 1.94 1718 1.59 2.35 0 37.50 Pupil teacher ratio 11.13 1.99 10.89 2.28 1713 13.38 23.42 5.40 796.00 School District Demographics Poverty rate 10.63 7.05 9.73 6.55 1631 11.10 7.46 0 41.60

Median income for households filing joint returns 43999.46 10470.87 44586.38 10749.52 1661 40373.51 10285.14 19015 89898 Per pupil equalized property value in 1999 1703 643541.80 543033.70 0 4066189

Population stability (% living in the same town for at least five years) 2065 0.66 0.06 0.46 0.84 Percentage of the population under 19 2065 0.27 0.03 0.10 0.39 Percentage of the population over 64 2065 0.13 0.04 0.05 0.30 School District Revenues Total revenue per pupil 14183.47 5232.58 15447.80 6138.07 16461.89 7016.06 1942 13095.29 5542.01 4544.97 84562.50 Total alternative revenue per pupil 2021.59 2988.72 2127.10 3262.81 2234.92 3453.34 1942 1739.56 2787.18 0 12128.89 Total miscellaneous revenue per pupil 299.21 785.25 329.69 980.10 390.54 1203.18 1942 208.83 664.51 0 8750 Total other alternative revenue per pupil 1722.37 2936.10 1797.41 3188.13 1844.38 3338.81 1942 1530.73 2733.46 0 12122.22 Total interest earnings per pupil 131.19 130.52 77.48 70.97 59.10 70.15 1942 95.15 96.55 0 1000

Total revenue from other school districts per pupil 1442.91 2908.18 1566.88 3164.17 1634.82 3306.27 1942 1301.29 2707.50 0 11702.22

Total revenue from other sales and services per pupil 3.49 33.56 1.65 17.63 2.84 26.89 1942 1.60 18.09 0 462.96 Tuition and fees per pupil 13.29 42.49 12.15 41.82 11.00 34.38 1942 16.57 85.14 0 2075.86 Transportation fees per pupil 6.51 53.29 1.86 18.50 0.93 11.92 1942 2.95 30.62 0 714.29 Revenue from the sale of books per pupil 0.14 1.72 0.00 0.00 0.00 0.00 1942 0.09 2.75 0 117.35 Activity fees per pupil 3.70 19.94 3.31 13.68 4.59 27.33 1942 4.38 21.72 0 383.12

0.95 4.34 1.32 5.12 1.83 22.54 1942 0.96 8.66 0 335.88 Federal math, science and professional development grants per pupil Student fees 2253 0 0 0 0

62

School District Expenses Expenses per pupil 8262.36 1491.91 8888.53 1583.64 10032.60 2403.12 1980 7285.03 2292.88 0 20945.95 Teacher salaries 36130.65 4861.57 37229.33 5192.18 1453 35195.23 4959.51 18999 53352

63

64

Table 2: Foundation Summary Statistics

1996 1997 1998 1999 2000 N Mean

Standard Deviation N Mean

Standard Deviation N Mean

Standard Deviation N Mean

Standard Deviation N Mean

Standard Deviation

School District Characteristics Enrollment 4 1809.75 1722.09 4 1304.25 1140.19 9 1121.44 1326.29 28 708.64 932.55 36 752.14 1011.19 4th grade math proficiency (%) 4 25.07 6.38 4 0 0 8 39.49 17.47 27 45.70 15.67 34 41.79 17.54 Pupil teacher ratio 4 13.21 1.80 4 12.40 2.20 8 12.56 1.71 27 11.85 1.55 34 11.84 1.70 Poverty rate 4 17.75 12.84 4 9.33 10.32 8 10.39 10.22 27 6.37 6.20 34 6.98 5.60

4 45944.30 17608.14 4 47312.53 17140.96 8 42069.13 20939.70 27 47338.46 24469.85 34 55770.68 14375.45 Median income for households filing joint returns School District Expenditures Teacher salaries 4 43176.30 6573.51 4 40342.84 4722.08 8 0 0 27 41500.82 7144.23 34 41481.51 6356.17 Expenses per pupil 4 6939.29 402.86 4 7285.77 421.26 8 7365.51 812.54 27 7993.30 1153.51 34 8715.96 1339.31 Organization Revenues Contributions per pupil 4 8.45 16.91 4 10.32 15.97 9 445.12 688.21 28 1786.02 3388.14 34 1302.13 1622.69 Total revenue per pupil 4 75.73 118.72 4 73.28 67.23 9 481.53 672.42 28 1837.39 3434.39 34 1360.91 1673.24 Program revenue per pupil 2 0 0 2 7.56 10.69 7 1.59 4.20 26 1.11 5.27 33 1.65 5.76 Special events income per pupil 2 0 0 2 0 0 7 2.14 5.66 26 10.77 32.67 33 6.47 20.19 Investment income per pupil 3 83.17 142.65 4 79.90 126.19 9 29.76 79.94 28 39.49 78.09 34 50.49 74.08 Organization Expenses Solicitation expenses per pupil 2 0 0 2 0 0 6 18.94 46.34 26 24.15 53.43 33 11.77 28.94 Total phone expenses per pupil 0 0 5 0 0 21 1.21 3.97 28 0.65 1.76 Fundraising phone expenses per pupil 0 0 5 0 0 21 0 0 28 0.18 0.93 Total postage expenses per pupil 0 0 5 0 0 21 6.15 10.74 28 6.41 10.53 Fundraising postage expense per pupil 0 0 5 0 0 21 4.21 9.61 28 4.35 9.16 Total printing expenses per pupil 0 0 5 0.02 0.04 21 9.49 34.24 28 1.65 2.91 Fundraising printing expenses per pupil 0 0 5 0.02 0.04 21 5.46 17.33 28 0.80 1.83 Presence of paid staff 2 0.5 0.71 1 1 . 7 0.29 0.49 26 0.08 0.27 35 0.26 0.44 Compensation to officers and directors 4 7.03 14.05 4 7.81 15.61 9 4.76 11.59 28 1.31 6.95 34 4.42 14.66 Other compensation 1 0 . 1 0 . 6 0 0 26 1.62 8.25 33 4.37 14.82

65

2001 2002 2003 Aggregate

N Mean Standard Deviation N Mean

Standard Deviation N Mean

Standard Deviation N Mean

Standard Deviation Minimum Maximum

School District Characteristics Enrollment 40 953.88 1155.49 42 878.52 1071.41 40 867.70 1081.18 205 874.29 1081.81 0 3951 4th grade math proficiency (%) 38 41.49 16.82 40 49.39 21.06 0 155 42.73 19.29 0 89 Pupil teacher ratio 38 11.10 1.92 40 10.67 2.10 0 155 11.44 1.93 6.43 15.48 Poverty rate 38 7.07 6.41 40 6.42 5.44 0 155 7.26 6.67 0 29

38 55879.20 14532.11 40 55298.13 12565.58 0 155 53027.47 16937.08 0 93828.91 Median income for households filing joint returns

195 811030.80 921657.90 0 4066189 Per pupil equalized property value in 1999 Population over 64 (%) 195 0.14 0.05 0 0.20 Population under 19 (%) 195 0.24 0.05 0 0.31

195 0.61 0.12 0 0.75 Population stability (% living in the same town for at least five years) School District Expenditures Teacher salaries 38 41503.30 5774.46 40 41064.69 6036.90 0 155 39256.02 10970.31 0 54291.47 Expenses per pupil 38 9291.00 1338.90 40 9797.05 1450.89 0 155 8857.59 1515.21 5799.10 13424.66 Organization Revenues Contributions per pupil 38 1278.71 1895.26 40 1367.12 2349.49 34 1340.28 2313.06 191 1294.28 2238.58 0 16132.24 Total revenue per pupil 38 1349.36 1941.92 40 1532.44 2423.45 38 1400.83 2347.05 195 1376.68 2286.99 -190.41 16424.69 Program revenue per pupil 36 4.81 15.53 38 125.34 725.29 33 185.78 1013.76 177 63.14 550.78 0 5830.96 Special events income per pupil 36 7.40 21.53 38 11.33 32.10 33 8.68 37.08 177 8.43 28.15 -80.21 156.07 Investment income per pupil 37 50.84 85.48 39 34.79 55.08 34 23.46 43.85 188 40.93 71.59 -0.01 411.39 Organization Expenses Solicitation expenses per pupil 36 14.45 36.62 37 13.67 30.21 31 16.02 35.14 173 15.33 36.52 0 198.79 Total phone expenses per pupil 31 0.45 1.30 0 0 85 0.68 2.34 0 17.50 Fundraising phone expenses per pupil 31 0.24 0.96 0 0 85 0.14 0.78 0 4.92 Total postage expenses per pupil 31 5.82 9.77 0 0 85 5.75 9.97 0 39.25 Fundraising postage expense per pupil 31 3.13 7.80 0 0 85 3.62 8.47 0 39.25 Total printing expenses per pupil 31 2.02 3.86 0 0 85 3.63 17.29 0 157.63 Fundraising printing expenses per pupil 31 1.58 3.78 0 0 85 2.19 9.02 0 78.80 Presence of paid staff 38 0.21 0.41 40 0.25 0.438529 35 0.26 0.44 184 0.23 0.42 0 1 Compensation to officers and directors 38 1.19 5.37 40 2.68 11.30 34 5.47 18.45 191 3.29 12.30 0 82.36 Other compensation 36 11.41 32.62 38 12.62 36.75 33 21.97 72.50 174 10.35 39.71 0 389.61

66

67

Survey Data

Table 3: Sample representativeness

All supervisory unions Responding supervisory unions

Number of Observations Mean

Standard Deviation

Number of Observations Mean

Standard Deviation

Enrollment 61 1597.90 957.23 17 1573.77 948.26Poverty rate 61 9.67 5.16 17 9.36 4.78Free and reduced price lunch 61 29.56 12.73 17 29.77 13.60

61 19210.24 3575.69 17 18562.43 3025.02Average adjusted gross income

61 45909.85 11043.05 17 43980.31 9295.23Median income for households filing joint returns Per pupil current expenses 61 4618.64 3114.37 17 3929.79 2863.31

Table 4: Affiliated organizations

Mean

Standard Deviation

Parent teachers organizations/associations 4.12 2.29Booster clubs 1.59 1.62Local education foundations 0.47 0.94LEF attempts 0.24 0.44LEF successes 0.75 0.50

Table 5: Grantwriting

Mean Correlation

Number of districts

Standard Deviation

Competitive grants last year 12 489151.4 734009.04 Grantwriting expenses 5 7153.85 12348.05 0.54

Grants received by district with grantwriting expenses 5 879736 999406.5

Grants received by district with no grantwriting expenses 12 210162.4 322382.54

68

Table 6: User Fees

Mean

Number levying fees

Standard Deviation

After-school programs 6 13853.33 36005.08Transportation 3 600 1243.65Athletics 9 7000 13063.46Parking 2 369.23 933.97School supplies 0 0 0Other fees 0 0 0Total fees 13 18729.41 34035.43

Table 7: Alternative Revenue Generating Activities

Mean Standard Deviation

Number of districts

Number of districts generating revenue in excess of expenses

Average revenue generated

Advertising 0.19 0.40 3 Adult education programs 0.41 0.51 7 Day care services 0.24 0.44 4 Summer school programs 0.88 0.33 15 Summer camp 0.53 0.51 9 Provision of school space to other organizations 0.76 0.44 13 1 155000

0.29 0.47 5 Selling of services (food services, transportation services, etc...)

0.33 0.49 5 3 4900Provide concessions such as the exclusive rights for student pictures or beverage sales Independently operate vending machines 0.6 0.51 9 1 10000Scrip program 0 0 0

0.07 0.26 1 Purchase and re-sell certificates for products from local businesses

Table 8: Partnerships

Mean

Standard Deviation

Businesses 0.31 0.48Colleges and universities 0.35 0.49

69

Table 9: Investments

Number of districts

Arbitrage funds 9Savings accounts 1Money market accounts 3Certificates of deposit 5Treasury bills or other bonds 1

3Repurchase agreements with local lending institutions

70

Table 10: Alternative Revenue Regressions

lipaltrev lipaltrev lipaltrev lipaltrev lipaltrev year97 0.075 0.099 4.489 4.428 0.101 -0.042 (0.047)* (0.853)** (0.854)** (0.046)* year98 0.087 0.111 0 0 0.1 (0.042)* (0.047)* 0 0 (0.047)* year00 0.116 0.122 0.145 0.135 0.115 (0.041)** (0.043)** (0.054)** (0.053)* (0.043)**act60 -3.993 -4.153 0 0 -2.85 (0.479)** (0.506)** 0 0 (0.696)**incentact60 0.302 0.316 0.336 0.332 0.257 (0.036)** (0.038)** (0.066)** (0.066)** (0.041)**year01 0.315 0.298 0.356 0.316 0.287 (0.042)** (0.044)** (0.066)** (0.057)** (0.044)**year02 0.267 0.232 0.266 0.215 0.221 (0.042)** (0.044)** (0.073)** (0.057)** (0.044)**Ppov -0.006 -0.014 -0.014 -0.004 0.006 0.011 0.011 0.006limedjhh -0.157 -0.126 -0.154 0.021 0.251 0.435 0.435 0.252lmember -0.695 -1.019 -0.917 -0.756 (0.144)** (0.243)** (0.227)** (0.145)**laglippcurexp -0.246 0.213 lagm4c -0.001 -0.001 0.001 0.001 lagpupteach 0.001 0.001 act60over64 3.336 (0.724)**act60popstab -1.313 (0.446)**act60under19 -0.413 0.917Constant 6.342 11.706 10.816 8.44 10.117 (0.030)** (2.819)** (5.131)* 4.671 (2.825)**Observations 1651 1555 959 958 1540Number of ncesid 238 238 209 209 235R-squared 0.11 0.13 0.13 0.13 0.15Standard errors below coefficients * significant at 5% level; ** significant at 1% level

71

Table 11: Miscellaneous Revenue Regressions

Lipmiscrev lipmiscrev lipmiscrev year97 0.242 0.196 (0.099)* 0.110 year98 0.302 0.245 (0.098)** (0.112)* year00 0.257 0.242 0.292 (0.104)* (0.107)* (0.140)* act60 -8.216 -6.244 -9.973 (1.377)** (1.875)** (3.553)** incentact60 0.653 0.606 0.775 (0.090)** (0.103)** (0.189)** disincentact60 -0.011 -0.010 -0.006 (0.005)* 0.006 0.010year01 0.562 0.486 0.560 (0.103)** (0.108)* (0.172)** year02 0.548 0.443 0.444 (0.102)** (0.107)* (0.191)** Ppov 0.003 -0.008 0.015 0.027act60over64 4.452 7.278 (1.839)* (3.274)* act60popstab -2.465 -2.156 (1.096)* 1.809act60under19 -2.026 0.482 2.321 4.378limedjhh 1.156 0.137 0.628 1.152lmember -1.520 -1.486 (0.360)** (0.637)* lagm4c -0.006 (0.003)* laglippcurexp 0.114 0.559Constant 4.379 0.526 10.538 (0.070)** 7.104 13.823Observations 1499 1391 850Number of ncesid 235 232 202R-squared 0.12 0.16 0.13Standard errors below coefficients * significant at 5% level; ** significant at 1% level

72

Table 12: Other School Revenue Regressions

Lipothschrv lipothschrv lipothschrv year97 -0.001 0.04 0 0.073 0.094 0 year98 -0.019 0.042 0 0.073 0.096 0 year00 0.074 0.08 0.111 0.072 0.08 0.109 act60 -1.945 -2.107 -2.396 (0.754)* 1.357 2.44 incentact60 0.147 0.139 0.083 (0.057)* 0.072 0.131 year01 0.159 0.169 0.165 (0.072)* (0.078)* 0.111 year02 0.255 0.262 0.215 (0.071)** (0.079)** -0.113 Ppov -0.004 -0.017 0.014 0.022 act60over64 3.019 5.233 (1.530)* (2.644)* act60popstab 0.294 0.181 0.859 1.352 act60under19 -1.006 1.352 2.184 3.706 limedjhh -0.163 -0.487 0.513 0.927 lmember -0.008 -0.212 0.328 0.534 lagm4c 0.003 0.002 lagpupteach -0.003 (0.001)* laglippcurexp Constant 6.242 7.724 12.078 (0.052)** 6.025 10.506 Observations 916 811 476 Number of ncesid 174 170 137 R-squared 0.04 0.05 0.06 Standard errors below coefficients * significant at 5% level; ** significant at 1% level

73

Table 13: Affiliated Organizations Regressions

ipcont Ipcont Ipcont Ipcont act60 325.97 -1,363.62 -1,296.00 -215.06 778.771 1,268.52 1,688.77 1,093.67incentact60 0 0.001 0.001 0 0 0.001 0.001 0.001year97 5.217 -800.069 -492.357 0 1,009.08 1,603.16 2,019.80 0year98 -1,369.96 -895.66 -472.466 0 875.441 1,190.79 1,596.90 0year00 -13.564 263.956 240.691 -8.851 335.783 402.935 449.023 457.468year01 136.85 139.33 131.156 -123.556 326.339 395.983 438.506 401.749year02 277.936 216.64 286.888 0 319.932 433.238 503.793 0member -2.371 -1.871 -14.221 4.314 4.709 8.611exec -57.857 -377.119 556.851 647.5 imedjhh 0 -0.001 0.001 0.016 0.017 0.018ipsolicit 11.036 11.386 5.776 6.095 ppov -230.526 -223.903 -322.05 153.778 180.141 237.042lagm4c -0.121 8.903 lagpupteach -60.345 83.868 ipcompens 10.905 23.481ipothsal 3.283 10.876ipp2fphone -697.818 346.653ipp2fpostg 104.602 (27.699)**ipp2fprint -42.366 80.559Constant 1,139.13 5,014.25 5,198.70 14,437.58 756.841 4,796.94 5,407.90 7,965.50Observations 191 139 128 82Number of EIN 46 42 42 34R-squared 0.08 0.12 0.13 0.42Standard errors below coefficients * significant at 5% level; ** significant at 1% level

74

Table 14: Foundation Regressions using Average Daily Membership

ipcont2 ipcont2 ipcont2 year00 108.61 112.73 -61.23 269.37 311.63 372.22year01 46.13 98.62 221.31 257.17 291.92 383.59year02 42.69 145.35 292.44 341.50 enroll2 -0.95 -1.96 0.59 2.05 3.37 2.59ppov -128.55 -133.54 -49.24 102.13 118.11 166.27imedjhh 0.0031 0.0037 0.0077 0.0105 0.0116 0.0138ipsolicit3 14.45 12.67 (5.46)** (5.85)* exec 31.96 -132.14 384.45 418.71 iprop -0.00012 -0.00011 -0.00079 0.00031 0.00033 0.00092lagm4c 1.38 5.64 lagippcurexp -0.068 0.071 ipcompens3 2.95 5.86ipothsal3 -2.34 7.53ipp2fphone3 -694.87 (294.48)*ipp2fpostg3 107.47 (35.66)** ipp2fprint3 -25.76 75.00Constant 2354.84 3717.86 1243.04 2005.10 3104.86 3000.37Observations 131 122 79Number of EIN 42 42 34R-squared 0.1 0.12 0.37Standard errors below coefficients * significant at 5% level; ** significant at 1% level

75

Table 15: Affiliated Organizations Regressions - Random effects

ipcont2 ipcont2 Ipcont2 year00 16.63 0.24 -108.77 222.74 239.27 265.07year01 -32.90 -75.71 -110.79 215.92 237.76 259.68year02 -81.44 -29.20 217.02 244.33 enroll2 0.02 -0.01 -0.24 0.12 0.12 0.18Ppov -29.86 -34.93 5.66 18.86 19.71 30.33Imedjhh -0.0023 -0.0048 0.0011 0.0063 0.0064 0.0079ipsolicit3 23.18 24.14 (3.35)** (3.40)** Exec 175.37 228.98 198.21 199.29 Iprop 0.00038 0.00036 0.00030 (0.00007)** (0.00007)** (0.00014)*over64 2896.10 2213.41 Popstab 580.08 1328.12 under19 -1602.77 2548.82 lagm4c -3.51 4.51 lagippcurexp -0.008 0.060 ipp2fphone3 -28.25 236.92ipp2fpostg3 54.84 31.45ipp2fprint3 64.76 52.47ipcompens3 -7.44 5.02ipothsal3 11.71 6.95Constant 110.99 886.78 562.80 560.87 610.50 494.22Observations 131 122 79Number of EIN 42 42 34R-Squared 0.61 0.6 0.55Standard errors below coefficients * significant at 5% level; ** significant at 1% level

76

Table 16 – LEF Regressions – Random Effects

ipcont2 ipcont2 Ipcont2 year00 62.09 -9.45 -148.19 274.07 287.20 332.13year01 12.37 -77.65 -122.08 269.83 288.09 333.40year02 4.61 44.20 278.68 303.51 enroll2 0.12 0.22 -0.16 0.22 0.21 0.34ppov -56.88 -68.19 -10.67 31.16 (29.89)* 45.92imedjhh -0.0047 -0.0090209 0.00011 0.0096 0.0087 0.01112ipsolicit3 20.52 23.14 (4.22)** (3.80)** exec 1.71 99.19 256.45 230.92 iprop 0.00032 0.0003562 0.00028 (0.00011)** (0.000088)** 0.00017over64 2647.64 3049.34 popstab -681.47 2329.72 under19 -4285.65 4909.42 lagm4c -4.13 5.35 lagippcurexp -0.036 0.071 ipp2fphone3 -67.06 269.69ipp2fpostg3 59.53 35.83ipp2fprint3 47.84 61.55ipcompens3 -7.59 5.71ipothsal3 11.13 7.89Constant 1993.71 1580.82 811.79 1987.57 787.23 674.22Observations 100 96 62Number of EIN 29 29 26R-Squared 0.56 0.57 0.49Standard errors below coefficients * significant at 5% level; ** significant at 1% level