## Abstract

We examine changes in the use of nontax revenues for education finance from 1991 to 2010. Beyond the summary of usage over time, we ask whether nontraditional revenues like fees accentuate or mitigate the impact of downturns. More generally, we examine the extent to which school districts have responded to fiscal pressures by turning to nontax revenues. We also document the extent to which the use of nontax revenues varies across districts according to student poverty status. We show that alternative revenues continue to be a small source of local revenues and have increased quite little since the early 1990s. There was at most a minimal shift to nontax revenues in downturns, though there is evidence of greater use of these revenues among school districts facing more permanent fiscal pressures like tax limits. Differential access to fee revenues and other alternative revenues during downturns may slightly accentuate inequities in K–12 education spending.

## 1.  Introduction

School district per pupil revenues have been declining over the past few years in many parts of the country. As a result of the Great Recession, school districts have faced reductions in their two most important sources of revenue—property taxes and state aid. Although funding aid through the American Recovery and Reinvestment Act provided some help through fiscal year 2011, increased federal education funding was temporary. In order to prevent large cuts in education spending, school districts needed to find new sources of revenue. This paper examines how this combination of pressures may have influenced a shift in the mix of local school district revenues.

The popular press is replete with examples of school districts using fees to close funding gaps (e.g., The Pew Charitable Trusts 2012; Maxwell 2013). In very recent years, the growing use of fees has spawned lawsuits challenging their use in Idaho (Joki v. State of Idaho) and California (Doe v. The State of California). Killeen (2007) suggests, however, that the media attention paid to fees may overstate their importance as a response to the declines in federal and state aid. For example, in a survey of school administrators conducted by the American Association of School Administrators, only 17 percent of the respondents indicated that they were “shifting funding of extracurricular activities to families/community/boosters” in response to federal aid cuts resulting from sequestration (AASA 2013). These survey results are consistent with the finding in the sparse literature on school district utilization of these alternative revenue sources of relatively limited use of non-property tax revenues (Wassmer and Fisher 2002). This uncertainty about the extent to which districts had turned to fees to close revenue gaps is indicative of the fact that the use of alternative local government revenues for school districts is not well understood (Coleman 2014).

A relatively large public finance literature documents the fact that local governments often diversify away from property taxes and toward alternative revenues like fees in response to economic contractions and institutional pressures. Comparable research among school districts is far more limited but suggestive of a similar type of revenue diversification. Existing evidence on the link between tax limits and usage of fees suggests the possibility that imposition of fiscal constraints results in greater use of non-property tax revenues (Wassmer and Fisher 2002). The cross-sectional nature of the few studies of school district use of fees and charges means, however, that we cannot be sure whether the increased utilization of these revenue sources was a result of fiscal constraints or whether the political climate that led a state's voters or politicians to adopt fiscal constraints also made fees more tenable. The slightly larger literature on private contributions to public schools (Brunner and Sonstelie 1997, 2003; Brunner and Imazeki 2005; Downes and Steinman 2008) suggests contributions are sensitive to fiscal constraints but sheds no light on whether contributions are pro-cyclical. In the aftermath of the largest downturn since the Great Depression, the time is right to revisit the question of whether school districts turn to alternative revenue sources when faced with fiscal pressures.

We seek to accomplish three goals in this paper. First, because of the absence of systematic evidence on how the use of alternative revenue sources by school districts responds to fiscal pressures, our analysis will begin with a summary of the evolution of fees and other alternative revenue sources over the last two decades. Alternative revenues include fees and user charges for items like textbooks, transportation, and student activities. We will also document the extent to which the use of fees varies across school districts according to their student poverty status, with an eye toward providing some evidence on the question of whether the use of nontax revenue sources raises equity concerns.

Our second goal will be to examine the extent to which school districts have increased their reliance on these alternative revenue sources in response to the fiscal pressures created not only by economic downturns but also by constraints created by tax and expenditure limits and school finance reforms. Our third and final goal is to outline and discuss the policy implications of our findings.

The data needed to execute the type of analyses described here must span business cycles and cover states with a variety of fiscal institutions that affect each school district's ability to raise revenue. The F-33 survey data are part of the National Center for Education Statistics Common Core of Data (CCD) and satisfy these objectives. These data are national in scope and are available back to the 1991–92 school year. They allow us to examine the extent to which school districts have turned to alternative revenue sources in downturns and when faced with fiscal constraints such as tax and expenditure limits. In addition, because the CCD provides relatively rich data on each district's student population, one may summarize the characteristics of districts that make heavy and light use of alternative revenues.

The balance of this paper is organized in four sections. In the next section, the literature on alternative school district revenues is presented. This section provides definitions of these alternative revenues and places them within the context of the mix of overall state and local revenues. Our literature review helps frame our empirical models by providing a brief overview of what is known about the determinants of use of alternative local revenues. The following section describes the data and their sources and explains the methods used in this analysis. This is followed by a presentation of estimates of the empirical models used to explore how the use of fees and other alternative revenues changes across the business cycle. We then close by suggesting next steps for researchers and by making the case that fees remain an underutilized source of revenue.

## 2.  Background

### Alternative Local Revenues in Context

In sharp contrast with state and local governments generally, locally-sourced revenues for public education are characterized by an overwhelming reliance on property taxation and particularly limited experimentation with nontaxed-based revenue sources. Our data, as summarized in figures 1 and 2, indicate that in school year 2010–11 (fiscal year 2011), nontax-based revenue sources accounted for about 10 percent of total own-source revenues, including both fee-based sources (called charges) and other miscellaneous sources like donations.

Figure 1.

Per Pupil Alternative Revenues by Quartiles of Share of Students Eligible for Free Lunch, Fiscal Years 1992–2011.

Figure 1.

Per Pupil Alternative Revenues by Quartiles of Share of Students Eligible for Free Lunch, Fiscal Years 1992–2011.

Figure 2.

Per Pupil Total Local Own-Source Revenues by Quartile of Share of Students Eligible for Free Lunch, Fiscal Years 1992–2011.

Figure 2.

Per Pupil Total Local Own-Source Revenues by Quartile of Share of Students Eligible for Free Lunch, Fiscal Years 1992–2011.

Figure 3 shows the historical mix of state and local revenues by source for the past forty years. This chart includes revenue for all local governments (education and municipal), as well as state governments. Since the late 1970s the prominence of property taxation in the mix of overall revenues has decreased. Whereas tax-based revenues have remained somewhat stable in the overall mix, most of the change in revenues is due to the growth in charges and miscellaneous revenue. What are charges and miscellaneous revenue sources? These include a blend of fee-based revenues, as well as other revenues (miscellaneous) like interest earnings, sale of governmental property, and other nonspecified revenues. Charges and miscellaneous revenue swelled during the first tax revolt in the late 1970s and early 1980s (Fisher 2007) and have been relatively stable since then at just under 20 percent of total revenues.

Figure 3.

State and Local General Revenue by Source, 1977–2010. Source: The Urban Institute-Brookings Tax Policy Center; U.S. Census Bureau, Annual Survey of State and Local Government Finances (various years).

Figure 3.

State and Local General Revenue by Source, 1977–2010. Source: The Urban Institute-Brookings Tax Policy Center; U.S. Census Bureau, Annual Survey of State and Local Government Finances (various years).

The definition of charges and miscellaneous revenue differs by level of government, particularly when comparing state and local governments with school district organizations. In state governments, major current charges or fees include revenues from sources like higher education institutions, hospitals, and highways. Municipalities draw fees from more sources, including airports, sewerage, solid waste management, and parks and recreation, to name some of the larger categories. These are very different types of fees and sources of fees than are used by school district organizations. For education finance, where are fees in the mix of other nontax-based or alternative local revenues? Addonizio (1999) and Killeen (2007) use a three-point categorization to describe nontax-based local education revenues: Fixed revenue sources involve donor activities such as those from booster clubs, foundations, as well as private donations to education organizations; enterprise activities involve the entrepreneurial use of facilities, staff, and children to create revenue through activities like entrance fees, textbook fees, leasing of facility spaces, or vendor contracts. There is a third category of indirect revenue generation that comes from cooperative services, where districts generate revenue or conserve expenditures through cooperative agreements with organizations like regional education agencies. Cooperative services could involve revenues generated by districts for housing professional development activities or could involve the savings gleaned by exporting service delivery related to test scoring or special education services. The primary focus of this paper concerns charges or fee-based revenue as a particularly focused form of alternative and nontax-based local revenue.

### Fees and Alternative Revenues: Correlates

Estimates of variants of equation 1 are given in table 2, and table 3 includes estimates of the parameters of equation 2. The columns of the tables include separate regressions, each with a different dependent variable. Each dependent variable is the natural log of a different measure of per pupil revenue. In addition to total fees, transportation fees, revenues from school lunches, and alternative revenues, we include revenues from local sources and total revenues as dependent variables in order to provide some context for the results for fees and alternative revenues.

Table 2.
Impact of Downturns on School District Revenues – Year Effects Includeda,b
Dependent Variablec
Revenues  Revenues
from  from
Explanatory  Transportation School Alternative Local Total
Variable Fees Fees Lunch Revenues Sources Revenue
Log of Enrollment –0.0875*** –0.5211*** –0.2908*** –0.3589*** –0.4291*** –0.3430***
(0.0298) (0.1033) (0.0269) (0.0145) (0.0126) (0.0070)
Fraction special 0.4008*** –0.4363** 0.0909*** –0.1251*** 0.4882*** 0.1688***
education (0.0690) (0.2035) (0.0294) (0.0400) (0.0408) (0.0173)
Fraction eligible for 0.0633** –0.0130 –0.2244*** –0.0797** –0.0164 –0.0056
free lunch (0.0259) (0.0717) (0.0190) (0.0158) (0.0113) (0.0046)
Fraction Asian 1.4624*** –0.7776 1.3748*** 0.7087*** 0.8127*** 0.3824***
American (0.3935) (0.9642) (0.2037) (0.1568) (0.1124) (0.0584)
Fraction Native 0.3289*** –0.4298 –0.3148*** 0.3358*** 0.0601 0.0306
American (0.1274) (0.4747) (0.0967) (0.0711) (0.0521) (0.0245)
Fraction African –0.7436*** –0.8670** –0.4436*** –0.5810*** –0.2794*** –0.0484*
American (0.1543) (0.4134) (0.0929) (0.0779) (0.0561) (0.0288)
Fraction Hispanic –0.4685*** –0.8377* –0.7287*** –0.2064*** 0.1989*** 0.0659***
(0.1363) (0.4417) (0.0615) (0.0551) (0.0381) (0.0206)
State limit –0.0141 0.2530*** 0.0115* –0.1002*** 0.0176*** –0.0322***
(0.0099) (0.0672) (0.0069) (0.0072) (0.0047) (0.0025)
Local limit 0.1079*** –0.0464 0.0089* 0.0180*** –0.0192*** –0.0013
(0.0091) (0.0604) (0.0050) (0.0053) (0.0035) (0.0016)
Court ordered finance 0.0875*** 0.1801*** –0.0273*** –0.0562*** –0.0532*** 0.0166***
reform (0.0121) (0.0525) (0.0052) (0.0066) (0.0060) (0.0023)
Log of state formula –0.0023 0.0462*** 0.0142*** 0.0015 –0.2056*** 0.0464***
aid (0.0075) (0.0131) (0.0011) (0.0045) (0.0061) (0.0020)
Log of federal aid 0.0159** 0.0130 0.0505*** 0.0426*** 0.0285*** 0.0805***
(0.0070) (0.0243) (0.0052) (0.0048) (0.0037) (0.0019)
School Year Effectsd
1992–93 0.0480*** –0.0132 –0.0046 –0.0259*** 0.0370*** 0.0304***
(0.0075) (0.0279) (0.0033) (0.0048) (0.0026) (0.0013)
1993–94 0.0668*** 0.0049 –0.0096*** –0.0029 0.0551*** 0.0372***
(0.0087) (0.0327) (0.0036) (0.0050) (0.0029) (0.0014)
1994–95 0.0392*** –0.0997** 0.0031 0.0983*** 0.0665*** 0.0533***
(0.0092) (0.0404) (0.0041) (0.0052) (0.0032) (0.0015)
1995–96 0.0410*** –0.1101*** 0.0216*** 0.1451*** 0.0759*** 0.0702***
(0.0093) (0.0427) (0.0041) (0.0052) (0.0033) (0.0016)
1996–97 –0.0140 –0.1025** 0.0253*** 0.1714*** 0.1000*** 0.0928***
(0.0098) (0.0442) (0.0043) (0.0056) (0.0037) (0.0018)
1997–98 0.0181 –0.0987* 0.0553*** 0.2369*** 0.1379*** 0.1185***
(0.0112) (0.0505) (0.0048) (0.0059) (0.0039) (0.0019)
1998–99 0.0097 –0.0644 0.0825*** 0.2732*** 0.1524*** 0.1405***
(0.0109) (0.0509) (0.0049) (0.0061) (0.0042) (0.0020)
1999–00 0.0109 –0.0754 0.1008*** 0.3096*** 0.1674*** 0.1573***
(0.0112) (0.0519) (0.0052) (0.0063) (0.0045) (0.0021)
2000–01 –0.0046 –0.0795 0.1139*** 0.3933*** 0.2097*** 0.1937***
(0.0119) (0.0539) (0.0054) (0.0066) (0.0047) (0.0022)
2001–02 0.0128 –0.0157 0.1356*** 0.2763*** 0.2191*** 0.2055***
(0.0124) (0.0545) (0.0059) (0.0071) (0.0051) (0.0024)
2002–03 0.0336*** −0.0065 0.1238*** 0.2225*** 0.2143*** 0.2092***
(0.0134) (0.0571) (0.0063) (0.0075) (0.0055) (0.0026)
2003–04 –0.0222 0.0113 0.1261*** 0.1718*** 0.2561*** 0.2177***
(0.0156) (0.0585) (0.0067) (0.0078) (0.0055) (0.0026)
2004–05 –0.0116 0.0044 0.1277*** 0.2408*** 0.2285*** 0.2317***
(0.0158) (0.0608) (0.0067) (0.0078) (0.0060) (0.0027)
2005–06 –0.0097 0.0128 0.1317*** 0.3821*** 0.2632*** 0.2496***
(0.0165) (0.0621) (0.0072) (0.0081) (0.0063) (0.0029)
2006–07 –0.0166 0.0078 0.1348*** 0.4665*** 0.3019*** 0.2818***
(0.0170) (0.0620) (0.0074) (0.0082) (0.0063) (0.0029)
2007–08 –0.0325* –0.0166 0.1187*** 0.4221*** 0.3122*** 0.2940***
(0.0173) (0.0643) (0.0076) (0.0082) (0.0062) (0.0029)
2008–09 –0.0267 0.0203 0.1247*** 0.2991*** 0.3289*** 0.3033***
(0.0180) (0.0684) (0.0082) (0.0088) (0.0068) (0.0032)
2009–10 –0.0568*** 0.0055 0.0492*** 0.1724*** 0.2660** 0.2658**
(0.0195) (0.0727) (0.0097) (0.0098) (0.0080) (0.0036)
2010–11 –0.0328* –0.0395 0.0019 0.1434*** 0.2700*** 0.2638***
(0.0198) (0.0742) (0.0096) (0.0097) (0.0079) (0.0036)
Number of 194,491 24,423 233,016 260,058 260,802 260,863
observations
Number of 12,865 3,707 14,342 15,340 15,387 15,398
districts
Within R2 0.0078 0.0168 0.0603 0.1111 0.2258 0.6115
F Statistic 22.33 3.42 152.96 476.26 460.19 2116.59
Dependent Variablec
Revenues  Revenues
from  from
Explanatory  Transportation School Alternative Local Total
Variable Fees Fees Lunch Revenues Sources Revenue
Log of Enrollment –0.0875*** –0.5211*** –0.2908*** –0.3589*** –0.4291*** –0.3430***
(0.0298) (0.1033) (0.0269) (0.0145) (0.0126) (0.0070)
Fraction special 0.4008*** –0.4363** 0.0909*** –0.1251*** 0.4882*** 0.1688***
education (0.0690) (0.2035) (0.0294) (0.0400) (0.0408) (0.0173)
Fraction eligible for 0.0633** –0.0130 –0.2244*** –0.0797** –0.0164 –0.0056
free lunch (0.0259) (0.0717) (0.0190) (0.0158) (0.0113) (0.0046)
Fraction Asian 1.4624*** –0.7776 1.3748*** 0.7087*** 0.8127*** 0.3824***
American (0.3935) (0.9642) (0.2037) (0.1568) (0.1124) (0.0584)
Fraction Native 0.3289*** –0.4298 –0.3148*** 0.3358*** 0.0601 0.0306
American (0.1274) (0.4747) (0.0967) (0.0711) (0.0521) (0.0245)
Fraction African –0.7436*** –0.8670** –0.4436*** –0.5810*** –0.2794*** –0.0484*
American (0.1543) (0.4134) (0.0929) (0.0779) (0.0561) (0.0288)
Fraction Hispanic –0.4685*** –0.8377* –0.7287*** –0.2064*** 0.1989*** 0.0659***
(0.1363) (0.4417) (0.0615) (0.0551) (0.0381) (0.0206)
State limit –0.0141 0.2530*** 0.0115* –0.1002*** 0.0176*** –0.0322***
(0.0099) (0.0672) (0.0069) (0.0072) (0.0047) (0.0025)
Local limit 0.1079*** –0.0464 0.0089* 0.0180*** –0.0192*** –0.0013
(0.0091) (0.0604) (0.0050) (0.0053) (0.0035) (0.0016)
Court ordered finance 0.0875*** 0.1801*** –0.0273*** –0.0562*** –0.0532*** 0.0166***
reform (0.0121) (0.0525) (0.0052) (0.0066) (0.0060) (0.0023)
Log of state formula –0.0023 0.0462*** 0.0142*** 0.0015 –0.2056*** 0.0464***
aid (0.0075) (0.0131) (0.0011) (0.0045) (0.0061) (0.0020)
Log of federal aid 0.0159** 0.0130 0.0505*** 0.0426*** 0.0285*** 0.0805***
(0.0070) (0.0243) (0.0052) (0.0048) (0.0037) (0.0019)
School Year Effectsd
1992–93 0.0480*** –0.0132 –0.0046 –0.0259*** 0.0370*** 0.0304***
(0.0075) (0.0279) (0.0033) (0.0048) (0.0026) (0.0013)
1993–94 0.0668*** 0.0049 –0.0096*** –0.0029 0.0551*** 0.0372***
(0.0087) (0.0327) (0.0036) (0.0050) (0.0029) (0.0014)
1994–95 0.0392*** –0.0997** 0.0031 0.0983*** 0.0665*** 0.0533***
(0.0092) (0.0404) (0.0041) (0.0052) (0.0032) (0.0015)
1995–96 0.0410*** –0.1101*** 0.0216*** 0.1451*** 0.0759*** 0.0702***
(0.0093) (0.0427) (0.0041) (0.0052) (0.0033) (0.0016)
1996–97 –0.0140 –0.1025** 0.0253*** 0.1714*** 0.1000*** 0.0928***
(0.0098) (0.0442) (0.0043) (0.0056) (0.0037) (0.0018)
1997–98 0.0181 –0.0987* 0.0553*** 0.2369*** 0.1379*** 0.1185***
(0.0112) (0.0505) (0.0048) (0.0059) (0.0039) (0.0019)
1998–99 0.0097 –0.0644 0.0825*** 0.2732*** 0.1524*** 0.1405***
(0.0109) (0.0509) (0.0049) (0.0061) (0.0042) (0.0020)
1999–00 0.0109 –0.0754 0.1008*** 0.3096*** 0.1674*** 0.1573***
(0.0112) (0.0519) (0.0052) (0.0063) (0.0045) (0.0021)
2000–01 –0.0046 –0.0795 0.1139*** 0.3933*** 0.2097*** 0.1937***
(0.0119) (0.0539) (0.0054) (0.0066) (0.0047) (0.0022)
2001–02 0.0128 –0.0157 0.1356*** 0.2763*** 0.2191*** 0.2055***
(0.0124) (0.0545) (0.0059) (0.0071) (0.0051) (0.0024)
2002–03 0.0336*** −0.0065 0.1238*** 0.2225*** 0.2143*** 0.2092***
(0.0134) (0.0571) (0.0063) (0.0075) (0.0055) (0.0026)
2003–04 –0.0222 0.0113 0.1261*** 0.1718*** 0.2561*** 0.2177***
(0.0156) (0.0585) (0.0067) (0.0078) (0.0055) (0.0026)
2004–05 –0.0116 0.0044 0.1277*** 0.2408*** 0.2285*** 0.2317***
(0.0158) (0.0608) (0.0067) (0.0078) (0.0060) (0.0027)
2005–06 –0.0097 0.0128 0.1317*** 0.3821*** 0.2632*** 0.2496***
(0.0165) (0.0621) (0.0072) (0.0081) (0.0063) (0.0029)
2006–07 –0.0166 0.0078 0.1348*** 0.4665*** 0.3019*** 0.2818***
(0.0170) (0.0620) (0.0074) (0.0082) (0.0063) (0.0029)
2007–08 –0.0325* –0.0166 0.1187*** 0.4221*** 0.3122*** 0.2940***
(0.0173) (0.0643) (0.0076) (0.0082) (0.0062) (0.0029)
2008–09 –0.0267 0.0203 0.1247*** 0.2991*** 0.3289*** 0.3033***
(0.0180) (0.0684) (0.0082) (0.0088) (0.0068) (0.0032)
2009–10 –0.0568*** 0.0055 0.0492*** 0.1724*** 0.2660** 0.2658**
(0.0195) (0.0727) (0.0097) (0.0098) (0.0080) (0.0036)
2010–11 –0.0328* –0.0395 0.0019 0.1434*** 0.2700*** 0.2638***
(0.0198) (0.0742) (0.0096) (0.0097) (0.0079) (0.0036)
Number of 194,491 24,423 233,016 260,058 260,802 260,863
observations
Number of 12,865 3,707 14,342 15,340 15,387 15,398
districts
Within R2 0.0078 0.0168 0.0603 0.1111 0.2258 0.6115
F Statistic 22.33 3.42 152.96 476.26 460.19 2116.59

Notes:aAll regressions include district-specific fixed effects.

bIn parentheses are standard errors robust to heteroskedasticity and calculated by clustering by school district.

cAll dependent variables are measured as the natural log of the per pupil revenue measure.

dBolded year effects correspond to years which overlap with downturns as determined by the NBER Business Cycle Dating Committee.

*Statistically significant at the 10% level; **statistically significant at the 5% level; ***statistically significant at the 1% level.

Table 3.
Impact of Downturns on School District Revenues – State-Specific Downturn Variable Includeda,b
Dependent Variablec
Revenues  Revenues
from  from
Explanatory  Transportation School Alternative Local Total
Variable Fees Fees Lunch Revenues Sources Revenue
Log of Enrollment –0.0883*** –0.5306*** –0.1602*** –0.3786*** –0.4356*** –0.3487***
(0.0296) (0.1031) (0.0193) (0.0146) (0.0126) (0.0070)
Fraction special education 0.3893*** –0.3929** 0.0831*** –0.1544*** 0.4827*** 0.1643**
(0.0672) (0.1941) (0.0292) (0.0411) (0.0402) (0.0170)
lunch (0.0255) (0.0720) (0.0192) (0.0160) (0.0113) (0.0047)
Fraction Asian American 1.4496*** –0.6365 1.4622*** 0.8795*** 0.8666*** 0.4283***
(0.3919) (0.9628) (0.2052) (0.1592) (0.1133) (0.0583)
Fraction Native American 0.3231** –0.3440 –0.3059*** 0.3832*** 0.0781 0.0438*
(0.1272) (0.4759) (0.0970) (0.0714) (0.0521) (0.0246)
Fraction African American –0.7549*** –0.8518** –0.3813*** –0.4665*** –0.1663*** –0.0206
(0.1540) (0.4111) (0.0928) (0.0784) (0.0528) (0.0289)
Fraction Hispanic –0.4649*** –0.8109* –0.7082*** –0.1691*** 0.2121*** 0.0746***
(0.1363) (0.4399) (0.0614) (0.0550) (0.0381) (0.0207)
State limit –0.0109 0.1898*** 0.0073 –0.0920*** 0.0181*** –0.0329***
(0.0098) (0.0622) (0.0068) (0.0071) (0.0046) (0.0025)
Local limit 0.1080*** –0.0778 0.0046 0.0173*** –0.0183*** –0.0007
(0.0090) (0.0599) (0.0050) (0.0054) (0.0035) (0.0016)
Court ordered finance 0.0865*** 0.1583*** –0.0320*** –0.0481*** –0.0553*** 0.0136***
reform (0.0119) (0.0482) (0.0051) (0.0066) (0.0060) (0.0022)
Log of state formula aid –0.0040 0.0400*** 0.0150*** 0.0106** –0.2040*** 0.0481**
(0.0075) (0.0127) (0.0011) (0.0045) (0.0061) (0.0020)
Log of federal aid 0.0161** 0.0165 0.0479*** –0.0004 0.0220** 0.0757***
(0.0066) (0.0224) (0.0049) (0.0046) (0.0035) (0.0018)
Trend –0.0017 –0.0017 0.0291*** 0.0622*** 0.0282*** 0.0263***
(0.0017) (0.0077) (0.0008) (0.0010) (0.0007) (0.0003)
Trend squared –0.0001 0.0002 –0.0012*** –0.0024*** –0.0006*** –0.0005***
(0.0001) (0.0004) (0.00004) (0.0001) (0.0000) (0.0000)
Downturn indicator 0.0088*** 0.0492*** 0.0199*** –0.0021 0.0150*** 0.0109***
(0.0036) (0.0134) (0.0015) (0.0022) (0.0013) (0.0006)
Number of observations 194,491 24,423 233,016 260,058 260,802 260,863
Number of districts 12,865 3,707 14,342 15,340 15,387 15,398
Within R2 0.0068 0.0150 0.0532 0.0785 0.2217 0.6061
F Statistic 25.11 5.40 184.81 425.86 807.27 4221.07
Dependent Variablec
Revenues  Revenues
from  from
Explanatory  Transportation School Alternative Local Total
Variable Fees Fees Lunch Revenues Sources Revenue
Log of Enrollment –0.0883*** –0.5306*** –0.1602*** –0.3786*** –0.4356*** –0.3487***
(0.0296) (0.1031) (0.0193) (0.0146) (0.0126) (0.0070)
Fraction special education 0.3893*** –0.3929** 0.0831*** –0.1544*** 0.4827*** 0.1643**
(0.0672) (0.1941) (0.0292) (0.0411) (0.0402) (0.0170)
lunch (0.0255) (0.0720) (0.0192) (0.0160) (0.0113) (0.0047)
Fraction Asian American 1.4496*** –0.6365 1.4622*** 0.8795*** 0.8666*** 0.4283***
(0.3919) (0.9628) (0.2052) (0.1592) (0.1133) (0.0583)
Fraction Native American 0.3231** –0.3440 –0.3059*** 0.3832*** 0.0781 0.0438*
(0.1272) (0.4759) (0.0970) (0.0714) (0.0521) (0.0246)
Fraction African American –0.7549*** –0.8518** –0.3813*** –0.4665*** –0.1663*** –0.0206
(0.1540) (0.4111) (0.0928) (0.0784) (0.0528) (0.0289)
Fraction Hispanic –0.4649*** –0.8109* –0.7082*** –0.1691*** 0.2121*** 0.0746***
(0.1363) (0.4399) (0.0614) (0.0550) (0.0381) (0.0207)
State limit –0.0109 0.1898*** 0.0073 –0.0920*** 0.0181*** –0.0329***
(0.0098) (0.0622) (0.0068) (0.0071) (0.0046) (0.0025)
Local limit 0.1080*** –0.0778 0.0046 0.0173*** –0.0183*** –0.0007
(0.0090) (0.0599) (0.0050) (0.0054) (0.0035) (0.0016)
Court ordered finance 0.0865*** 0.1583*** –0.0320*** –0.0481*** –0.0553*** 0.0136***
reform (0.0119) (0.0482) (0.0051) (0.0066) (0.0060) (0.0022)
Log of state formula aid –0.0040 0.0400*** 0.0150*** 0.0106** –0.2040*** 0.0481**
(0.0075) (0.0127) (0.0011) (0.0045) (0.0061) (0.0020)
Log of federal aid 0.0161** 0.0165 0.0479*** –0.0004 0.0220** 0.0757***
(0.0066) (0.0224) (0.0049) (0.0046) (0.0035) (0.0018)
Trend –0.0017 –0.0017 0.0291*** 0.0622*** 0.0282*** 0.0263***
(0.0017) (0.0077) (0.0008) (0.0010) (0.0007) (0.0003)
Trend squared –0.0001 0.0002 –0.0012*** –0.0024*** –0.0006*** –0.0005***
(0.0001) (0.0004) (0.00004) (0.0001) (0.0000) (0.0000)
Downturn indicator 0.0088*** 0.0492*** 0.0199*** –0.0021 0.0150*** 0.0109***
(0.0036) (0.0134) (0.0015) (0.0022) (0.0013) (0.0006)
Number of observations 194,491 24,423 233,016 260,058 260,802 260,863
Number of districts 12,865 3,707 14,342 15,340 15,387 15,398
Within R2 0.0068 0.0150 0.0532 0.0785 0.2217 0.6061
F Statistic 25.11 5.40 184.81 425.86 807.27 4221.07

Notes:aAll regressions include district-specific fixed effects.

bIn parentheses are standard errors robust to heteroskedasticity and calculated by clustering by school district.

cAll dependent variables are measured as the natural log of the per pupil revenue measure.

*Statistically significant at the 10% level; **statistically significant at the 5% level; ***statistically significant at the 1% level.

The estimated effects of the common explanatory variables differ little between tables 2 and 3. For simplicity, our discussion will focus on the estimates in table 3.

#### Impact of Fiscal Pressures

We turn first to the relationship between nontax revenues and sources of fiscal pressure. The general lesson from these estimates is the lack of consistent evidence of increased use of nontax revenues in response to fiscal pressure. For example, our results do not provide a uniform picture of whether fees are used more heavily by districts constrained by fiscal institutions. Although fee revenues are higher in districts in states with limits on localities (local limit), they are not higher where limits on state governments are likely to affect state aid flows (state limit). Alternative revenues overall are lower in districts with state limits. Similar lack of clarity is evident in the coefficients on the court-ordered reform indicator (court-ordered finance reform), though that lack of clarity may be a result of the fact that court-ordered reforms have heterogeneous effects on districts.

We also see no evidence that fees and total alternative revenues are being used to compensate for declines in state formula aid or federal aid. Statistically significant coefficients on the aid variables are positive (state formula aid, federal aid), though they are quantitatively small with elasticities ranging from 0.01 to 0.04.

Our confidence in concluding there is no consistent evidence of alternative revenues being used to compensate for declines in traditional revenues is increased by the consistency of our estimates with existing estimates in the literature. For example, like Wassmer and Fisher (2002), we find revenues from fees and alternative revenues are higher in states where limits have been imposed on the ability of local governments to raise revenues. The final two columns of table 3 are also consistent with the general findings in the literature that, whereas local limits reduce own-source revenues, they do not reduce overall revenues. Only in states where limits are imposed on both state and local governments is revenue reduced.

Beyond the new description of fee revenue use among school districts, we also ask whether shifts in the use of fees may be attributed to the economic downturn. The results do not consistently show that fee revenue usage by school districts was countercyclical. The coefficients on the year dummies in table 2 indicate that fee revenues fell in the first year of each downturn and then rebounded in the second year. Because the timing of changes in revenue flows have not necessarily matched the timing of downturns, the coefficients for the years immediately after a downturn might also reflect district efforts to use alternative revenue sources to fill gaps—although the results show no consistent picture that user fee revenues are countercyclical. Total alternative revenues generally decline in the downturn and post-downturn years, with the exception being the first year of the downturn that started during the 2000–01 school year.

The lack of clarity in table 2 may be partly attributable to the fact that the timing of downturns is not uniform across all states. Table 3 presents the findings with controls for the timing of the economic downturn within states. The coefficients on that variable indicate fee revenues do move countercyclically and total alternative revenues exhibit no cyclical behavior, all else equal. The magnitude of the coefficient on the indicator in the fees regression is small (implying a percent change of less than one), however, indicating fee revenues fill little of the gap created by cyclical declines in state aid and other revenue sources. In summary, the results in table 3 provide limited evidence that growth in alternative and nontax revenues is particularly responsive to economic downturns among states hardest hit by the Great Recession.11

#### Other Correlates with Alternative Revenue Use

We find that larger districts (enrollment) make less use of fees and alternative revenues, with an elasticity of fees with respect to enrollment of about –0.09 and of alternative revenues with respect to enrollment of about –0.38. This finding parallels a common result in the literature on private contributions (Brunner and Sonstelie 2003; Downes and Steinman 2008), as well as the results in Killeen (2007). Our finding that fees are higher in districts in which a larger proportion of students are on IEPs (special education),12 however, runs counter to Killeen’s results. The difference between our results and Killeen’s probably reflects our ability to account for district-specific effects and to include a richer set of controls for student demographics.13

Fee revenues also appear to be higher in districts with larger proportions of students eligible for free school lunch. Wassmer and Fisher (2002) also found a negative relationship between income and fee revenues. Although in the Ohio districts analyzed by Killeen (2007) fee revenues were higher in districts with higher median household income, that result was attributable to the strength of the relationship between fee revenues and income in the poorest districts. We will return to that observation subsequently when we look explicitly at how fee usage varies by the economic status of a district’s student population.

Fee revenues are also higher in districts with larger proportions of Asian American and Native American students and lower in districts with larger proportions of African American and Hispanic students, all results that are generally consistent with Killeen (2007).

#### Narrowing the Scope: Disaggregating Revenue Sources and District Types

The results for transportation fees and revenues from school lunch generally look very similar to those for fees. The coefficients on the year dummies exhibit patterns of year-to-year change much like the coefficients on the year dummies for fees, and the coefficients on the Downturn dummy indicate these revenue sources exhibit some countercyclical movement—though both of these coefficients are quantitatively small. Neither of these revenue sources appears to be used more heavily when aid is reduced, and only transportation fees appear to be larger in districts in states with either state or local limits. Further, few districts generate revenues by charging for transportation, indicating that Wassmer and Fisher’s (2002) argument about the underutilization of this and other alternative revenue sources continues to be valid.

The absence of evidence in tables 2 and 3 that fees and other alternative revenues are responsive to fiscal constraints may be due to the fact that we do not separately analyze districts by the type of students they serve. The results in table 4, which provide estimates of equation 2 for each type of district, suggest there is little evidence that districts' use of fees in response to fiscal constraints differs by district type. Not surprisingly, because most districts are K–12, the results for these districts closely parallel the results in table 3. Somewhat surprisingly, high school districts do not appear to make more use of fees in the face of fiscal constraints. For these districts, the coefficients on the state and local limit dummies are both negative; neither of the coefficients on the aid variables differs from zero; and the coefficient on the downturn variable is positive for all alternative revenues, negative for fees, and effectively zero for both.

Table 4.
Impact of Downturns on School District Revenues by District Typea,b
Dependent Variablec
Alternative Alternative Alternative
Fees – Fees – Fees – Revenues – Revenues – Revenues –
Explanatory Elementary High School K–12 Elementary High School K–12
Variable Districts Districts Districts Districts Districts Districts
Log of Enrollment 0.0842 –0.1089 –0.1361*** –0.4423*** –0.3872*** –0.2993***
(0.0610) (0.1781) (0.0335) (0.0261) (0.0814) (0.0180)
Fraction special 0.3477*** 0.0605 0.4468*** –0.1356* –0.1605 –0.2754***
education (0.1162) (0.3170) (0.0550) (0.0743) (0.1164) (0.0354)
Fraction eligible 0.0286 –0.1843 0.0652** –0.2295*** –0.2350*** –0.0614***
Fraction Asian 0.8307*** 0.4005 1.6251*** 0.6766** 0.8168 1.0548***
American (0.5876) (1.9609) (0.5352) (0.2839) (0.6650) (0.2115)
Fraction Native 0.0584 0.0730 0.3990*** 0.0122 –0.9285** 0.8168***
American (0.1810) (0.6292) (0.1548) (0.1475) (0.3944) (0.1041)
Fraction African –0.5184 –0.9677* –0.9268*** –0.0727 –0.7858* –0.5538***
American (0.3215) (0.5336) (0.1883) (0.1406) (0.4484) (0.0997)
Fraction Hispanic 0.1927 –0.1315 –0.6295*** –0.2768*** –0.7024** –0.1768***
(0.3184) (0.9154) (0.1466) (0.0957) (0.3393) (0.0682)
State limit –0.1106* –0.1754* 0.0112 –0.0430 –0.0254 –0.0819***
(0.0577) (0.0976) (0.0101) (0.0437) (0.1986) (0.0071)
Local limit –0.0626** –0.2221*** 0.1140*** 0.0155 0.0080 0.0088
(0.0301) (0.0526) (0.0107) (0.0182) (0.0353) (0.0057)
Court ordered 0.7069*** –0.4041*** 0.0987*** –0.1014*** –0.0783 –0.0177***
finance reform (0.1240) (0.1366) (0.0116) (0.0253) (0.0587) (0.0069)
Log of state 0.0391*** –0.0172 0.0082*** 0.0141*** 0.0280** –0.0023*
formula aid (0.0120) (0.0182) (0.0021) (0.0043) (0.0125) (0.0012)
Log of federal aid –0.0143 –0.0260 0.0136* 0.0298*** –0.0231 –0.0297***
(0.0162) (0.0221) (0.0075) (0.0099) (0.0143) (0.0055)
Trend 0.0394*** 0.0695*** –0.0056*** 0.0799*** 0.0717*** 0.0608***
(0.0017) (0.0144) (0.0017) (0.0027) (0.0055) (0.0010)
Trend squared –0.0015*** –0.0019*** –0.0001 –0.0034*** –0.0026*** –0.0023***
(0.0003) (0.0006) (0.0001) (0.0001) (0.0003) (0.00004)
Downturn indicator 0.0195* –0.0345 0.0209*** 0.0269*** 0.0244 –0.0011
(0.0100) (0.0210) (0.0041) (0.0068) (0.0162) (0.0023)
Number of 19,059 5,763 159,578 43,491 8,470 190,670
observations
Number of 1,922 525 10,607 3,541 661 11,349
districts
Within R2 0.0496 0.0438 0.0111 0.0850 0.0701 0.0773
F Statistic 9.28 4.03 28.20 99.92 19.04 324.57
Dependent Variablec
Alternative Alternative Alternative
Fees – Fees – Fees – Revenues – Revenues – Revenues –
Explanatory Elementary High School K–12 Elementary High School K–12
Variable Districts Districts Districts Districts Districts Districts
Log of Enrollment 0.0842 –0.1089 –0.1361*** –0.4423*** –0.3872*** –0.2993***
(0.0610) (0.1781) (0.0335) (0.0261) (0.0814) (0.0180)
Fraction special 0.3477*** 0.0605 0.4468*** –0.1356* –0.1605 –0.2754***
education (0.1162) (0.3170) (0.0550) (0.0743) (0.1164) (0.0354)
Fraction eligible 0.0286 –0.1843 0.0652** –0.2295*** –0.2350*** –0.0614***
Fraction Asian 0.8307*** 0.4005 1.6251*** 0.6766** 0.8168 1.0548***
American (0.5876) (1.9609) (0.5352) (0.2839) (0.6650) (0.2115)
Fraction Native 0.0584 0.0730 0.3990*** 0.0122 –0.9285** 0.8168***
American (0.1810) (0.6292) (0.1548) (0.1475) (0.3944) (0.1041)
Fraction African –0.5184 –0.9677* –0.9268*** –0.0727 –0.7858* –0.5538***
American (0.3215) (0.5336) (0.1883) (0.1406) (0.4484) (0.0997)
Fraction Hispanic 0.1927 –0.1315 –0.6295*** –0.2768*** –0.7024** –0.1768***
(0.3184) (0.9154) (0.1466) (0.0957) (0.3393) (0.0682)
State limit –0.1106* –0.1754* 0.0112 –0.0430 –0.0254 –0.0819***
(0.0577) (0.0976) (0.0101) (0.0437) (0.1986) (0.0071)
Local limit –0.0626** –0.2221*** 0.1140*** 0.0155 0.0080 0.0088
(0.0301) (0.0526) (0.0107) (0.0182) (0.0353) (0.0057)
Court ordered 0.7069*** –0.4041*** 0.0987*** –0.1014*** –0.0783 –0.0177***
finance reform (0.1240) (0.1366) (0.0116) (0.0253) (0.0587) (0.0069)
Log of state 0.0391*** –0.0172 0.0082*** 0.0141*** 0.0280** –0.0023*
formula aid (0.0120) (0.0182) (0.0021) (0.0043) (0.0125) (0.0012)
Log of federal aid –0.0143 –0.0260 0.0136* 0.0298*** –0.0231 –0.0297***
(0.0162) (0.0221) (0.0075) (0.0099) (0.0143) (0.0055)
Trend 0.0394*** 0.0695*** –0.0056*** 0.0799*** 0.0717*** 0.0608***
(0.0017) (0.0144) (0.0017) (0.0027) (0.0055) (0.0010)
Trend squared –0.0015*** –0.0019*** –0.0001 –0.0034*** –0.0026*** –0.0023***
(0.0003) (0.0006) (0.0001) (0.0001) (0.0003) (0.00004)
Downturn indicator 0.0195* –0.0345 0.0209*** 0.0269*** 0.0244 –0.0011
(0.0100) (0.0210) (0.0041) (0.0068) (0.0162) (0.0023)
Number of 19,059 5,763 159,578 43,491 8,470 190,670
observations
Number of 1,922 525 10,607 3,541 661 11,349
districts
Within R2 0.0496 0.0438 0.0111 0.0850 0.0701 0.0773
F Statistic 9.28 4.03 28.20 99.92 19.04 324.57

Notes:aAll regressions include district-specific fixed effects.

bIn parentheses are standard errors robust to heteroskedasticity and calculated by clustering by school district.

cAll dependent variables are measured as the natural log of the per pupil revenue measure.

*Statistically significant at the 10% level; **statistically significant at the 5% level; ***statistically significant at the 1% level.

### Heterogeneity in the Use of Fees and Alternative Revenues

Figures 1 and 5 offer evidence that the patterns of change in the use of alternative revenues and fees have not been the same for districts with different levels of student poverty. The results in tables 5 and 6 were generated to shed some light on the sources of this heterogeneity. Some of the results in these tables do suggest potential sources of heterogeneity. For example, the negative coefficient on fraction free lunch in the fees model for the highest poverty districts helps explain why fee revenues have fallen in these districts over the past several years. The sharp drops in alternative revenues in districts in the second and third quartiles could be attributable, in part, to the strength of the negative relationship between alternative revenues and federal aid.

Table 5.
Impact of Downturns on School District Revenues – Districts Grouped by Student Povertya Dependent Variable: Natural Log of Per Pupil Fee Revenueb
First QuartileSecond QuartileThird QuartileFourth Quartile
Explanatoryof Fractionof Fractionof Fractionof Fraction
VariableFree LunchFree LunchFree LunchFree Lunch
Log of Enrollment –0.0455 –0.0940 –0.1385** –0.2249***
(0.0723) (0.0606) (0.0568) (0.0424)
Fraction special education 0.2471** 0.1677 0.3190*** 0.0164
(0.1126) (0.1460) (0.0940) (0.0793)
(0.1717) (0.1671) (0.1147) (0.0701)
Fraction Asian American 0.3266 1.8712* –0.1009 0.4200
(0.5451) (1.0922) (0.8444) (0.5473)
Fraction Native American 0.4024 0.6757 1.2812*** –0.0205
(0.5597) (0.4291) (0.3021) (0.1214)
Fraction African American –0.6232** –1.1233** –1.4168*** –0.3864*
(0.2891) (0.4650) (0.3596) (0.2220)
Fraction Hispanic 0.8765** 0.7104* –1.1229*** –0.8485***
(0.3839) (0.3985) (0.2519) (0.1715)
State limit –0.0428*** 0.0972*** –0.0950*** –0.0976**
(0.0154) (0.0205) (0.0206) (0.0389)
Local limit 0.0112 0.1433*** 0.0885*** 0.0578***
(0.0169) (0.0170) (0.0178) (0.0168)
Court ordered finance reform –0.1716*** 0.1583*** 0.1360*** 0.0413
(0.0324) (0.0205) (0.0203) (0.0257)
Log of state formula aid –0.0317* 0.0037 –0.0007 –0.0428***
(0.0180) (0.0127) (0.0125) (0.0134)
Log of federal aid 0.0072 0.0226* –0.0012 0.0197
(0.0105) (0.0122) (0.0135) (0.0142)
Trend 0.0095** 0.0040 –0.0060* 0.0054*
(0.0038) (0.0041) (0.0034) (0.0031)
Trend squared 0.0003 –0.0011*** –0.0001 –0.0003*
(0.0002) (0.0002) (0.0001) (0.0001)
Downturn indicator –0.0350*** 0.0605*** 0.0174** –0.0004
(0.0066) (0.0088) (0.0078) (0.0066)
Number of observations 44,218 50,868 52,654 46,751
Number of districts 7,363 6,761 7,320 5,384
Within R2 0.0216 0.0133 0.0158 0.0103
F Statistic 17.09 14.39 13.37 8.9
First QuartileSecond QuartileThird QuartileFourth Quartile
Explanatoryof Fractionof Fractionof Fractionof Fraction
VariableFree LunchFree LunchFree LunchFree Lunch
Log of Enrollment –0.0455 –0.0940 –0.1385** –0.2249***
(0.0723) (0.0606) (0.0568) (0.0424)
Fraction special education 0.2471** 0.1677 0.3190*** 0.0164
(0.1126) (0.1460) (0.0940) (0.0793)
(0.1717) (0.1671) (0.1147) (0.0701)
Fraction Asian American 0.3266 1.8712* –0.1009 0.4200
(0.5451) (1.0922) (0.8444) (0.5473)
Fraction Native American 0.4024 0.6757 1.2812*** –0.0205
(0.5597) (0.4291) (0.3021) (0.1214)
Fraction African American –0.6232** –1.1233** –1.4168*** –0.3864*
(0.2891) (0.4650) (0.3596) (0.2220)
Fraction Hispanic 0.8765** 0.7104* –1.1229*** –0.8485***
(0.3839) (0.3985) (0.2519) (0.1715)
State limit –0.0428*** 0.0972*** –0.0950*** –0.0976**
(0.0154) (0.0205) (0.0206) (0.0389)
Local limit 0.0112 0.1433*** 0.0885*** 0.0578***
(0.0169) (0.0170) (0.0178) (0.0168)
Court ordered finance reform –0.1716*** 0.1583*** 0.1360*** 0.0413
(0.0324) (0.0205) (0.0203) (0.0257)
Log of state formula aid –0.0317* 0.0037 –0.0007 –0.0428***
(0.0180) (0.0127) (0.0125) (0.0134)
Log of federal aid 0.0072 0.0226* –0.0012 0.0197
(0.0105) (0.0122) (0.0135) (0.0142)
Trend 0.0095** 0.0040 –0.0060* 0.0054*
(0.0038) (0.0041) (0.0034) (0.0031)
Trend squared 0.0003 –0.0011*** –0.0001 –0.0003*
(0.0002) (0.0002) (0.0001) (0.0001)
Downturn indicator –0.0350*** 0.0605*** 0.0174** –0.0004
(0.0066) (0.0088) (0.0078) (0.0066)
Number of observations 44,218 50,868 52,654 46,751
Number of districts 7,363 6,761 7,320 5,384
Within R2 0.0216 0.0133 0.0158 0.0103
F Statistic 17.09 14.39 13.37 8.9

Notes:aAll regressions include district-specific fixed effects.

bIn parentheses are standard errors robust to heteroskedasticity and calculated by clustering by school district.

*Statistically significant at the 10% level; **statistically significant at the 5% level; ***statistically significant at the 1% level.

Table 6.
Impact of Downturns on School District Revenues – Districts Grouped by Student Povertya Dependent Variable: Natural Log of Per Pupil Alternative Revenuesb
First QuartileSecond QuartileThird QuartileFourth Quartile
Explanatoryof Fractionof Fractionof Fractionof Fraction
VariableFree LunchFree LunchFree LunchFree Lunch
Log of Enrollment –0.5336*** –0.3742*** –0.3677*** –0.4570***
(0.0432) (0.0273) (0.0246) (0.0284)
Fraction special education 0.0739 –0.2908*** –0.1875 –0.1084**
(0.0515) (0.0700) (0.0940) (0.0553)
(0.1213) (0.0922) (0.0567) (0.0879)
Fraction Asian American 0.5102* 1.1905*** 0.7162** 0.3117
(0.2739) (1.3724) (0.3289) (0.2970)
Fraction Native American 0.1826 0.4858** 0.7411*** 0.3383***
(0.1573) (0.2222) (0.1567) (0.0792)
Fraction African American –0.2476 0.2339 –0.3447* –0.5737***
(0.1562) (0.2212) (0.1939) (0.1308)
Fraction Hispanic –0.1752 0.0537 0.0275 0.0254
(0.1586) (0.1493) (0.0945) (0.0902)
State limit –0.0360** –0.0356*** –0.0827*** –0.1726***
(0.0157) (0.0128) (0.0141) (0.0338)
Local limit –0.0596*** 0.0243*** 0.0375*** 0.1073***
(0.0104) (0.0091) (0.0098) (0.0122)
Court ordered finance reform –0.0298** –0.0495*** –0.0276** –0.0669***
(0.0146) (0.0107) (0.0116) (0.0169)
Log of state formula aid 0.0421*** 0.0016 0.0093 –0.0103
(0.0111) (0.0073) (0.0080) (0.0113)
Log of federal aid –0.0037 –0.0175** –0.0187** –0.0034
(0.0079) (0.0078) (0.0135) (0.0116)
Trend 0.0622*** 0.0539*** 0.0487*** 0.0694***
(0.0038) (0.0025) (0.0019) (0.0020)
Trend squared –0.0021*** –0.0018*** –0.0017*** –0.0028***
(0.0001) (0.0001) (0.0001) (0.0001)
Downturn indicator 0.0032 0.0033 –0.0098** –0.0141***
(0.0043) (0.0043) (0.0042) (0.0051)
Number of observations 61,629 64,426 67,461 66,542
Number of districts 9,421 8,320 8,788 6,825
Within R2 0.0915 0.0495 0.0578 0.0922
F Statistic 102.33 75.68 101.82 153.1
First QuartileSecond QuartileThird QuartileFourth Quartile
Explanatoryof Fractionof Fractionof Fractionof Fraction
VariableFree LunchFree LunchFree LunchFree Lunch
Log of Enrollment –0.5336*** –0.3742*** –0.3677*** –0.4570***
(0.0432) (0.0273) (0.0246) (0.0284)
Fraction special education 0.0739 –0.2908*** –0.1875 –0.1084**
(0.0515) (0.0700) (0.0940) (0.0553)
(0.1213) (0.0922) (0.0567) (0.0879)
Fraction Asian American 0.5102* 1.1905*** 0.7162** 0.3117
(0.2739) (1.3724) (0.3289) (0.2970)
Fraction Native American 0.1826 0.4858** 0.7411*** 0.3383***
(0.1573) (0.2222) (0.1567) (0.0792)
Fraction African American –0.2476 0.2339 –0.3447* –0.5737***
(0.1562) (0.2212) (0.1939) (0.1308)
Fraction Hispanic –0.1752 0.0537 0.0275 0.0254
(0.1586) (0.1493) (0.0945) (0.0902)
State limit –0.0360** –0.0356*** –0.0827*** –0.1726***
(0.0157) (0.0128) (0.0141) (0.0338)
Local limit –0.0596*** 0.0243*** 0.0375*** 0.1073***
(0.0104) (0.0091) (0.0098) (0.0122)
Court ordered finance reform –0.0298** –0.0495*** –0.0276** –0.0669***
(0.0146) (0.0107) (0.0116) (0.0169)
Log of state formula aid 0.0421*** 0.0016 0.0093 –0.0103
(0.0111) (0.0073) (0.0080) (0.0113)
Log of federal aid –0.0037 –0.0175** –0.0187** –0.0034
(0.0079) (0.0078) (0.0135) (0.0116)
Trend 0.0622*** 0.0539*** 0.0487*** 0.0694***
(0.0038) (0.0025) (0.0019) (0.0020)
Trend squared –0.0021*** –0.0018*** –0.0017*** –0.0028***
(0.0001) (0.0001) (0.0001) (0.0001)
Downturn indicator 0.0032 0.0033 –0.0098** –0.0141***
(0.0043) (0.0043) (0.0042) (0.0051)
Number of observations 61,629 64,426 67,461 66,542
Number of districts 9,421 8,320 8,788 6,825
Within R2 0.0915 0.0495 0.0578 0.0922
F Statistic 102.33 75.68 101.82 153.1

Notes:aAll regressions include district-specific fixed effects.

bIn parentheses are standard errors robust to heteroskedasticity and calculated by clustering by school district.

*Statistically significant at the 10% level; **statistically significant at the 5% level; ***statistically significant at the 1% level.

These results also tend to confirm findings in the literature. For example, for most districts fee revenues are higher when limits on local revenue-raising ability are in place. Districts with the highest average poverty exhibit a strong negative relationship between fee revenues and this measure of economic well-being, whereas no such relationship is apparent for the lowest poverty districts. These findings echo results in Killeen (2007).

At the same time, the results in tables 5 and 6 might raise as many questions as they address. For example, there is the surprising result that districts with the lowest level of student poverty appear to make less use of fees in states with court-ordered finance reform. This result stands in sharp contrast to the findings of Brunner and Sonstelie (1997, 2003), Brunner and Imazeki (2005), and Downes and Steinman (2008) on the growth of private contributions in districts constrained by post-reform finance systems. For the same districts, in the alternative revenues model, the negative coefficient on the local limit is also surprising. Although both of these results might be attributable to better access to additional property tax revenues relative to other districts,14 further exploration of these counterintuitive results is clearly necessary.

The estimates in tables 5 and 6 also fail to provide a uniform picture of whether differential use of fees and other alternative revenues accentuates inequalities during downturns. For example, for the lowest poverty districts, the negative coefficient on the downturn indicator in the fees regression would appear to indicate fee revenues in these districts move cyclically. In the fees regression, however, the coefficients on this indicator are positive for the next two quartiles. And, in the alternative revenues regression, the coefficients on the downturn indicators are consistent with alternative revenues moving cyclically in the districts with the highest levels of student poverty. Thus, the preponderance of evidence from these regressions is consistent with our interpretation of figures 1 and 5—differential access to fee revenues and other alternative revenues during downturns may slightly accentuate inequities in K–12 education spending.

## 5.  Discussion

### Summary of Major Findings

One of the major contributions of this work is that it offers the first long-term portrait of the use of alternative local revenues in the funding of public education. The use of nontax revenues in education contrasts sharply with their use among state and local governments. We have demonstrated that alternative revenues provide a small share of local education revenues, and they have increased quite minimally since the early 1990s. For example, between 1991 and 2010, fee revenue grew by 47 percent to just under \$70 per pupil. This contrasts with total revenue growth during this period of 53 percent and alternative (nontax and nonfee) revenue growth of 87 percent.

We started this paper with the assumption that the Great Recession would have stimulated a substantial fiscal shock among local school districts, similar to the period of change following the national tax revolts in the late 1970s. We assumed that local districts would respond to the downturn with some movement away from reliance on the property tax and toward alternative revenues like fees. Not only was this shift minimal in total fee revenue, we found only a mild association with the downturn. For both the 2001 and 2007 recessions, our results indicate an immediate decline in fee revenues in the year following the onset of the recession, followed by a small increase.

### Prospects for Reducing Reliance on the Property Tax

Despite the conventional expectation that school districts might move toward user fees in public education finance, they have been very slow to do so. Even the fiscal stress resulting from recessions has not substantively shifted revenues away from property taxation. We are thus left with the question: What events might stimulate school districts to move away from property taxation?

State and local governments in general appeared to migrate toward fee-based revenues following periods of tax revolts and TELs. Although more research is needed, this suggests large fiscal shocks could stimulate the increased use of non-tax revenues by school districts. The findings in this paper, however, signal that school districts may be quite resistant to local revenue diversification even during major economic downturns. Historically, the property tax has provided a stable source of revenue for school districts, and the added fiscal pressure associated with downturns was not enough to trigger revenue diversification by school districts. We examine whether the use of alternative local revenues could occur as long as three years after the Great Recession, but find very little support for this idea. Similarly, there is no strong evidence of revenue restructuring or diversification as a result of the 2001 recession.15

The literature suggests fiscal pressures created by contractions of state aid or responses to TELs might trigger a shift toward fees or other nontax revenues. But our findings suggest temporary fiscal pressures, such as those created by downturns, may simply not be enough to motivate fee usage. Even permanent fiscal pressures, such as those that may be created by property tax limits, might only induce limited revenue diversification.

One reason for the slow rate of adoption of fees and charges by school districts might be that their opportunities for fee-based financing are quite limited. Both the courts and public opinion tend to agree that the core education functions of public schools, and most of their budgets, are not amenable to the collection of fees.16 Although there is support for the use of fees for extracurricular activities, these activities generally account for a minor portion of education spending. Cutbacks in spending on extracurricular activities as a result of recessions may further reduce the opportunity for fee-based revenues.

Irrespective of the persistent public support for funding education through anything but the property tax, and despite the size of the Great Recession and the extended recovery, our findings point to very limited movement toward the use of alternative local revenues, such as fees. Although local governments other than school districts, including institutions of higher education, readily rely on fee revenues to supplement and supplant property taxation, no such change seems underfoot in public education, at least for now.

## Notes

1.

In 2006 the Census Bureau widened survey items to include private contributions, rents, fines, and property sales.

2.

Growth of nontax revenue may be an explanation for Dye, McGuire, and McMillen’s (2005) findings that, in Illinois school districts subject to a tax cap, growth in operating spending had declined less than had growth in school district tax revenues, and the different changes in growth rates were not attributable to compensating changes in state aid.

3.

The constructed cities database, which is now called the Fiscally Standardized Cities database, was created by the authors to facilitate comparisons of finances across the largest cities in the U.S. The database accounts for differences across cities in tax and spending responsibilities. More detail on the database can be found at www.lincolninst.edu/subcenters/fiscally-standardized-cities/.

4.

For details on the extent of coverage in fiscal years 1993 and 1994, see www.census.gov/govs/www/school9296doc.html.

5.

We had hoped to create measures of district revenues from sales and from entrepreneurial activity, but doing so was not possible since, in fiscal year 2006, the Census Bureau added to the F-33 survey items on private contributions, rents and royalties, fines and forfeits, and sales of property. Prior to that year, these items were probably part of each district's miscellaneous revenues, though some districts may have included these items in their reports of other, nonfee alternative revenue sources. As a result, we can only be confident that our measures of fee revenue and the total of local, nontax revenue are consistently reported in all years.

6.

Data on each district’s racial/ethnic composition, age composition, and per capita income were drawn from the Decennial Censuses of 1990, 2000, and 2010. Because the long-form Census was discontinued after the 2000 Census, no information on district per capita income was available in the 2010 Census. In creating our final data set, we matched each school year to the nearest Decennial Census year.

7.

Because Springer, Liu, and Guthrie (2009) find little evidence that there is a difference in the effects of court mandated equity and adequacy reforms, we chose to group together all court mandates.

9.

In Illinois, residents of individual counties can choose to impose limits. As a result, we coded the timing of limits in counties in Illinois using the August 2012 version of the History of PTELL map provided by the Property Tax Division of the Illinois Department of Revenue.

10.

As Owyang, Piger, and Wall (2005) note, gross state product is only available on an annual basis and thus gross state product cannot be used to date state cycles.

11.

If the services for which fees are charged are highly price elastic, then districts may be more willing to add fees than to increase existing fees. As a result, limiting our analysis to only those districts with fee revenues, as we do in tables 3 and 4, might understate the degree of responsiveness of fees and other alternative revenues. To explore this possibility, we estimated variants of equations 1 and 2 in which the dependent variable was the level of per pupil revenues from fees or other alternative revenue sources or was a dummy variable indicating whether a district had any revenues of a particular type. The implications of those estimates, which are available upon request, are consistent with the implications of the estimates in tables 2 and 3.

12.

Every public school student who receives special education must have an Individualized Education Program (IEP).

13.

When we estimate models that use the shorter panel for which fraction limited English proficient is available, we find the coefficient on fraction limited English proficient is consistently negative and significant, a finding that matches Killeen (2007).

14.

This possibility is suggested by the finding that higher income districts are more likely to override local limits, which dates back to Reschovsky and Schwartz (1992) and has been confirmed by Bradbury and Zhao (2009) and others.

15.

Our failure to find evidence of increased use of fees could be a result of the shifting of service provision from schools to other local governments in the face of fiscal constraints. Such shifting might happen if public perceptions of schools levying fees differ from perceptions of other local governments levying fees, even if those fees are for the same services. However, Downes (2007) suggests there is no evidence of such shifting in the face of fiscal constraints.

16.

In contrast, municipal and county governments generally provide a broader range of services where it is acceptable to raise revenues by charging fees for services.

## Acknowledgments

Thanks to Amy Ellen Schwartz, Eric Brunner, two anonymous reviewers, and participants’ comments at the AEFP annual conference and the Lincoln Institute of Land Policy’s Property Tax and the Financing of K–12 Education conference. Also thanks to Mark Meiselbach for his assistance with data work. All remaining errors of commission and omission are the authors’.

## REFERENCES

American Association of School Administrators (AASA)
.
2013
.
Surviving sequester, round one: Schools detail impact of sequester cuts
. Available http://blogs.edweek.org/edweek/campaign-k-12/Surviving%20Sequester.pdf. Accessed 17 June 2014.
,
Michael
.
1999
.
New revenues for public schools: Alternatives to broad based taxes
. In
Selected papers in school finance, 1997–1999
, edited by
William J.
Fowler
, pp.
85
110
.
Washington, DC
:
National Center for Education Statistics
.
Alm
,
James
,
Robert D.
Buschman
, and
David L.
Sjoquist
.
2009
.
Economic conditions and state and local education revenue.
Public Budgeting & Finance
29
(
3
):
28
51
. doi:10.1111/j.1540-5850.2009.00935.x
Alm
,
James
,
Robert D.
Buschman
, and
David L.
Sjoquist
.
2011
.
Rethinking local government reliance on the property tax.
Regional Science and Urban Economics
41
(
4
):
320
331
. doi:10.1016/j.regsciurbeco.2011.03.006
,
Katherine
, and
Bo
Zhao
.
2009
.
Measuring non-school fiscal disparities among municipalities.
National Tax Journal
62
(
1
):
25
56
.
Bry
,
Gerhard
, and
Charlotte
Boschan
.
1971
.
Cyclical analysis of time series: Selected procedures and computer programs
.
New York
:
Columbia University Press and the National Bureau of Economic Research
.
Brunner
,
Eric J.
, and
Jon
Sonstelie
.
1997
.
Coping with Serrano: Private contributions to California’s public schools
.
In Proceedings of the 89th Annual Conference on Taxation
,
Boston, MA
, pp.
372
381
.
Brunner
,
Eric J.
, and
Jon
Sonstelie
.
2003
.
School finance reform and voluntary fiscal federalism.
Journal of Public Economics
87
(
9–10
):
2157
2185
. doi:10.1016/S0047-2727(02)00040-3
Brunner
,
Eric J.
, and
Jennifer
Imazeki
.
2005
.
Fiscal stress and voluntary contributions to public schools
. In
Developments in school finance, 2004
, edited by
William J.
Fowler
, pp.
39
54
.
Washington, DC
:
National Center for Education Statistics
.
Caroll
,
Deborah A.
,
Robert J.
Eger
III
, and
Justin
Marlowe
.
2003
.
Managing local intergovernmental revenues: The imperative of diversification.
26
(
13
):
1495
1518
Chakrabarti
,
Rajashri
,
Amy
Farber
, and
Max
Livingston
.
2013
.
Historical echoes: The changing face of education in the United States
.
Liberty Street Economics Blogs
.
New York
:
Federal Reserve Bank of New York
, 27 September. Available http://libertystreeteconomics.newyorkfed.org/2013/09/historical-echoes-the-changing-face-of-education-in-the-united-states.html. Accessed 28 July 2014.
Chernick
,
Howard
,
Langley
, and
Andrew
Reschovsky
.
2011
.
Revenue diversification and the financing of large American central cities.
Public Finance and Management
11
(
2
):
138
159
.
Coleman
,
Henry A.
2014
.
Non-traditional public school funding sources: Trends, issues, and outlooks
. In
Education, land, and location
,
edited by Gregory K. Ingram and Daphne A. Kenyon
, pp.
187
209
.
Cambridge, MA
:
Lincoln Institute of Land Policy
.
,
Lucy
.
2012
.
The impact of the Great Recession on local property taxes. Unpublished paper, The Nelson A. Rockefeller Institute of Government
,
Albany, NY
.
,
Lucy
, and
Donald
Boyd
.
2012
.
Sales tax revenues show slowest growth in the last two years. Unpublished paper, The Nelson A. Rockefeller Institute of Government
,
Albany, NY
.
Downes
,
Thomas A.
2007
.
Do non-school resources substitute for school resources? A review of the evidence. Unpublished paper, Tufts University
.
Downes
,
Thomas A.
, and
Jason
Steinman
.
2008
.
Alternative revenue generation in Vermont public schools: Raising funds outside the tax base to support public education. Unpublished paper, Tufts University
.
Dye
,
Richard F.
,
Therese J.
McGuire
, and
Daniel P.
McMillen
.
2005
.
Are property tax limitations more binding over time?
National Tax Journal
58
(
2
):
215
225
.
Figlio
,
David N.
1997
.
Did the ‘tax revolt’ reduce school performance?
Journal of Public Economics
65
(
3
):
245
269
. doi:10.1016/S0047-2727(97)00015-7
Fisher
,
Ronald C.
2007
.
State and local public finance
, 3rd ed.
Mason, OH
:
Thomson South-Western
.
Hamilton
,
James D.
1989
.
A new approach to the economic analysis of nonstationary time series and the business cycle.
Econometrica
57
(
2
):
357
384
. doi:10.2307/1912559
Harris
,
Benjamin H.
, and
Yuri
.
2013
.
State and local governments in economic recoveries: The recovery is different. Unpublished paper, Urban-Brookings Tax Policy Center, The State and Local Finance Initiative
.
Hendrick
,
Rebecca
.
2002
.
Revenue diversification: Fiscal illusion or flexible financial management.
Public Budgeting & Finance
22
(
4
):
52
72
. doi:10.1111/1540-5850.00089
Joyce
,
Philip G.
, and
Daniel R.
Mullins
.
1991
.
The changing fiscal structure of the state and local public sector: The impact of tax and expenditure limitations.
51
(
3
):
240
253
. doi:10.2307/976948
Killeen
,
Kieran
.
2007
.
How the media misleads the story of school consumerism: A perspective from school finance.
Peabody Journal of Education
82
(
1
):
32
62
. doi:10.1080/01619560709336536
Lutz
,
Byron
.
2008
.
The connection between house price appreciation and property tax revenues.
National Tax Journal
61
(
3
):
555
572
.
Lutz
,
Byron
,
Raven
Molloy
, and
Hui
Shan
.
2011
.
The housing crisis and state and local government tax revenue: Five channels.
Regional Science and Urban Economics
41
(
4
):
306
319
. doi:10.1016/j.regsciurbeco.2011.03.009
Maxwell
,
Leslie A.
2013
. Districts asking parents to pony up for bus services.
Education Week Blog
, 26 June. Available http://blogs.edweek.org/edweek/District_Dossier/2013/06/districts_ask_parents_to_pony_.html?r=444682445&qs=Maxwell. Accessed 28 July 2014.
McCubbins
,
Mathew D.
, and
Ellen
Moule
.
2010
.
Making mountains of debt out of molehills: The pro-cyclical implications of tax and expenditure limitations.
National Tax Journal
63
(
3
):
603
621
.
McGuire
,
Therese J.
1999
.
Proposition 13 and its offspring: For good or for evil?
National Tax Journal
52
(
1
):
129
138
.
Mullins
,
Daniel R.
2004
.
Tax and expenditure limitations and the fiscal response of local government: Asymmetric intra-local fiscal effects.
Public Budgeting & Finance
24
(
4
):
111
147
. doi:10.1111/j.0275-1100.2004.00350.x
Mullins
,
Daniel R.
, and
Philip G.
Joyce
.
1996
.
Tax and expenditure limitations and state and local fiscal structure: An empirical assessment.
Public Budgeting & Finance
16
(
1
):
75
101
. doi:10.1111/1540-5850.01061
Mullins
,
Daniel R.
, and
Bruce A.
Wallin
.
2004
.
Tax and expenditure limitations: Introduction and overview.
Public Budgeting & Finance
24
(
4
):
2
15
. doi:10.1111/j.0275-1100.2004.00344.x
National Education Access Network
.
2014
.
National education access network
. Available www.schoolfunding.info/. Accessed 6 September 2013.
Netzer
,
Dick
.
1992
.
Differences in reliance on user charges by American state and local governments.
Public Finance Review
20
(
4
):
499
511
. doi:10.1177/109114219202000407
Owyang
,
Michael T.
,
Jeremy
Piger
, and
Howard J.
Wall
.
2005
.
Business cycle phases in U.S. states.
Review of Economics and Statistics
87
(
4
):
604
616
. doi:10.1162/003465305775098198
Reschovsky
,
Andrew
.
2004
.
The impact of state government fiscal crises on local governments and schools.
State and Local Government Review
36
(
2
):
86
102
. doi:10.1177/0160323X0403600201
Reschovsky
,
Andrew
, and
Amy Ellen
Schwartz
.
1992
.
Evaluating the success of need-based state aid in the presence of property tax limitations.
Public Finance Review
20
(
4
):
483
498
. doi:10.1177/109114219202000406
,
Ronald J.
2003
.
Did the property tax revolt affect local public education? Evidence from panel data.
Public Finance Review
31
(
1
):
91
121
. doi:10.1177/1091142102239136
Sjoquist
,
David
,
Mary Beth
Walker
, and
Sally
Wallace
.
2005
.
Estimating differential responses to local fiscal conditions: A mixture model analysis.
Public Finance Review
33
(
1
):
36
61
. doi:10.1177/1091142104270656
Skidmore
,
Mark
, and
Mehmet S.
Tosun
.
2011
.
Property value assessment growth limits, tax base erosion, and regional in-migration.
Public Finance Review
39
(
2
):
256
287
. doi:10.1177/1091142110381636
Springer
,
Matthew G.
,
Keke
Liu
, and
James W.
Guthrie
.
2009
.
The impact of school finance litigation on resource distribution: A comparison of court‐mandated equity and adequacy reforms.
Education Economics
17
(
4
):
421
444
. doi:10.1080/09645290802069269
The Pew Charitable Trusts
.
2012
.
The local squeeze: Falling revenues and growing demands for services challenge cities, counties, and school districts
.
Washington, DC
:
The Pew Charitable Trusts
.
Verstegen
,
Deborah
.
2011
.
A quick glance at school finance: A 50 state survey of school finance policies
. Available http://schoolfinancesdav.wordpress.com/.
Accessed 17 June 2014
.
Waisanen
,
Bert
.
2010
.
State tax and expenditure limits-2010
.
Denver, CO
:
National Conference of State Legislatures
. Available www.ncsl.org/research/fiscal-policy/state-tax-and-expenditure-limits-2010.aspx.
Accessed 28 July 2014
.
Wassmer
,
Robert
, and
Ronald
Fisher
.
2002
.
Interstate variation in the use of fees to fund K–12 public education.
Economics of Education Review
21
(
1
):
87
100
. doi:10.1016/S0272-7757(00)00051-0