Abstract

This is the first study to directly examine the relationship between tax increment financing (TIF) and education expenditures, using the state of Iowa as a case study. I find that greater use of TIF is associated with reduced education expenditures. I also find little evidence to support the commonly held proposition that school spending increases when TIF districts expire. Finally, the negative price effect of TIF on education spending is increasingly larger for school districts in lower wealth or income groups compared with their counterparts in higher wealth or income groups. The negative, though small, effect of TIF on education spending, coupled with no gain from the often-claimed long-run benefits of TIF, justifies policy measures to protect school districts from TIF.

1.  Introduction

Tax increment financing (TIF) has become an increasingly popular economic development tool in the United States since its first use in California in 1952.1 As of 2013, only Arizona has not enacted TIF legislation.2 In recent years, TIF has been a hot-button issue in Iowa—-the state of focus in this paper. In 2011, there were more than 2,200 TIF districts in Iowa. Nearly 86 percent (or 297) of the school districts in the state contained one or more TIF districts in at least one year during the sample period between 2001 and 2011. These 297 school districts with TIF districts accounted for 96 percent of state total enrollment in 2011.

Two major reports (Fisher 2011; Fisher and Lipsman 2012) detail how TIF in Johnson and Polk counties in Iowa has been abused, with consequences for overlapping jurisdictions including school districts. For one, the merged Iowa River Landing/Coral Ridge Mall Urban Renewal Area in Johnson County diverted $4.99 million in property taxes from the Clear Creek Amana and Iowa City Community school districts, leading them to increase property tax rates by $2.83 and $0.56 per $1,000 of taxable value, respectively.

To use TIF, municipal and county governments establish a TIF district and are then allowed to divert the additional property taxes (associated with increased property values in the district) as TIF funds to defray development-related costs. In the absence of TIF, the additional property taxes would have been accessible to other overlapping jurisdictions, such as school districts and counties. Indeed, one of the key issues in using TIF is how it may affect overlapping jurisdictions, especially school districts and their spending. As reviewed later in this paper, however, most studies on TIF focus on issues other than education finance. A few of them, such as Lehnen and Johnson (2001) and Weber (2003), examine the impact of TIF on school district finances, particularly school district property tax revenues. No study has directly examined the relationship between TIF and education expenditures. This paper aims to fill this gap in the literature. Given the widespread use of TIF in Iowa and potentially sizeable fiscal impacts on school districts, the empirical findings from this paper have implications for TIF policy in Iowa and other states.

How might TIF affect education expenditures? In many cases, TIF freezes the property tax base at a level below what it would be without TIF, and lower property wealth is likely to reduce the amount of money local taxpayers are willing to pay for education. This approach can be explained using a demand, or tax price, mechanism (tax price is defined as the share of a voter's house value to the district's tax base measured by total per pupil taxable property value). TIF proponents often claim that voters in a school district with a TIF project have lower tax prices of, and thus higher demand for, education expenditures than in the absence of TIF. Tax prices may be lower from a larger property tax base after the TIF project comes to an end, and/or when the TIF project is designated to a blighted area with properties that would have depreciated in value without TIF. The voters would enjoy a larger tax base and demand more education services when the TIF project produces positive spillover effects on neighboring properties. Opponents, however, argue that TIF may increase voters’ tax prices of education spending and thus lower their demand for it. Higher tax prices occur when cities adopt TIF for areas rich in properties that would have trended upward in value, even without TIF, and/or when TIF negatively affects the value of neighboring non-TIF properties within the school district.

I use panel data on Iowa school districts between 2001 and 2011 to answer four interrelated research questions: (1) How are education expenditures generally affected by TIF use? (2) Are there differential effects from different types of TIF properties on education spending? (3) Does education spending increase when TIF districts expire? and (4) Does TIF affect the spending of school districts in different wealth or income groups differently? To preview, I find that heavier use of TIF is associated with decreased school district spending, holding all other factors constant. Also, residential and industrial TIF properties (but not commercial TIF properties) have significant negative effects on school district spending. I find little evidence of increased education expenditures once TIF districts expire. Finally, the lower a school district's wealth, the more TIF negatively affects education spending.

The rest of the paper is organized as follows. The next section presents background on TIF use in Iowa, a review of TIF-related literature, and basic information on school finance in the state. I then present a theoretical framework on how TIF might affect school expenditures, followed by a section that elaborates on my empirical strategy. I then discuss empirical results, and conclude with policy implications and suggestions for future research.

2.  Background

How TIF Works, and TIF in Iowa

TIF is a financing tool used by local governments, mostly cities and counties, to fund a project to address blight or promote economic development. Although some TIF programs are based on sales and income taxes (Smith 2009), property tax increment financing is used in most states with TIF-enabling legislation, including Iowa.3 A TIF project in Iowa begins when a municipality designates an urban renewal area with clearly defined boundaries as a TIF district or area.4 The TIF district is governed by an authority empowered to enter into contractual agreements and sell debt backed by TIF revenue.

Once established, the city freezes the assessed value of all taxable property within the TIF district at the base year—the year immediately prior to when the TIF district becomes effective. During the subsequent life span of the TIF project, the increase in assessed value above the base value is the incremental value. “Tax increment” thus refers to tax revenues generated on this incremental value by all overlapping taxing jurisdictions, namely, the city, school districts, counties, and other special-purpose districts. The TIF governing authority uses this tax increment to finance development (via bond issuance or on a pay-as-you-go basis), while property tax receipts from the frozen tax base continue to be channeled to the corresponding overlapping jurisdictions. All of the incremental value that may have built up over the years becomes accessible to these overlapping jurisdictions upon expiration of the TIF project.

TIF in Iowa is authorized by the Urban Renewal Law (Iowa Code, Chapter 403). This law, enacted in 1957, allowed municipalities to use TIF only in slum or blighted areas.5 (Whether a TIF district is blighted and whether it cannot be redeveloped through regular private enterprise but for the TIF incentive, also known as the “but for” test, are two common considerations for TIF approval.) A legal amendment in the 1985 Iowa Acts (Chapter 66) puts Iowa among a few states that do not currently require the presence of slum or blight conditions for TIF adoption. This amendment suggests that cities in Iowa are allowed, and would probably prefer, to adopt TIF for nonblighted areas, enabling them to capture a portion of the incremental value that would have been available to overlapping jurisdictions.

The Urban Renewal Law has had additional important amendments over the years. For one, counties in Iowa were authorized to use TIF only for industrial purposes (1991 Iowa Acts, Chapter 214).6 TIF can also be used for development of affordable housing for low- and moderate-income residents (1996 Iowa Acts, Chapter 1204). Since 1996, public hearings have been legally required for the initial approval of an urban renewal plan, although not for each TIF district that is established under the plan (1996 Iowa Acts, Chapter 1047). Overlapping jurisdictions are still unable to opt out of the establishment of a renewal area or TIF district, however.

These legal amendments led to a soaring increase in the number of TIF districts in Iowa. In 1989, there were approximately 185 TIF districts in Iowa, most of which were based on the blight requirement in the pre-1985 law (Talanker and Davis 2003). By 1999, the number of TIF districts statewide had increased to 2,496 (Swenson and Eathington 2002), before decreasing to 2,125 in 2011 (Robinson 2011). Iowa cities and counties use TIF at rates comparable with other Rust Belt states, such as Wisconsin and Illinois (Briffault 2010).

Iowa school districts subject to TIF vary significantly in terms of the number of TIF districts within their jurisdictions. The Davenport School District had the greatest number, with fifty-nine TIF districts in 2011. As shown in the first panel of table 1, it has become more common for school districts to contain a large number of TIF districts. For example, from 2001 to 2011, there was a six-fold increase in the number of school districts each containing at least thirty TIF districts. The second panel of table 1 shows that school districts are now less likely to have TIF districts in their jurisdictions expire. For example, 128 school districts had TIF districts expire from 2001 to 2003, compared with the period 2009 to 2011 when only 29 school districts had TIFs expire. The third panel of table 1 shows fewer school districts experiencing new TIF districts over time. School districts with new TIF districts still outnumbered those with expired TIF districts in all the years during the sample period.

Table 1.
Numbers of School Districts Comprising TIF Districts
 Year
20012002200320042005200620072008200920102011
(1) 48 59 66 71 73 76 87 94 99 103  111  
(2) 10 14 21 20 22 25 27 31 33 34 38 
(3)  3  4  5  5  9  9 10 12 14 17 19 
(4)  0  1  1  1  3  5  4  6  5  5  7 
(5) 52 46 30 18 33 19 25 18 13  8  8 
(6)  6 11  5  1  0  2  1  0  1  0  1 
(7)  4  1  2  0  0  1  1  0  0  0  0 
(8) 87 97 86 88 70 62 74 77 79 60 76 
(9) 20 28 26 22 15 17 21 23 20 11 22 
(10) 11 12 14  7  7  5  6  5  5  4  6 
 Year
20012002200320042005200620072008200920102011
(1) 48 59 66 71 73 76 87 94 99 103  111  
(2) 10 14 21 20 22 25 27 31 33 34 38 
(3)  3  4  5  5  9  9 10 12 14 17 19 
(4)  0  1  1  1  3  5  4  6  5  5  7 
(5) 52 46 30 18 33 19 25 18 13  8  8 
(6)  6 11  5  1  0  2  1  0  1  0  1 
(7)  4  1  2  0  0  1  1  0  0  0  0 
(8) 87 97 86 88 70 62 74 77 79 60 76 
(9) 20 28 26 22 15 17 21 23 20 11 22 
(10) 11 12 14  7  7  5  6  5  5  4  6 

Notes: (1), (2), (3), and (4) are the numbers of school districts that had 10, 20, 30, and 40 TIF districts, respectively.

(5), (6), and (7) are the numbers of school districts that had 1, 3, and 5 expired TIF districts, respectively.

(8), (9), and (10) are the numbers of school districts that had 1, 3, and 5 new TIF districts, respectively.

Source: Author's calculations are based on data from the Iowa Department of Management.

Related Literature

Studies on TIF appeared in academic journals as early as the late 1970s (Davidson 1978). This now substantial body of literature has revolved around key issues. For one, scholars have sought to identify the factors that drive municipalities to adopt TIF. Population size, intergovernmental aid, industrial composition, fiscal pressure, and tax competition (but not blight) are associated with the likelihood of municipal TIF adoption (Anderson 1990; Dye and Sundberg 1998; Man and Rosentraub 1998; Man 1999; Byrne 2005). Another key issue is how TIF adoption can affect the values of property within TIF districts and have spillover effects on property outside TIF districts. Byrne (2006) examines factors influencing property value growth within TIF districts, and Smith (2006) finds that properties in TIF districts exhibit higher rates of appreciation than those outside TIF districts, and higher rates than prior to TIF designation. Weber, Bhatta, and Merriman (2003) find that improved industrial TIF parcels appreciate at the same rate as otherwise identical industrial non-TIF parcels.

Several studies explicitly examine TIF spillover effects on property in non-TIF areas. Bossard (2011) finds evidence of nonlinear TIF spillover effects on the growth of non-TIF property values in school districts. Several studies find negative spillovers from TIF use. Dye and Merriman (2000) show the non-TIF portions of Illinois municipalities with TIF districts have slower real value growth than communities without TIF. Dye and Merriman's (2003) subsequent finding that TIF adoption has no impact on the growth of citywide property values suggests value growth in TIF districts is offset by declines in non-TIF areas in the adopting city. Specifically, commercial and industrial TIF districts hinder the growth of commercial non-TIF property values. Weber, Bhatta, and Merriman (2007) also find residential houses adjacent to commercial and industrial TIF districts see decreases in appreciation rates, and they argue that industrial TIF districts produce negative spillovers through development that is “noisy, polluting, aesthetically unappealing, and, like commercial development, conflicts with residential land uses” (p. 279). These authors also posit that negative spillover to residential neighborhoods from commercial TIF districts might result from traffic congestion and the lack of pedestrian access around the big-box retail stores typically found in commercial TIF districts. By contrast, Merriman, Skidmore, and Kashian (2011) find positive, though not precisely estimated, stimulatory effects of commercial TIF on commercial activity in non-TIF areas.

Three studies examine the effects of TIF on school revenues. Lehnen and Johnson (2001) find that TIF is not a revenue drain for school districts in Indiana because TIF was not intensely adopted in 1995—the only year with data available. Weber (2003) finds an inverse relationship between TIF intensity and property tax revenues across school districts in Cook County, Illinois. Weber, Hendrick, and Thompson (2008), however, find little effect of TIF on school revenues when they extend the sample to include all districts in Illinois.

A few studies focus on TIF in Iowa, in addition to the two Fisher reports cited earlier. Nydle (2009) and Perri (2001) examine legal aspects of TIF in Iowa. Lawrence and Stephenson (1995) develop a model to determine which Iowa taxpayers under which taxing authorities fund TIF expenditures and receive subsidies from TIF programs. Pacewicz (2013) looks at the role of TIF in the financialization of urban politics in two cities in Iowa. Swenson and Eathington (2002, 2006) examine the growth of TIF in Iowa and how it might impact economic development in Iowa cities. This study is the first to examine directly how TIF affects education expenditures.

School Finance in Iowa

School districts in Iowa, as in other states, are financed primarily by property taxes and state aid. Iowa adopts a foundation aid formula. The foundation level is equal to 87.5 percent of state cost per pupil, as determined by the previous year's state cost per pupil plus the regular allowable growth (usually 1 to 4 percent) set annually by the state's General Assembly. All districts contribute to the foundation level with property taxes at a uniform (statewide) rate of $5.40 per $1,000 of assessed valuation available to school districts (that is, the incremental value of TIF areas is excluded). The state provides aid for the difference between the foundation level and this uniform levy (or the required local contribution). Under this formula, state aid per pupil is smaller for property-rich than for property-poor districts. Beyond this foundation level, districts can use an additional levy to meet district costs per pupil, as derived by their previous year district cost per pupil plus the state-determined regular allowable growth. Because the maximum district cost per pupil can be no more than 105 percent of the state cost per pupil, district costs per pupil vary minimally across districts.

Little variation in district costs per pupil does not necessarily mean little variation in actual expenditures. As indicated in table 2, after adjusting for inflation, total operating expenditures per pupil vary substantially, with a standard deviation of nearly $3,000. The maximum spending was nearly eight times as much as the minimum spending. This substantial variation results from different expenditure ceilings that are determined by districts’ total spending authority. Spending authority for any given year comprises the total district cost, miscellaneous income, and unspent spending authority, which is the difference between the district's total spending authority and actual expenditures in the previous year.

Table 2.
Summary Statistics for the Full Sample (2001–2011)
Standard
VariableMeanDeviationMin.Max.Source
Total operating expenditures per pupil 10,283 2,962 7,042 54,447 (1) 
Tax price (TP0.42 0.21 0.03 1.30 (1), (2), (4) 
Total incremental property value per 10,750 17,266 218,647 (2) 
 pupil (     
Incremental per pupil value of 3,674 7,766 149,040 (2) 
 residential property      
Incremental per pupil value of 4,792 10,604 155,713 (2) 
 commercial property      
Incremental per pupil value of 1,908 4,567 49,636 (2) 
 industrial property      
Ratio of incremental taxable property 0.0005 0.0044 0.0932 (2) 
 value to total taxable value in yeart–1      
Enrollment 1,344 2,520 21 31,369 (1) 
Percent of free and reduced-price lunch 31.03 12.39 3.26 90.91 (1) 
 students      
Percent of LEP students 1.72 4.83 58.66 (1) 
Percent of African American students 1.44 2.68 30.22 (1) 
Total state aid per pupil 4,341 1,023 93 16,548 (1) 
Total federal aid per pupil 722 603 119 9,783 (3) 
Median household income 52,523 10,162 30,314 103,353 (4) 
Percent of college graduates 19.43 8.33 5.43 63.33 (4) 
Percent of owner-occupied housing units 84.63 5.79 50.51 97.05 (4) 
Standard
VariableMeanDeviationMin.Max.Source
Total operating expenditures per pupil 10,283 2,962 7,042 54,447 (1) 
Tax price (TP0.42 0.21 0.03 1.30 (1), (2), (4) 
Total incremental property value per 10,750 17,266 218,647 (2) 
 pupil (     
Incremental per pupil value of 3,674 7,766 149,040 (2) 
 residential property      
Incremental per pupil value of 4,792 10,604 155,713 (2) 
 commercial property      
Incremental per pupil value of 1,908 4,567 49,636 (2) 
 industrial property      
Ratio of incremental taxable property 0.0005 0.0044 0.0932 (2) 
 value to total taxable value in yeart–1      
Enrollment 1,344 2,520 21 31,369 (1) 
Percent of free and reduced-price lunch 31.03 12.39 3.26 90.91 (1) 
 students      
Percent of LEP students 1.72 4.83 58.66 (1) 
Percent of African American students 1.44 2.68 30.22 (1) 
Total state aid per pupil 4,341 1,023 93 16,548 (1) 
Total federal aid per pupil 722 603 119 9,783 (3) 
Median household income 52,523 10,162 30,314 103,353 (4) 
Percent of college graduates 19.43 8.33 5.43 63.33 (4) 
Percent of owner-occupied housing units 84.63 5.79 50.51 97.05 (4) 

Notes: There are 3,817 observations (347 districts for 11 years from 2001 to 2011). Monetary measures are adjusted for inflation (using government price indexes published by the Bureau of Economic Analysis) and in 2010 dollars. Sources are (1) Iowa Department of Education; (2) Iowa Department of Management; (3) Public Elementary-Secondary Education Finance Data (www.census.gov/govs/school/); and (4) U.S. Census in 2000 and 2010 (the annual values for inter-census years and 2011 were interpolated and extrapolated by using the linear growth rate between 2000 and 2010).

The catchall term of “miscellaneous income” includes any income a school district receives other than the uniform levy, any additional levies, and state foundation aid. Examples include investment interest, fees for student services, income surtaxes, federal aid, state grants, and funds raised for educational programs, such as instructional support programs (ISP) and educational improvement programs (EIP).7 Specifically, funds for the ISP and EIP can be increased up to 10 and 5 percent of the district cost, respectively. These additional funds for ISP or EIP can come from property taxes exclusively, or from a combination of property taxes and revenues from an income surtax that is charged as a percent of an individual's state income tax liability.8 Notably, some property tax levies are not subject to TIF funds captured by cities or counties. Property tax levies in this category are those for debt service and for physical plant and equipment. Therefore, my empirical work focuses only on the effects of TIF on operating expenditures.

3.  Theoretical Framework

How TIF might affect education expenditures can be examined in terms of how TIF might affect demand for education services. Demand for publicly provided education services depends on factors such as local residents’ income, demographic characteristics representing differential preferences for education, and the price of education services. Of these factors, TIF most plausibly affects the demand for education services through the price voters have to pay for additional education spending. As education is a publicly provided normal good, the higher the price, the lower the demand for education spending, and vice versa. This section presents a theoretical framework for potential effects of TIF on education spending by comparing a voter's tax liability or price (and thus his/her demand for education spending) in the presence and absence of TIF.

The tax liability, , of a voter in a school district is determined by school district tax rate, , and this voter's taxable property value, , or
formula
1
This district has a budget constraint represented by equation 2
formula
2
where E is total operating expenditures per pupil; is total taxable property value per pupil; is total state aid per pupil; and is other revenues per pupil, such as income surtaxes and federal aid. As discussed earlier, state education aid per pupil (A) is distributed to make up the difference between the foundation level () equal to 87.5 percent of state district cost per pupil, and local contributions equal to the product of the uniform property tax rate () of 0.0054 and total taxable property value per pupil (). Equation 2 can be rewritten as equation 3:
formula
3
In a typical school district's annual budget process, the district calculates per pupil property tax revenues that need to be raised () by subtracting F and O from E. As the final step to establish how much the district will spend (Mikesell 2014, p. 495), the property tax rate, , is set by equation 4:
formula
4
The first term of equation 4, , gives an additional tax rate the district needs to levy on local residents in addition to the uniform rate, .9 Substituting equation 4 into equation 1 yields
formula
5

I now incorporate the potential effects of TIF into equation 5. Of the factors in this equation, TIF is mostly likely to affect , which is negatively correlated with the voter's tax liability, . Let us take a closer look at how this might happen.10 I can divide the school district into two areas: the TIF and non-TIF (N) areas. Whereas the non-TIF area has its own taxable property values per pupil (), the TIF area has two separate taxable property values per pupil: frozen value () and incremental value (). The frozen value, , is set by freezing the previous year's values of the properties within the TIF district, called the base value (). produces additional revenue that, rather than being channeled to the school district, is captured by the initiating city or county to finance TIF activities, although the school district is still entitled to and , or .11

Assuming there is no spillover effect of TIF on non-TIF areas (or on ), I focus first on how and thus might be smaller or larger with TIF than without TIF. One of two common scenarios may occur, depending on the underlying trend (or growth rate) of the base value, .12 The first scenario illustrated by figure 1 occurs when has a positive (upward) underlying trend (i.e., the property tax base of the TIF district is poised to increase even without TIF). The frozen value, , is lower than what would have been in the absence of TIF. Put differently, the voter has higher annual tax liabilities and lower annual demand for education spending during the TIF period than he or she would have had without TIF. A portion of captures the difference between and that would otherwise have been accessible to the school district. TIF adoptions for areas rich in appreciating property benefit cities but fiscally constrain overlapping jurisdictions including school districts. Opponents often cite this scenario in their criticisms of TIF. The second scenario happens when a TIF district is established for an area with declining (or negative-trending) property values. This is usually the case for blighted areas. A voter's tax liability is lower with TIF than in the absence of TIF because would have been lower than the frozen value, . This is the best scenario in which local residents, all else being equal, benefit from lower tax liabilities, thereby probably increasing their demand for education spending.13

Figure 1.

Scenario in which the Property Tax Base of a TIF Area Has a Positive Growth Rate of Appreciation.

Figure 1.

Scenario in which the Property Tax Base of a TIF Area Has a Positive Growth Rate of Appreciation.

Two points are worth mentioning. First, either scenario 1 or 2 may occur when as in figure 1—that is, when the values of properties within TIF districts have a higher rate of appreciation than those of similar properties outside TIF districts (as in the case of Smith's [2006] finding). Second, scenario 1 may even occur when the values of TIF properties trend upward at the same rate as those of comparable non-TIF properties (as found in Weber, Bhatta, and Merriman [2003] for improved industrial TIF parcels in Chicago).

I now relax the assumption of no spillover. As evidenced in the literature discussed earlier, the adoption of TIF may generate spillover effects on property near TIF districts. In other words, can be a function of . The value of is larger or smaller with TIF than it would have been without TIF, when has positive or negative spillover effects, respectively. Together with the discussions in the preceding paragraph, this observation suggests that a voter's demand for education spending is lower (higher) as a result of his higher (lower) tax liability with TIF than without TIF when has negative (positive) spillover effects, and/or when has a positive (negative) underlying growth rate.14 It is thus difficult to accurately predict whether the relationship between TIF and Iowa school districts’ education expenditures is neutral, positive, or negative. Instead, the data can inform us about this relationship.

The earlier review of the TIF literature also suggests that the spillover effects from TIF may vary depending on the type of TIF district (i.e., commercial, industrial, residential). Put differently, the size of the spillover effects and thus of the demand for education spending (holding everything else constant) may vary depending on type of TIF property. The second research question is intended to explore these potentially differential effects of different TIF property types.

These discussions apply to TIF districts during their life spans. When a TIF district expires, its incremental value () in the immediately preceding year (or yeart–1) becomes accessible to the school district in the current year, making its with TIF larger than what it would have been without TIF.15 At the expiration of the TIF district, decreases, resulting in a lower price of education services and thus in higher demand for education spending. This is one of the major arguments for TIF. The third research question is motivated by this argument for the potential effects of expired TIF districts.

TIF cities would prefer areas with appreciating properties (scenario 1) to blighted areas with depreciating properties (scenario 2). Only with these TIF areas would they be able to capture the portion of property tax increases that otherwise would have been available to overlapping jurisdictions. In other words, cities tend to adopt TIF for purposes other than addressing blight. Man (1999) finds Indiana cities under fiscal pressures are more likely to adopt TIF. Fiscally strained cities are likely to have lower property tax bases, be located in lower-wealth school districts, and adopt nonblighted TIF areas. Controlling for total numbers of TIF districts and , lower-wealth school districts are likely to have more nonblighted TIF districts, and thus have a lower demand for education spending, than their higher-wealth counterparts.16 I test this likelihood in the fourth research question.

4.  Empirical Strategy

In keeping with TIF literature, I use a reduced-form constant-elasticity expenditure model to estimate the effects of TIF on school district spending.17 Under this reduced form, I specify operating expenditures per pupil (E) to be a function of measures related to TIF use (Y), and a vector of control variables (X) as in equation 6:
formula
6
where i and t index school districts and years. In equation 6, represents district fixed effects to control for time-invariant district-specific unobserved factors. As adopted in Downes and Pogue (1994), these district fixed effects also account for time-invariant unobserved efficiency factors, thereby addressing most of the relative efficiency differences across districts.18 Year dummies, , control for common factors affecting all school districts in Iowa in a given year.

Vector X includes all other control variables (unless otherwise specified, all financial measures are in 2010 dollars and per pupil terms). The first group of control variables includes characteristics of the student body, such as enrollment, percent of African American students, and shares of disadvantaged students, including those receiving free or reduced-price lunch, and those with limited English proficiency. A higher concentration of disadvantaged students is expected to induce greater expenditures. Studies reviewed in Fox (1981) and Andrews, Duncombe, and Yinger (2002) provide empirical evidence that economies of size in education may help larger districts incur lower costs per pupil. Following this literature, I specify the log of per pupil expenditures as a quadratic function of the log of enrollment. The second group of control variables represents voter income (measured by median household income following the median voter framework of public choice to be discussed), income surtaxes, and intergovernmental aid (namely, state and federal aid) that school districts receive.19 Higher voter income and additional revenues from income surtaxes or intergovernmental aid are expected to increase education expenditures.

The third group of variables includes variables representing district-level differences in demographic characteristics, that is, the share of the population that graduated from a four-year college and the share of owner-occupied housing units. These populations, compared with non-college graduates and renters, respectively, may have different preferences for school expenditures. Specifically, college-educated people may desire greater school spending (Bergstrom, Rubinfeld, and Shapiro 1982; Hilber and Mayer 2009). Because school quality is found to be capitalized into property values (Nguyen-Hoang and Yinger 2011), homeowners are also expected to prefer higher education expenditures.20

Variables of TIF Use

I have two sets of TIF-related measures (Y). I show in the theoretical framework that the potential effects of TIF on education expenditures revolve mostly around the property tax base, , and the ratio of . The first measure in my empirical estimations that captures this ratio is based on the median voter framework.21 In this public-choice framework, a school district's level of spending is the one desired by the majority of its local residents, that is, as chosen by the median voter. The median voter is decisive—so is his tax price indicating his preference, or demand, for education. Using Bergstrom and Goodman's (1973) definition, the median voter is the citizen with a median income who is assumed to own a house of median value. The standard measure of the price for public services is, therefore, the ratio of median house value to total per-pupil taxable property value accessible to the school district. In this standard measure of tax price (), the numerator, , is the median voter’s. Although may capture a portion of the effect of TIF on education spending, it is not a direct indicator of TIF use, and does not allow us to quantify how much of this effect can be attributed to TIF.

The second set of TIF measures is related more closely to TIF use. The first and key measure in this set is the incremental taxable property value per pupil in all TIF areas of a school district in the current year, or in the theoretical framework.22 (I naturally log-transform and replace undefined logged values [when with 0.) reflects the potential effects of TIF on . Higher captures greater absolute differences between and what would have been without TIF (as in the earlier two -related scenarios), and/or greater spillover effects on . As previously discussed, the coefficient of can be neutral, positive, or negative. A negative coefficient of suggests undesirable consequences of TIF on education spending: TIF captures a portion of the property tax base of the TIF areas that would have increased and been available to school districts without TIF, and/or makes the property tax base of the non-TIF area smaller (as a result of negative TIF spillovers) than with no TIF.

Following Merriman, Skidmore, and Kashian's (2011) finding that the number of existing TIF districts within a municipality affects property values, I use a subordinate TIF-related measure, that is, the number of new TIF districts within a school district () in the current year. This measure might also capture the same potential effects of TIF on as . The coefficients of and are thus expected to have the same sign. When both and are estimated, the coefficient of indicates whether an increased concentration of TIF districts has any effect on educational spending, in addition to the effect from .

In equation 6, represents the total incremental value from property of all types. As discussed earlier, the spillover effects from TIF may vary depending on the type of TIF district, namely, commercial, industrial, or residential. To investigate potential differential effects from different types of TIF property on education spending (the second research question), I disaggregate into three major types of TIF property—residential, commercial, and industrial23—and estimate them together with other variables described in equation 6.

A major argument for TIF use is that overlapping jurisdictions, including school districts, may benefit financially when TIF districts expire, releasing that has potentially built up over the years to the overlapping jurisdictions, and thus lowering local voters’ tax price. To capture this potential price effect of expired TIF districts (the third research question), I use the ratio of the incremental taxable property value to the total taxable property value, both in yeart–1 (),24 and the number of expired TIF districts within a school district ().25 These two variables are expected to be positively correlated with education expenditures.

To investigate the fourth question as to whether TIF affects the education expenditures of lower-wealth school districts more than those of higher-wealth districts, I include in equation 6 an interaction between the main measure of TIF (), and a variable indicating a school district's relative level of wealth or income (W).26 This variable, W, is coded 0, 1, and 2 for districts in which the median housing value in Census 2010 was below the 33rd percentile, between the 33rd and 67th percentiles, and above the 67th percentile, respectively. If the coefficient of is significantly negative, a positive significant coefficient of the interaction term then means that TIF has milder price effects on higher-wealth school districts. To test the robustness of the results, I interact with a variant of W, which is based on school districts’ median household income in Census 2010.27

Potential Endogeneity

One might be concerned over potential endogeneity as a result of reverse causality between education expenditures and TIF use. In a school district that expects education spending to grow rapidly, its officials might be more resistant to TIF establishment than in school districts with more stable spending. Put differently, school districts with low education spending are prone to TIF adoption. If such a link between spending and TIF existed, estimated results would be biased downward.

This concern for potential bias is not warranted for several reasons. First, although previous studies recognize TIF to be potentially endogenous, none of them provides significant evidence of endogeneity. Dye and Merriman (2000) reject endogenous TIF adoption; Man and Rosentraub (1998) and Anderson (1990) find insignificant relationships between school district–related variables and TIF adoption; and Weber (2003) does not find evidence of endogenous TIF in the estimations of school district revenues, state aid, and effective tax rates. Second, unlike their counterparts in other states, school districts in Iowa cannot choose to opt out of the TIF process.28 Third, as argued in Weber, Hendrick, and Thompson (2008), the endogeneity is generally not an issue for estimating the effects of TIF at the school district level because local governments (mostly municipalities) that make TIF adoption decisions in Iowa are distinct from school districts. The boundaries of school districts do not coincide with those of municipalities and counties (the two jurisdictions with the authority to establish TIF districts). The designation of TIF districts is, thus, unlikely to be correlated with school districts’ fiscal conditions. All in all, potential downward bias is not a major cause for concern; one can, however, treat my estimates as conservative.

Table 2 provides summary statistics of all the variables used to estimate different variants of equation 6, and their sources. The data included 347 school districts in Iowa between 2001 and 2011.29 Of these, fifty districts did not have any TIF district during the sample period. Nearly 84 percent of the total observations had positive TIF values. Incremental taxable property values per pupil within TIF districts represented by showed marked growth and variation with the standard deviation of $17,266.30 Of the three major property types subject to TIF, commercial properties provided school districts with the most incremental per pupil value, followed by residential and industrial properties.

5.  Results

Table 3 presents the regression results on the general effects of TIF on education expenditures (the first research question) with the specification of both and . (The results are almost identical when they are estimated separately.) The coefficient of is negative but insignificant, showing ambiguous evidence of this variable on education spending. The other two TIF measures, namely, tax price () and total incremental taxable property value per pupil (), show negative price effects on school expenditures, indicating lower demand for spending in response to higher prices. A 1 percent increase in and is associated with a reduction of 0.12 and 0.0018 percent, respectively, in school district operating expenditures. I am unable to directly quantify the impact of TIF with the coefficient of . The price effect of TIF measured by on education expenditures is relatively small. Suppose that an average-spending district that has the average of $10,750 and enrollment of 1,344 increases its by approximately one standard deviation to slightly over $28,000. This 160 percent increase in would induce a reduction of only 0.29 percent (160 0.0018 percent) in this district's operating spending per pupil ($31), thus, a reduction of $41,664 in total spending.

Table 3.
General Effects of TIF on Education Expenditures (dependent variable: logged total operating expenditures per pupil)
VariablesCoefficients
Log of tax price (−0.12 
 (−5.67)*** 
Log of incremental taxable property value per pupil (−0.0018 
 (−2.86)*** 
Number of new TIF districts within school districts (−0.00033 
 (−0.54) 
Log of enrollment −1.01 
 (−10.49)*** 
Squared log of enrollment 0.050 
 (7.40)*** 
Logged percent of free and reduced-price lunch students 0.018 
 (2.12)** 
Logged percent of LEP students −0.00036 
 (−0.23) 
Logged percent of African American students 0.0017 
 (0.91) 
Log of median household income 0.061 
 (1.74)* 
Log of state aid per pupil 0.14 
 (4.28)*** 
Log of federal aid per pupil 0.015 
 (3.14)*** 
Log of income surtaxes per pupil 0.0028 
 (2.78)*** 
Percent of college graduates 0.0016 
 (1.74)* 
Percent of owner-housing units 0.00077 
 (1.66)* 
R2 0.621 
VariablesCoefficients
Log of tax price (−0.12 
 (−5.67)*** 
Log of incremental taxable property value per pupil (−0.0018 
 (−2.86)*** 
Number of new TIF districts within school districts (−0.00033 
 (−0.54) 
Log of enrollment −1.01 
 (−10.49)*** 
Squared log of enrollment 0.050 
 (7.40)*** 
Logged percent of free and reduced-price lunch students 0.018 
 (2.12)** 
Logged percent of LEP students −0.00036 
 (−0.23) 
Logged percent of African American students 0.0017 
 (0.91) 
Log of median household income 0.061 
 (1.74)* 
Log of state aid per pupil 0.14 
 (4.28)*** 
Log of federal aid per pupil 0.015 
 (3.14)*** 
Log of income surtaxes per pupil 0.0028 
 (2.78)*** 
Percent of college graduates 0.0016 
 (1.74)* 
Percent of owner-housing units 0.00077 
 (1.66)* 
R2 0.621 

Notes: There are 3,817 observations between 2001 and 2011. Regressions are estimated with district fixed effects and year dummies. Hypothesis testing is done with the robust heteroskedasticity and autocorrelation (HAC) Newey–West standard errors. Numbers in parentheses are t-statistics. LEP = limited English proficiency students.

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

Many of the other variables are highly significant with expected signs. I find a quadratic relationship between enrollment and education spending. While more students decrease education expenditures per pupil, diseconomies of size start to set in at an enrollment of about 24,300. (In Iowa, only the enrollment of the Des Moines School District exceeds this threshold.) A 1 percent increase in the share of low-income students is associated with an increase of nearly 0.02 percent in operating expenditures. Voters’ higher income also increases their demand for education expenditures. The demand also becomes higher with increased income surtaxes and state and federal aid. The effect of state aid is more than nine times larger than that of federal aid. As in Nguyen-Hoang (2013), an increase in the shares of college graduates and homeowners within a district is associated with higher school spending.

Table 4 reports the estimation results for the second research question regarding the potential differential effects of different TIF types on education expenditures. This table shows that incremental property values of residential and industrial properties in TIF districts are negatively related with school district spending. An increase in the incremental values of residential and industrial properties is associated with a reduction of 0.0012 percent, respectively, in educational spending. This price effect of residential TIF on demand for education spending can be explained by, as reviewed in Nguyen (2005), the negative, though small, spillover effects of affordable housing on the values of nearby properties. (The primary purpose of residential TIF in Iowa is to create affordable housing.) In keeping with the theoretical framework, the negative price effect of industrial TIF on education expenditures may be explained by negative spillover effects on nearby properties (Dye and Merriman 2003; Weber, Bhatta, and Merriman 2007) and/or a positive growth trend of industrial TIF properties (Weber, Bhatta, and Merriman 2003). The coefficient of commercial TIF is insignificant probably because of mixed spillover effects of all commercial TIF districts within a school district on nearby properties: Some commercial TIF districts have positive spillovers (Merriman, Skidmore, and Kashian 2011), others have negative spillovers (Weber, Bhatta, and Merriman 2007).

Table 4.
Effects of Different Property TIF Types
Key VariablesCoefficients
Log of tax price (TP−0.12 
 (−5.66)*** 
Log of incremental per pupil taxable property value of residential property −0.0012 
 (−1.85)* 
Log of incremental per pupil taxable property value of commercial property 0.00036 
 (0.60) 
Log of incremental per pupil taxable property value of industrial property −0.0012 
 (−2.07)** 
Number of new TIF districts within school districts (−0.00034 
 (−0.55) 
Key VariablesCoefficients
Log of tax price (TP−0.12 
 (−5.66)*** 
Log of incremental per pupil taxable property value of residential property −0.0012 
 (−1.85)* 
Log of incremental per pupil taxable property value of commercial property 0.00036 
 (0.60) 
Log of incremental per pupil taxable property value of industrial property −0.0012 
 (−2.07)** 
Number of new TIF districts within school districts (−0.00034 
 (−0.55) 

Note: All of the unreported variables and the other notes are the same as in table 3.

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

Table 5 reports the results for the third and fourth research questions. In columns 1 and 2, I find little empirical evidence for effects of expired TIF districts measured by either the ratio of incremental taxable property value to total taxable value in yeart–1 (), or the number of expired TIF districts within a school district (). Although the signs of these two variables are positive as expected (i.e., higher demand for education spending in response to lower prices), their coefficients are not precisely estimated. This seemingly puzzling finding provides little support for the argument that overlapping jurisdictions would benefit when TIF projects expire. This finding can be plausibly explained by insufficient statistical power,31 or by an unfavorable attitude toward growth government spending among the large majority of voters. This attitude may come from the voters’ fear of a “ratchet effect” in school district spending (i.e., once increased, the spending will not be lowered).

Table 5.
TIF Effects of Expired TIF Districts and for Low-Income School Districts
Key Distinguishing Specification
and #School DistrictSchool District
of ExpiredTypes BasedTypes Based
Expired TIFTIF Districtson Medianon Median
Ratio Housing ValueHousehold Income
Key Variables(1)(2)(3)(4)
Log of tax price (−0.12 −0.12 −0.12 −0.12 
 (−5.67)*** (−6.20)*** (−5.68)*** (−5.73)*** 
Log of total incremental taxable −0.0018 −0.0018 −0.0034 −0.0038 
 property value per pupil ((−2.86)*** (−3.21)*** (−3.46)*** (−3.79)*** 
Number of new TIF districts −0.00034 −0.00034 −0.00035 −0.00041 
 within a school district ((−0.54) (−0.54) (−0.55) (−0.64) 
Ratio of incremental taxable 0.020 0.019 0.023 0.022 
 property value to total (0.11) (0.09) (0.11) (0.10) 
 taxable value in yeart–1 (    
Number of expired TIF districts  0.000022 −0.000038 0.000058 
 within a school district ( (0.01) (−0.02) (0.03) 
Logged W   0.0019 0.0021 
   (2.49)** (2.81)*** 
Key Distinguishing Specification
and #School DistrictSchool District
of ExpiredTypes BasedTypes Based
Expired TIFTIF Districtson Medianon Median
Ratio Housing ValueHousehold Income
Key Variables(1)(2)(3)(4)
Log of tax price (−0.12 −0.12 −0.12 −0.12 
 (−5.67)*** (−6.20)*** (−5.68)*** (−5.73)*** 
Log of total incremental taxable −0.0018 −0.0018 −0.0034 −0.0038 
 property value per pupil ((−2.86)*** (−3.21)*** (−3.46)*** (−3.79)*** 
Number of new TIF districts −0.00034 −0.00034 −0.00035 −0.00041 
 within a school district ((−0.54) (−0.54) (−0.55) (−0.64) 
Ratio of incremental taxable 0.020 0.019 0.023 0.022 
 property value to total (0.11) (0.09) (0.11) (0.10) 
 taxable value in yeart–1 (    
Number of expired TIF districts  0.000022 −0.000038 0.000058 
 within a school district ( (0.01) (−0.02) (0.03) 
Logged W   0.0019 0.0021 
   (2.49)** (2.81)*** 

Notes: All of the unreported variables and the other notes are the same as in table 3. W in column 3 is coded 0, 1, and 2 for school districts whose median housing values in Census 2010 were below the 33rd percentile, between the 33rd and 67th percentiles, and above the 67th percentile, respectively. W follows the same coding procedure, except that the Census 2010 median household income is used.

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

Columns 3 and 4 of table 5 provide evidence on the differential effects of TIF on the education expenditures of school districts with different levels of wealth or income. A 1 percent increase in for the lowest wealth or income school districts (W = 0) is associated with a reduction of 0.0034 or 0.0038 percent in education spending, respectively. This price effect for the lowest income district group is approximately twice as large as the average effect reported in table 3. The positive significant interaction terms of logged in columns 3 and 4 suggest that the negative price effect of TIF on educational expenditures becomes smaller for higher-income school districts. While the spending of school districts in the middle group (between the 33rd and 67th wealth or income percentiles) is expected to decline by only 0.0015 or 0.0017 percent in response to a 1 percent increase in , changes in have no significant effect on the educational expenditures of school districts in the highest wealth or income group.32 These findings suggest, all else being equal, cities in the lowest wealth or income group were more likely to designate nonblighted areas as TIF districts even though property values would have increased without TIF.

6.  Conclusion

Tax increment financing (TIF) has been a popular tool for economic (re)development in many states. Of these, Iowa is a top user of TIF, with more than 2,200 TIF districts. TIF has been a hot-button policy issue in the state following recent reports on the (ab)use of TIF in Polk and Johnson counties. Integral to the controversy is how TIF may affect overlapping taxing jurisdictions, particularly school districts. On the one hand, proponents of TIF hold that voters in a school district may demand greater education expenditures from lower tax prices associated with a larger property tax base when one of these scenarios occurs: Cities in the district adopt TIF for blighted areas that would have otherwise depreciated, TIF exerts positive spillover effects on neighboring property, or TIF districts expire. On the other hand, opponents of TIF argue that voters in a school district may face higher tax prices of education spending and thus demand less of it when TIF district property values frozen during the TIF period would have increased without TIF (and been available to the school district), or when TIF induces negative spillover effects on property near TIF districts.

This is the first study to directly examine the relationship between TIF and education expenditures. I find greater use of TIF is associated with reduced education expenditures. I also find little evidence to support the commonly held proposition that school spending increases from TIF when TIF districts expire. This finding might reflect voters’ fear of the ratchet effect (i.e., school spending, once increased, keeps going up). Finally, the negative price effect of TIF on education spending is increasingly larger for school districts in lower wealth or income groups compared to their counterparts in higher wealth or income groups. The effect of TIF on the spending of school districts in the lowest income group is approximately twice as large as the average effect. These school districts might have more nonblighted TIF districts that would not have passed the “but for” test.

These findings have policy implications for Iowa and elsewhere. The negative, though small, effect of TIF on education spending, coupled with no gain from the often-claimed long-run benefits of TIF, justify policy measures to protect school districts from TIF. For one, school districts, as in Ohio, Georgia, Texas, and Pennsylvania, should be allowed to opt in, or out of, TIF plans initiated by cities. In addition, the “but-for” test should be reinstated as a major condition for TIF approval. Reinstating the test would particularly help the lowest-income school districts most affected by TIF.

Finally, state education aid formulas could buffer the effects of TIF on education expenditures. In Iowa, the aid is designed to make up the difference between the foundation level and a school district's required contribution based only on its non-TIF tax base. Practically, the state partially offsets the district's potential tax-base losses from TIF. As noted earlier, the negative price effect of TIF on education expenditures, which I find to be relatively small for Iowa, is likely to be higher for states that use a school district's total tax base (i.e., including the TIF incremental value) to compute required local contributions in their state aid formulas. Identifying and studying states that include TIF incremental value in the calculation of local contributions would help reveal the full extent of TIF effects on education expenditures.

Notes

1. 

TIF is also less commonly known as a Tax Allocation District (TAD) as in Georgia, Revenue Allocation District (RAD) as in New Jersey, or Tax Increment Reinvestment Zone (TIRZ) as in Texas.

2. 

California—as noted, the first state to enact TIF legislation—repealed its TIF law in February 2012.

3. 

The only example of a sales tax–based TIF district in the state is the Iowa Speedway Project in Newton (approved in 2005).

4. 

A TIF district in Iowa can encompass an entire urban renewal area. Alternatively, multiple TIF districts can be contained in a single urban renewal area (Cory and Martin 2012). For simplicity, I interchangeably use the terms TIF districts or TIF areas in this paper.

5. 

Under Chapter 403, blight was defined as “an economic and social liability imposing onerous municipal burdens which decrease the tax base and reduce tax revenues.”

6. 

Among local governments in Iowa, municipalities are still the primary recipients of TIF revenues. In fiscal year 2011, 40 out of 99 counties and 349 out of 947 incorporated cities received TIF revenues (Robinson 2011).

7. 

The property tax levy for instructional support programs is not available for TIF use if TIF debt incurs after 24 April 2012, which post-dates the last year of my sample period. Also, funds for both of these programs are included in a school district's operating funds.

8. 

Iowa is among a few states (including Kentucky, Ohio, and Pennsylvania) that authorizes school districts to levy local income taxes for specific education programs (Ross and Nguyen-Hoang 2013).

9. 

Total school district property tax rates were always higher than the uniform rate during the sample period, ranging between $8.168 and $24.426 per $1,000 assessed valuation.

10. 

Potential effects of TIF on education expenditures can be explained through a budgeting mechanism. This mechanism also examines how TIF might affect . Unlike the demand mechanism, the effects of TIF on education spending E are captured in the first term on the right-hand side in equation 2, . The budgeting mechanism is less appropriate for this study. Although TIF might change , the final link between TIF and E depends crucially on whether school districts can set the tax rate, , at will. If they can do so, the effects of TIF on E are then confounded and harder to untangle. The budgeting mechanism would be more appropriate in a state with limits on property tax rates levied by school districts (as in California with Proposition 13). This is not the case for Iowa, where school districts are not subject to state-imposed limits on property tax rates.

11. 

Whereas a district's assessed value that is used to compute its required contribution in Iowa's state education aid formula is based only on non-TIF areas, or , other states may calculate the local contributions based on the total local property tax base, regardless of whether it is available to the school district; that is, . Working this contribution formula through equations 24, a voter's tax liability in those states equals . All else being equal, a marginal increase in , a key measure of TIF use, raises the tax liability of a voter (via ) more in those states than in Iowa, which suggests that the marginal effect on education expenditures of TIF, especially in cases of higher tax price and thus lower spending demand, is highly likely to be larger in those states than in Iowa.

12. 

The term underlying indicates the growth trend of observed prior to TIF adoption would have continued in later years in the absence of TIF. during a TIF period is a counterfactual as illustrated in figure 1.

13. 

The third and less likely scenario occurs when has a zero growth rate (i.e., during the TIF period would have been equal to ). Any increase in can be attributed completely to TIF. The voter's tax liability with TIF is equal to that without TIF; TIF thus has no effect.

14. 

For cases in which has positive (negative) spillover effects, and has a positive (negative) underlying growth rate, whether is higher or lower with TIF than without TIF depends on the size of the spillover effects relative to the size of the absolute difference between and what would have been without TIF.

15. 

An example of a school district's receiving a sizeable increase in its tax base following an expired TIF district is Lewis Central School District (LCSD) in Council Bluffs. This school district benefited from three expired TIF districts in 2003. These three districts had a combined incremental value of over $40.3 million in 2002. This amount became available to LCSD in 2003, dramatically increasing its tax base by 13 percent, to about $535.4 million. The 13 percent jump was much higher than the year-on-year increase of between 5 and 7 percent in the previous three years when none of the TIF districts expired.

16. 

This likelihood assumes away differences in spillover effects across school district groups. This assumption is innocuous because there is little reason for such differences.

17. 

The constant elasticity demand model is dominant in the applied literature on public and education spending (Duncombe 1996).

18. 

Studies such as Duncombe and Yinger (2011) may specify efficiency factors that do change over time as a function of voter income and tax price. However, I use different measures of tax price for vector Y, and variables indicating voter income are included in vector X of equation 6. For ease of interpretation, these efficiency effects are disregarded.

19. 

I do not have data on investment income or student fees, although they are expected to play a negligible role in counteracting the use of TIF. Both investment income and student fees traditionally account for a very small proportion of a school district's operating budget. Also, school districts have little control over investment receipts primarily determined by market forces.

20. 

Although I log percentage variables indicating student characteristics, I do not log demographic variables (also in percentage terms). The decision of whether to log percentage variables is to maximize explanatory power. The main results change little whether all of the percentage variables are logged or not logged.

21. 

The median voter framework is just one of the formal public choice voting models. As reviewed in Gill and Gainous (2002), none of the voting models perfectly describes reality.

22. 

For school districts with multiple TIF areas, the estimated coefficient indicates the average effect of all TIF areas.

23. 

Other property types include agricultural land and buildings, railroads, and utilities with or without gas and electric property. These properties represented only 2.7–4.3 percent of the total incremental value in TIF districts during the sample period. Also, the data do not have an indicator of TIF type for individual TIF districts; I am thus unable to disaggregate or .

24. 

This ratio captures the intensity of the TIF use in year t–1. I use this measure instead of the incremental taxable property value per pupil in the preceding year because I do not have enrollment data for the academic year 1999–2000. The estimation results reported in table 5 are still insignificant when I divide the incremental property value in year t–1 by the enrollment in the current year, or when I log this ratio.

25. 

New TIF districts are in the current year but not in the immediately preceding year, and expired TIF districts exist in the immediately preceding year but not in the current year.

26. 

As shown and discussed in the Results section, does not show significant impacts on education spending. Its interaction with W is not significant either and thus is not reported. The interaction between W and is also insignificant and not reported.

27. 

W cannot be estimated separately. It is dropped in fixed effects models because it is constant for each district.

28. 

Overlapping jurisdictions may legally opt out of participation in the TIF process in eleven states (Weber 2003).

29. 

There were 359 school districts as of the 2010–11 academic year. Given that district consolidations have substantial expenditure implications (Duncombe and Yinger 2007), however, districts that were reorganized or consolidated during the sample period were deleted from the data set.

30. 

The summary statistics without the fifty non-TIF school districts are quite similar.

31. 

Compared with nearly 84 percent of the observations with positive TIF values, only 270 (or 7 percent) of observations have at least one expired TIF district during the sample period.

32. 

Using column 3, the statistical test of a linear combination of (–0.0034 + 2 0.0019) (with the lincom Stata command) provides an insignificant result.

Acknowledgments

I would like to thank Benjamin Gillig for his assistance in early stages of data collection, and participants at the Property Tax and Financing of K–12 Education conference organized by the Lincoln Institute of Land Policy for their helpful comments. My special thanks go to Therese McGuire and an anonymous reviewer for their insightful comments and suggestions on the paper. I am also particularly thankful to Ted Nellesen at the Iowa Department of Management for his provision of the TIF data and for his patience in responding to all my questions.

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