This document analyzes the political economy of parcel taxes in California school districts, highlighting the historical context and adoption rates since their inception in 1983. It discusses factors influencing the willingness of wealthy districts to impose these taxes, such as income distribution and property value disparities, while presenting empirical data on their impacts. Preliminary results suggest that variations in property values significantly affect parcel tax adoption more than median household income.

1. Political Economy of Parcel Tax
in California School Districts
Soomi Lee
Assistant Professor
University of La Verne
January 9, 2016
Southern Political Science Association Annual Meeting
San Juan, Puerto Rico

2. California Tax Foundation, 2014
36
25
15
7
7
3
2
2
2
1
Education (K14)
Infrastructure
Health Care
Park and Recreation
Fire Protection District
Library
Public Safety
Unavailable
Emergency Medical Services
Other
Parcel Tax Revenue (2013-14):
Percentage Distribution by Function
$1.9 billion in total
From 1970 parcel taxes
in 754 local governments
1

3. Parcel tax elections in school
districts
• First school parcel tax was adopted in 1983.
• 644 parcel tax measures since then.
– 23% of school districts (222) held at least one
parcel tax election.
– 13% (124) passed at least one parcel tax
measure.
2

4. 57
68 70
84
93
108
2003-04 2005-06 2007-08 2009-10 2011-12 2012-13
Number of School Districts with
Parcel Taxes in California
Ed Source 2013.
Data: SACS unaudited data files, California Department of Education 3
$373 million
$138 million

5. Previous studies
• Why isn’t the use of parcel tax more pervasive in
California school districts?
• Focus on characteristics of school districts with
parcel tax
1. Brunner (2001): marginal price too high
2. Lang and Sonstelie (2015): tax price for per
pupil spending, district median income, Bay
Area
3. Kiewiet and Hill (2015): political ideology,
election strategies, district median income, Bay
Area 4

6. • Not surprising that rich districts are more
willing to pay additional taxes for public
schools. Why do some rich districts adopt a
parcel tax but not others?
• Given the same income, what determines the
likelihood of parcel tax adoption?
• Regressivity and the supermajority
requirement would make distribution of
property values more important than the
median household income.
5

7. Illustrative model: Community with
two home owners, A and B
• Assumptions for simplicity
– Same demand function for public schooling for A and
B
– Same income for A and B
– All property taxes are spent on public schools
– Total enrollment = 100
6
Total parcel tax revenue goal = $400
A B
Property value ($) 500,000 300,000
1% ad valorem property tax ($) 5,000 3,000

8. A B
Property value ($) 500,000 300,000
1% ad valorem
property tax ($)
5,000 3,000
Ad Valorem Property Tax Regime
Proposed tax increase = 0.05%
New property tax rate 1.05% 1.05%
Tax increase ($) 250 150
Total taxes 5,000+250=5,250 3,000+150=3,150
Additional cost per pupil
for each household
250/100=$2.5 150/100=$1.5
7

9. A B
Property value ($) 500,000 300,000
1% ad valorem
property tax ($)
5,000 3,000
Ad Valorem Property Tax Regime
Total parcel tax revenue goal = $400
Proposed tax increase = 0.05%
New property tax rate 1.05% 1.05%
Tax increase ($) 250 150
Total taxes 5,000+250=5,250 3,000+150=3,150
Additional cost per pupil
for each household
250/100=$2.5 150/100=$1.5
8
1. Different income effects (thus, income
distribution may play a bigger role)
2. Marginal cost of schooling per pupil is higher
for A  less likely to support parcel tax
measures

10. A B
Property value ($) 500,000 300,000
1% ad valorem
property tax ($)
5,000 3,000
Parcel Tax Regime
Proposed parcel tax per parcel = $200
Additional cost per pupil
for each household
200/100 = $2 200/100=$2
Taxes after parcel tax 5,200 3,200
New property tax rate 1%+0.04% = 1.04% 1%+0.07% = 1.07%
9
Same income effect for A and B

11. A B
Property value ($) 500,000 300,000
1% ad valorem
property tax ($)
5,000 3,000
Parcel Tax Regime
Total parcel tax revenue goal = $400
Proposed parcel tax per parcel = $200
Additional cost per pupil
for each household
200/100 = $2 200/100=$2
Taxes after parcel tax 5,200 3,200
New property tax rate 1%+0.04% = 1.04% 1%+0.07% = 1.07%
10
1. 0.03%pt higher property tax rate on B  less
likely to support parcel tax
2. Districts with a large difference in property
values would have a larger property tax rate
differences.

12. 11
Stylized fact 1: median income & support for
parcel tax adoption

13. 12
Stylized fact 2: housing value gap & support
for parcel tax adoption

14. Empirical test:
Pooled logistic regression
• Data: California school districts with 200 or more
students from 2003 to 2013. N=4063.
• Dependent variable: presence of parcel tax
revenue (binary)
• Main explanatory variables:
– Income gap: mean to median household
income ratio
– Home value gap: upper 25% to lower 25%
average single family home value ratio
13

15. • Control variables
– Median household income
– Median home value
– Racial heterogeneity (Herfindahl index)
– State aid (% of total revenue)
– Total population
– Population of age 65 or older (%)
– Renters in occupied housing units (%)
– People with BA degree (%)
– Bay Area indicator (1=Bay Area, 0=Otherwise)
14

16. 15
(1) (2) (3) (4)
Median household
income (log)
4.086***
(.157)
Median housing
price (log)
3.082***
(.137)
Income gap
1.201***
(.306)
Housing value gap
-2.542***
(.189)
Control variables No No No No
N 4062 4062 4062 3884
Notes: Robust standard errors are in parentheses.; * p<0.1; ** p<.0.05; *** p<0.01
Preliminary results 1:
Bivariate logistic regressions

17. 16
(1) (2) (3)
Median household
income (log)
-.440 -.187
(.460) (.399)
Median housing price
(log)
.329 .226
(.266) (.227)
Income gap
-.705 -.613 -.573
(.468) (.470) (.470)
Home price gap
-.212** -.247** -.210*
(.107) (.106) (.113)
Control variables Yes Yes Yes
N 3884 3884 3884
Notes: Robust standard errors are in parentheses.; * p<0.1; ** p<.0.05; *** p<0.01
Preliminary results 2:
Multiple logistic regressions

18. 17
(1) (2) (3)
Median household
income (log)
-.585 -.331
(.468) (.420)
Median housing
price (log)
.331 .200
(.267) (.235)
Income gap
-.577 -.487 -.427
(.489) (.486) (.495)
Home price gap
-.205** -.241** -.204*
(.103) (.102) (.110)
Racial diversity
.864 .862 .745
(.562) (.563) (.555)
Control variables Yes Yes Yes
N 3884 3884 3884
Notes: Robust standard errors are in parentheses.; * p<0.1; ** p<.0.05; *** p<0.01
Preliminary results 3:
Effect of racial heterogeneity

19. Conclusion
• Within-variations in property values in school
districts, not the median household income,
have robust effects on parcel tax adoption.
• The empirical model needs to be improved to
address potential selection bias and
endogeneity.
18

20. Implications & future research
• Research on parcel tax is about how
Californians cope with local revenue constraints.
• Heterogeneous school districts in many
Southern California communities will have less
opportunity to bring additional resources such as
parcel taxes.
• The impact of parcel tax on public school
funding is unknown, however.
– E.g. Its impact on financial disparities across
school districts and on school performance
19

21. Ed Source 2013.
Data: SACS unaudited data files, California Department of Education
77
23
87
13
Never Held an
Election (736
districts)
Held at Least
One Election (222
districts)
Never passed a
Parcel Tax (834
districts)
Passed at Least
One Parcel Tax
(124 districts)
School Districts and Parcel Tax
Elections (%), 1983-2012
20

22. 2.4
3.1 3.1
3.7 3.7
2003-04 2005-06 2007-08 2009-10 2011-12
57 districts 68 districts 70 districts 84 districts 93 districts
District Average Parcel Tax
Revenue
($millions, 2011 constant)
Ed Source 2013.
Data: SACS unaudited data files, California Department of Education 21

23. Parcel Tax Elections by California School Districts
1983-2008
Lang and Sonstelie, 2015. 22

24. Characteristics of Parcel Tax Ballot Measures, 1983-2013
Effort to lower political costs
Ballot Measure Provisions Percent (# elections)
Senior Exemption 78.6% (439/559)
SSI Disability Exemption 11.4% (64/559)
Other Exemption 6.8% (25/559)
Ad Valorem Correlates 17.0% (95/559)
COLA 20.6% (115/559)
No Sunset Clause 6.2% (40/648)
Kiewiet and Hill, 2015
Strategies to reduce political
costs
23