Jeffrey Zabel

Economics Department

Tufts University
梅德福, 嘛 02155

jeff.zabel@tufts.edu

UNINTENDED CONSEQUENCES:
THE IMPACT OF PROPOSITION 2 1
2
OVERRIDES ON SCHOOL

SEGREGATION IN

MASSACHUSETTS

抽象的
I investigate a possible unintended consequence of
1
Proposition 2
2 override behavior—that it led to in-
creased segregation in school districts in Massachusetts.
This can occur because richer, low-minority towns tend
to have more successful override votes that attract
similar households with relatively high demands for
public services who can afford to pay for them. To eval-
uate this hypothesis, I collect panel data on override
behavior from 1982 到 2012 and merge this with data
on school district enrollments and other district- 和
town-level characteristics. I ﬁnd evidence that passing
overrides earmarked for schools results in a signiﬁcant
decrease in the percent of nonwhite students enrolled in
Massachusetts school districts. This happens in districts
with below-average nonwhite school enrollments, 和
hence increases segregation.

土井:10.1162/EDFP_a_00144
© 2014 Association for Education Finance and Policy

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PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

1. 介绍
A recent study by Northeastern University researchers found that public
schools in Springﬁeld (second place) 和波士顿 (fourth place), 马萨诸塞州,
are some of the most segregated in the United States (McArdle, Osypuk, 和
Acevedo-Garc´ıa 2010). Although this study measured within-district segre-
gation, there is evidence that most segregation is between school districts
(Clotfelter 2004). As Rivkin (1994) points out, school segregation is driven by
residential segregation because students are usually assigned to local schools.
此外, the relationship between school segregation and residential seg-
regation increased between 2000 和 2010 (Frankenberg 2013).

Since Tiebout (1956), we understand that households “vote with their feet.”
那是, households will choose where to live based on the local public goods
provided by jurisdictions. This results in local public goods provision being cap-
italized into house prices as households are willing to pay for these services by
bidding up house prices in these towns. One of the most important local public
goods is school quality. Because school quality is a normal good, households
with higher incomes and those with school-aged children will likely reside in
towns with better schools. 因此, it is not too surprising that minority house-
holds, which tend to have lower incomes, attend high-minority, high-poverty,
and low-quality schools (McArdle, Osypuk, and Acevedo-Garc´ıa 2010).

For more than 50 years an important driver of the changing demographic
makeup of towns in Massachusetts (as is the case for most major metropolitan
地区) is the relocation of white households from central cities to the sub-
urbs (Boustan and Margo 2013), so-called “white ﬂight.” Based on data for
Massachusetts from the 2010 Decennial Census, Schworm and Caroll (2011)
conclude that this has resulted in public school districts with a high percent-
age of minority students, particularly in low-income communities, “plagued
by violent crime and struggling schools.”

To provide some evidence about trends across segregation in school dis-
tricts in Massachusetts, I plot the percent nonwhite enrolled in school districts
在 1985 和 2013 (the ﬁrst and last years for which I have district enrollments).
Districts are aggregated into 10-point bins based on percentage nonwhite
enrollment, and those in the extreme bins are ones with high levels of
segregation.1 First, note that the percent nonwhite enrolled in Massachusetts
school districts has increased over this period—the median was 2.3 百分比在
1985 and rose to 15.2 百分比在 2013. The bins are therefore measured relative
to the median. 第二, because the percent nonwhite enrollment in Mas-
sachusetts is low, many districts will have low nonwhite enrollments. 因此,
I focus on districts with percent nonwhite enrollments above the median.

1. See Clotfelter (2013) for an example of this technique.

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Jeffrey Zabel

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10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90

Percentage Nonwhite Above the Median

1985

2013

笔记: The median percent nonwhite enrolled in 1985 是 2.3 并在 2013 这是 15.2.
Each bin is the percent nonwhite enrolled in the district above the median. So for 1985 the first bin
includes districts with percent nonwhite enrolled between 2.3%-12.3%.

数字 1. Percentage Nonwhite Enrolled Above the Median: 1985 和 2013 MA School Districts
with Percentage Nonwhite above the Median.

The results are given in ﬁgure 1. 在 1985, 86.7 percent of the districts with
above-median percent nonwhite enrollments had values that were within 10
percentage points of the median and 96.3 percent were within 20 percentage
points of the median, 而在 2013, 仅有的 35.2 百分比和 51.5 百分
were within 10 和 20 percentage points of the median, 分别. 在 2013,
所以, there were many more districts with percent nonwhite enrolled that
was substantially above the median than was the case in 1985. 此外,
in an absolute sense, 在 2013 多于 10 percent of all the districts had
nonwhite enrollment of 80 percent or more, whereas none of the districts had
nonwhite enrollments at this level in 1985. This indicates that the segregation
of nonwhites into districts with a high percentage of nonwhite enrollments
has increased over this period.

Additional support for Schworm and Caroll’s (2011) conclusion is the fact
that for the majority of towns in Massachusetts that are also school districts
the correlation between the percent nonwhite enrolled in town schools and the
median household income has increased in magnitude from −0.15 in 1985 到
−0.30 in 2009. 那是, not only has there been an increase in districts with
a high percentage of nonwhite enrollment, but they tend to be increasingly in
low-income areas.

Another sign of increasing segregation in schools is the decreasing percent-
age of nonwhites in districts with high enrollments of white students. 给定
the low percentage of nonwhites in school districts in Massachusetts, 参与-
ularly in 1985, the above approach is not appropriate for showing changes in

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483

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

segregation that occur with a decrease in nonwhite enrollments. 在本文中,
I provide an alternative means for providing evidence of increased “whiten-
ing” of already majority white school districts. I show this is an unintended
1
consequence of a major property tax limitation law, Proposition 2
2 , 这是
implemented in the early 1980s in Massachusetts. It limited local property
taxes to 2.5 percent of assessed value (levy ceiling) and restricted growth in
the levy limit to 2.5 percent a year (with an allowance for new growth). 这
measure did allow residents to vote to override the 2.5 percent increase in the
levy limit as long as it did not result in taxes that exceeded the levy ceiling.
An override results in a permanent increase in the city or town’s levy limit
(increasing the base for each successive year’s allowable 2.5 percent increase).
In a recent paper (Wallin and Zabel 2011), we ﬁnd that richer towns tend to
have more successful override votes and tend to be in better ﬁscal condition
than poorer towns. 那是, richer towns attract households with a relatively
high demand for public services who can afford to pay for them.

在本文中, I investigate whether successful overrides earmarked
for school expenditures led to increased segregation in school districts in
马萨诸塞州; 尤其, that it decreased the percent of nonwhites en-
rolled in districts with already high percentages of white enrollments. The logic
is that these successful overrides led to greater spending on schools which,
反过来, led to a greater concentration of residents with high demands for
school quality. These households tend to have higher incomes and, because of
the negative correlation with race, are more likely to be white. 此外,
successful overrides tend to occur in high-income towns with relatively low
minority enrollment. Given that increased school spending leads to increased
school quality and increased school quality is capitalized into house prices,
there is a multiplier effect as higher house prices attract higher income and
white households that demand greater school quality.2

I collect panel data on overrides from 1982 到 2012 and merge this with data
on school district enrollment starting in 1985, along with numerous town-level
特征. I rely on the panel nature of the data to use a difference-in-
difference estimator. I ﬁnd evidence that passing overrides earmarked for
schools results in a signiﬁcant decrease in the percent of nonwhites enrolled
in Massachusetts town schools. This happens in towns with below-average
nonwhite school attendance and hence increases segregation.

These results will be useful for state and local policy makers as they pro-
vide information about the unintended consequences of tax and expenditure

2. Bayer, 费雷拉, and McMillan (2004) ﬁnd the indirect effect of school quality on residential sorting

is signiﬁcantly larger than the direct effect.

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Jeffrey Zabel

局限性 (TELs) on school segregation. Depending on the goals of the TEL,
the possibility of increased segregation might make it less desirable.

The paper is organized as follows. 部分 2 provides background informa-
1
tion about Proposition 2
2 . The literature on the impact of TELs on schools is
reviewed in section 3. 部分 4 gives details about the data. 部分 5 discusses
the identiﬁcation strategy used in this study. Estimation results are given in
部分 6, and section 7 concludes.

2. 背景
In the 1970s, the characterization of Massachusetts as “Taxachusetts” became
popular. This label was justiﬁed as Massachusetts residents saw their overall
state and local tax effort, as determined by the U.S. Advisory Commission
on Intergovernmental Relations, rise from an already high 129 (29 百分
above average) 在 1975, 到 133 在 1977, and then to 144 in 1979—second highest
in the nation to New York. 在 1977, Massachusetts’ per capita property taxes
were almost twice that of the average state (卡特勒, Elmendorf, and Zeckhauser
1999). 在 1980, 49.5 percent of the general revenue of local governments in
Massachusetts came from property taxes, compared with 28.2 percent in the
average state (Bradbury and Ladd 1982). This was due, 部分地, to the fact that
local revenue options were limited and the percentage of local government
revenue in Massachusetts that came from state aid, 27.8 百分, was below
the national average of 35 百分.

Given this backdrop, the passage in 1978 of California’s property tax limi-
tation law (Proposition 13) motivated Massachusetts citizens to follow a similar
小路. On November 4, 1980, Massachusetts residents approved Proposition
2 by a 59 百分比到 41 percent vote. The measure limited local property taxes
2
到 2.5 percent of assessed value (levy ceiling), and restricted growth in the levy
limit to 2.5 percent a year plus an allowance for new growth. Many towns were
above the levy ceiling and, starting in ﬁscal year (风云) 1982, had to reduce their
property tax collections by no more than 15 percent per year until they reached
the levy ceiling. Some towns required three years to do so.

Once towns reached their levy ceiling after these cuts, the levy limit was in-
creased by 2.5 percent on an annual basis. Given that assessed property values
increased by more than 2.5 百分, 一般, the difference between the
levy limit and the levy ceiling continued to grow over time such that exceeding
1
the latter was of little concern. Proposition 2
2 does allow residents to increase
taxes above the levy limit by passing overrides that result in a permanent in-
crease in the town’s levy limit (increasing the base for each successive year’s
allowable 2.5 percent increase) but it cannot exceed the levy ceiling. 第一的, A
majority of the town’s selectmen or town or city council members must vote to

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485

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

place the override on the ballot, in some cases with the consent of the mayor.
Then the override is approved if it receives 50 percent or more of the town vote
(Massachusetts Department of Revenue 2007).

Besides overrides, towns have two other means for increasing revenues
beyond the levy limit. Temporary increases in the levy limit (even if this
surpasses the levy ceiling) are allowed via a capital or debt exclusion. 这些
exclusions are typically used to fund speciﬁc capital projects. In the case of
a capital exclusion, taxes are increased by the cost of the capital project (所以
that taxes are only increased for one year). A debt exclusion raises taxes by the
amount of the debt service for the life of the debt as projects are funded by
issuing bonds. Successful debt exclusions do not become part of the base upon
which the levy limit is calculated for future years. In both cases, a two thirds
vote by the local government is required to put the capital or debt exclusion on
the ballot. A capital or debt exclusion is approved if it receives 50 percent or
more of the town’s vote (Massachusetts Department of Revenue 2007).

Bradbury (1991) ﬁnds that towns attempting at least one override vote had
higher incomes per capita, lower new growth as a percent of the previous
year’s property tax levy limit, and lower levels of excess capacity (the difference
between the levy limit and actual property tax revenues) relative to their levy
limit. These towns also tended to be smaller and have lower property tax rates.
Bradbury concludes that voters in many towns in Massachusetts do appear to
1
get what they want from the Proposition 2
2 override process. But she notes
that one problem with the override process is that towns in most need of
additional public services (those with relatively low incomes) are less likely to
pass an override.

1
Bradbury, Mayer, and Case (2001) analyze the impact of Proposition 2
2
on the ﬁscal behavior of towns in Massachusetts during 1990–94 when (在
the beginning of this period) the Massachusetts economy was in a recession
and real state aid was cut by 30 百分. 因此, there was a great deal of
override activity to make up for the decline in revenues. 更远, 有
pressure to increase school spending due to a demographically driven rise in
enrollments (see ﬁgure 3).

Data are limited to 208 的 351 towns in Massachusetts because Case-
Schiller repeat sales indices are only available for 214 towns with enough
sales to generate reliable indices. Six towns are excluded due to other data
1
局限性. The results indicate towns most constrained by Proposition 2
2
prior to 1990 had the slowest growth in school spending during the 1990–94
时期. House price regressions indicate changes in school spending were
correlated with growth in house prices between 1990 和 1994. 但是这个
1
relationship was limited to towns constrained by Proposition 2
2 —those at
their levy limit and that had not passed an override prior to 1990.

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Lang and Jian (2004) examine how Proposition 2

1
2 affected the relation-
ship between local revenues (property taxes and fees) and house prices in
Massachusetts for the period 1984–88. They argue that towns constrained by
1
Proposition 2
2 provide local public goods at suboptimal levels and, 所以,
house prices will be negatively affected. Increasing property taxes in these
towns is expected to increase house values. 在这种情况下, towns will set fees
to maximize house values. Lang and Jian state that some towns are unable to
optimize the fee levels and so increasing fees in these towns will also increase
house values.

The preferred sample is limited to 178 的 351 towns in Massachusetts.
Towns with fewer than 40 housing transactions in 1984 (76) and those where
the housing stock increased by more than 20 之间的百分比 1980 和 1990
(84) 被排除在外. The Two-Stage Least Squares results that control for the
endogeneity of state aid indicate that all three revenue sources (property taxes,
state aid, and fees) have a positive and signiﬁcant impact on the percent change
in the real equalized assessed property value. This is particularly true for towns
constrained by Proposition 2

1
2 .

Similar results are obtained when using the real per capita change in
the assessed value of residential property. Thus the evidence supports the
1
hypothesis that, given the constraints imposed by Proposition 2
2 , 增加
in property tax revenues lead to greater growth in house values. The positive
impact of fees on house values is not as robust across different speciﬁcations.
In some cases the impact is not signiﬁcantly different from zero, which is the
expected outcome if there was no constraint on imposing fees. As Lang and
Jian (2004) point out, their results are similar to those in Bradbury, Mayer,
and Case (2001), even though that study focuses on the impact of expenditures
on house values and Lang and Jian focus on revenues.

Wallin and Zabel (2011) look at the impact on local ﬁscal conditions of
1
Proposition 2
2 . We ﬁnd richer towns tend to have more successful override
votes and tend to be in better ﬁscal condition than poorer towns. 那是, richer
towns attract households with a relatively high demand for public services who
can afford to pay for them.

We estimate a dynamic override equation that includes lags of binary
indicators of an override vote, OVERRIDE, and whether the override passed,
WIN. We ﬁnd the coefﬁcient estimate for the lag of OVERRIDE is positive and
signiﬁcant at the 1 percent level, whereas the coefﬁcient estimate for the lag
of WIN is negative but only signiﬁcant at the 10 percent level. The estimated
semi-elasticities for the lags of OVERRIDE and WIN are 40.0 百分比和
−20.1 percent, 分别. This implies the experience of putting an override
on the ballot (win or lose) increases by 40 percent the likelihood of another
override vote the following year. Although a successful override decreases the

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487

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

likelihood of another vote the next year, the full impact of a winning override
vote on the likelihood of an override vote the following year is the sum of the
coefﬁcients on the lags of OVERRIDE and WIN. The estimate of this sum is
positive with a p-value of 0.015 and a semi-elasticity of 20 percent—so even
having a winning override vote in the previous year makes it more likely there
will be a vote in the current year.

In the conclusion of the paper, I speculate that Proposition 2

1
2 led to
increased sorting across towns in Massachusetts as the passage of overrides
led to greater levels of public goods which, 反过来, led to a greater concentration
of residents with high demands for these goods. The ability of these towns to
provide more services is enhanced as the “median voter” is now more likely to
vote for more services. Given that these additional services are capitalized into
house prices, this can further entice high-income households to move in and
further raise house prices, resulting in a multiplier effect. The result is an even
greater distinction between the high- and low-spending towns than otherwise
would be the case without the overrides that are a part of Proposition 2

1
2 .

3. LITERATURE REVIEW
There is a rapidly growing literature that documents the important role schools
play in residential sorting. This literature is surveyed in Brunner (2013). I focus
on two strands of literature that are closely related to this study. 第一的, I review
the literature on the impact of TELs on schools. 第二, I review the literature
on local bond referenda for school expenditures because this is similar to
override votes in Massachusetts.

The Impact of TELS on Schools

There is a large number of papers that investigate the impact of TELs on town-
level outcomes such as local public goods provision. 不出所料, 许多
focus on schools because this is one of the most important local public goods
that jurisdictions provide. Downes and Figlio (1999) review the literature that
evaluates the impact of TELs on student performance. They note that such
analyses are complicated by the fact that many states implemented school
reforms close to the time when tax limitations became effective. It follows
that panel data on school districts of which a subset are subject to tax limits
and/or school reforms are needed to best estimate the impact of tax limita-
tions on student performance. Their conclusion is that the literature generally
supports the outcome that TELs have a long-run negative impact on student
表现, particularly for math.

Other school characteristics have also been affected by TELs. In a na-
tionwide study, Figlio (1997) ﬁnds that TELs are associated with larger

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Jeffrey Zabel

student-teacher ratios and lower cost-of-living-adjusted starting teacher
salaries, but not with administrative costs. Figlio (1998) also ﬁnds that Measure
5, a TEL imposed in Oregon in 1990, led to signiﬁcantly higher student-teacher
ratios and that the ratio of administration to educational spending was unaf-
fected or possibly even increased after Measure 5 was in effect. Figlio and
Rueben (2001) ﬁnd that TELs led to a reduction in the quality of new public
school teachers. Shadbegian (2003) ﬁnds that state-level TELs have little effect
on local public education. Results also indicate that local TELs have no effect
on spending because TEL-related cuts were offset by increases in state aid.
Local TELs do have a small positive impact on student-teacher ratios but not
on teacher salaries.

1
Two papers focus speciﬁcally on the impact of Proposition 2
2 在
Massachusetts schools. Shadbegian and Jones (2005) look at the impact of
1
Proposition 2
2 on per capita own-source and intergovernmental (state and
联邦) 收入, expenditures per pupil, and eighth-grade reading and math
Massachusetts Educational Assessment Program test scores. Data on school
district ﬁnances come from the Census of Government for the years 1972,
1977, 1982, 1987, 和 1992; and economic, demographic, and school enroll-
ment data come from the 1970, 1980, 和 1990 Decennial Censuses. 这
sample is limited to 1982, 1987, 和 1992 为了 139 towns in Massachusetts that
have their own K–12 public schools or their own K–8 public schools and then
1
send their students to a regional high school. The Proposition 2
2 变量
include two indicators of whether the town was required to cut property taxes
for at least one year and two or three years to initially reach the levy ceiling
and an indicator of a successful override earmarked for the general operating
or school budget.

The results using a school district ﬁxed effects estimator show that the
lowering of property taxes to the levy ceiling did reduce own-source revenues
but these reductions were offset by increases in state and federal aid, thus the
net effect on spending per pupil was zero. 然而, the authors ﬁnd some evidence
1
that Proposition 2
2 had a negative effect on student performance. 比较的
with towns that did not have to reduce their property taxes, test scores in those
towns that had to make property tax cuts for at least one year were 1.4 百分
降低. But this result is based on ordinary least squares (OLS) estimates so it
could just reﬂect unobserved differences in the two groups of towns.

Bradbury, 案件, and Mayer (1998) document the changes in school enroll-
ment in Massachusetts in the 1980s and 1990s. Public school enrollments
declined throughout the 1980s as baby boomers graduated from high school,
but enrollments rose in the 1990s as birth cohorts started to expand in the mid
1980s. 实际上, this trend continued until 2001 and has steadily declined since
然后 (see ﬁgure 3). 当然, there is a large amount of heterogeneity across

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489

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

towns around this state trend. The authors’ goal was to determine factors that
were correlated with this variation in town enrollments. They ran regressions
of the change in net enrollments (the percentage point difference between ac-
tual and predicted enrollments) 之间 1980 和 1985 和 1990 和 1995 在
a school quality index, percent developable land, demographic characteristics,
median house prices, and median rents.

Bradbury, 案件, and Mayer (1998) ﬁnd that school quality was signiﬁcantly
positively correlated with enrollments in the 1990s but not in the 1980s.
One explanation is that as the returns to education have increased over time,
households have placed a greater importance on their children’s education.
1
Bradbury, 案件, and Mayer also investigate the role that Proposition 2
2 有
played in town enrollments. They include indicators of whether Proposition
1
2
2 was binding—whether the town had to make one cut or two or three
cuts in property tax revenues to initially meet the levy ceiling, and for the
1990s regression whether the town was at the levy limit in 1989. 为了
1980s regression, they ﬁnd the indicator of two or three revenue cuts has a
negative and signiﬁcant impact on enrollments and the impact is economically
signiﬁcant—compared with towns with no revenue cuts, those with two or
three revenue cuts experienced, 一般, A 0.37 standard deviation decline
in net enrollments. For the 1990s regression, enrollments in towns at the levy
limit in 1989 是 0.23 standard deviations lower than those towns not at
the levy limit. These results led Bradbury, 案件, and Mayer to conclude that
1
Proposition 2
2 resulted in a movement of households from towns that were
constrained by this law to those that were not.

The results from these two papers indicate the initial reduction in property
1
taxes brought on by the implementation of Proposition 2
2 resulted in a drop in
enrollments, had no impact on per-pupil spending, and possibly had a negative
impact on student performance. 在本文中, I look at the subsequent impact
1
of Proposition 2
2 on school enrollments. 那是, I am looking at the impact of
1
tax increases on school enrollments in the context of a TEL (Proposition 2
2 ).
As discussed by Downes and Figlio (1999), it is important to account
for any school reforms that occurred around the time a TEL was promul-
gated. Massachusetts instituted comprehensive school reform in 1993—the
Massachusetts Education Reform Act (MERA). The main reform involved
a new system of school ﬁnancing (Chapter 70) 那, initially, redistributed
state aid to provide relatively greater support for low-spending school dis-
tricts. MERA also initiated a higher level of accountability and a new statewide
standardized test—the Massachusetts Comprehensive Assessment System
(MCAS)—that was designed to evaluate districts’ efforts to meet these new
learning expectations. One way that MERA might affect the impact of suc-
cessful overrides is through its impact on state aid. 但, 大部分情况下,

490

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Jeffrey Zabel

Chapter 70 involves a mechanical procedure for calculating state aid that is
unlikely to be affected by override behavior. 此外, as I discuss sub-
依次地, I ﬁnd no signiﬁcant evidence of a reduction in state aid when an
override is passed. 因此, the impact of MERA on how successful overrides
affect segregation in school districts is likely to be minimal at best.

Local Bond Referenda and the Financing of School Capital
Cellini, 费雷拉, and Rothstein (2010) analyze the impact of bond ﬁnancing
for school capital on house prices, test scores, and local demographics. 他们
compare house prices in school districts that just passed votes on bonds versus
ones that just failed to pass bonds. They include jurisdiction-year observations
in an eight-year window around the bond votes. House price data are collected
at the census tract level in California for 1988–2005. Bond data are from the
California Educational Data partnership for 1987–2006. Racial composition
and average family income are from information collected as a result of the
Home Mortgage Disclosure Act. Test data correspond to third and fourth grade
math and reading scores.

The results show that successful bonds signiﬁcantly increase capital outlays
but not current instructional expenditures two to four years after passage.
此外, house prices steadily increase from around 3 percent in the year
of passage to around 7–10 percent six years later (though prices are higher in
districts with successful votes and it is not clear that these changes are adjusted
for these higher prices prior to passage). The impact of bond passage on test
scores is generally not signiﬁcant so it appears that house price increases
reﬂect willingness to pay for academic school outputs that are not captured by
test scores. 有趣的是, the authors ﬁnd no effect of bond passage on average
收入, enrollments, racial composition, or average parental education.

Balsdon, Brunner, and Rueben (2003) develop a framework for estimating
the demand for local school infrastructure based on outcomes of bond refer-
enda. An important insight into this model is that the referenda that come to
a vote are not randomly selected. Because they are proposed by school boards
(or in the case of overrides by local ofﬁcials), these referenda likely represent
the preferences of the school board members as well the preferences of their
constituencies. Hence the model of the demand for school infrastructure in-
vestment that the authors propose includes a referenda selection equation that
will account for the nonrandom selection of referenda that are put to a vote.

The data used by Balsdon, Brunner, and Rueben (2003) are similar to
those used by Cellini, 费雷拉, and Rothstein (2010); district-level data from
California for 1996–2000. They estimate and test two models of school board
decision making—the competitive and the budget-maximizing agenda-setting
型号. The results are consistent with the budget-maximizing model with

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491

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

risk aversion. The estimate of the price elasticity is −0.59 and the estimate of
the income elasticity is 0.74, so spending on school infrastructure is reasonably
responsive to both price and income. Controlling for sample selection does
affect the results, although the impact is modest as the OLS estimates of
the price and income elasticities are −0.52 and 0.60, 分别. At least as
重要的, 尽管, is the addition of sociodemographic variables to the model,
which decreases the estimate of the income elasticity to 0.54.

A closely related paper is by Neilson and Zimmerman (2011), who evaluate
the effect of a large school facility investment program in New Haven, 骗局-
内蒂克特, on student achievement, residential sorting, and home prices. 这
city of New Haven underwent a ﬁfteen-year, $1.4 billion school construction program. The ﬁrst school project was completed in 1998 and the last is sched- uled to end in 2014. The mean expenditure per project was $34 百万 (在
$2005) or about $78,000 per capita in the affected area. A key to the identi-
ﬁcation of the causal impacts of the school construction program is the fact
that the process for the choosing of and the timing of schools for renovation
was not based on factors related to the schools or the local community. 这
authors then use a difference-in-difference framework based on the variation
in the timing of the school construction projects.

Neilson and Zimmerman (2011) ﬁnd that six years after construction be-
两个都, A $10,000 increase in per capita expenditures on construction raised reading scores by 0.027 standard deviations but had no signiﬁcant effect on math scores. At the mean construction expenditure this is an increase of 0.21 standard deviations. House prices rise by about 1 percent and enrollments increase by up to 4.4 percent after project completion for every $10,000 in per
capita construction expenditures.

4. 数据
The data used in this study are from four main sources: (1) 马萨诸塞州
Department of Revenue (MDOR), (2) Massachusetts Department of Elemen-
tary and Secondary Education (MDOESE), (3) National Center for Education
统计数据 (NCES) 和, (4) the Census Bureau.

Information on Proposition 2

1
2 comes from the MDOR. An override vote
is an attempt to permanently increase the levy limit to a level that is no higher
than the levy ceiling. The ballot must state the purpose and dollar amount of
the override. A successful override vote results in the amount of the override
being included in the levy limit for that year. The median amount of an override
vote is $1.00 百万 (在 $2012). The median amount of successful overrides
是 $1.24 百万 (在 $2012).

The override data cover activity from FY1982, the ﬁrst ﬁscal year in which
overrides were allowed, until FY2012. There were a total of 4,662 overrides

492

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Jeffrey Zabel

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1985

1990

1995

2000

2005

2010

年

Number of Overrides

Number of Wins

数字 2. Number of Override Votes and Wins FY1983–FY2012.

reported in the data. Not all overrides are included because it is up to town
ofﬁcials to report override votes. 的 351 城市, 305 have reported at least
one override vote, 50 percent have reported nine or fewer votes, 5 percent have
报道 50 或者更多, and two have reported 100 或者更多.

The annual number of overrides has varied considerably over the thirty-one
years of data. 数字 2 shows the annual number of override attempts on local
ballots. This has ranged from a low of 31 在 1984 和 1985 (after the infusion
of state aid) 到 538 和 548 在 1990 和 1991, 分别 (in response to
state aid reductions). The number of overrides showed a steady increase at the
beginning of the 2000s but never reached the number attained in the early
1990s. 实际上, the number has steadily decreased since 2005, 仅与 56
override attempts in FY2011 and only 51 in FY2012. The percentage of wins
曾是 45.9 percent in the 1980s, 33.4 percent in the 1990s, 和 51.1 百分比在
the 2000s. Hence the difference in the number of recent wins compared with
those in the early 1990s is not as great as the difference in overrides.

I will not only look at the impact of override wins on school segregation
but also at successful debt and capital exclusions to see if these also have
an impact. 此外, without controlling for successful debt and capital
exclusions, the impact of successful overrides might be biased. The data on
capital exclusions cover FY1988 to FY2012. There were a total of 1,442 首都
exclusion votes during this period, 其中 59 percent passed. The median
曾是 $5.18 百万 (在 $2012) for all capital exclusion attempts and $5.43 百万 (在 $2012) for successful votes. 有 7,704 debt exclusions for the period

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493

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

n
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1985

1990

1995

2000

年

2005

2010

Percent Nonwhite
Percent Hispanic
Total Enrollment

Percent Black
Percent Asian

数字 3. Percent Minorities Enrolled and Total Enrollment Massachusetts Public Schools,
1985–2013.

FY1982 to FY2012, 超过 50 percent more than the number of overrides. 几乎
two thirds of the debt exclusion votes passed. It is hard to know the amount of
the debt exclusions because only the net excludable debt for winning votes in
each ﬁscal year is given.

The school-related data come mostly from the MDOESE and are aug-
mented for certain series using the Common Core data from NCES. I have
school district-level enrollment data for Massachusetts from 1985 到 2013 那
include the percent of nonwhite, 黑色的, Hispanic, and Asian students in the
区.

数字 3 shows the annual total enrollment of students in public schools
in Massachusetts. During this time period, total enrollments were in decline
直到 1989. There was then a steady increase until the early 2000s (19 百分
total increase) and then a constant, though slight, decline in the last ten years
(3 percent total decline). With this setting in mind, the percent of black,
Hispanic, Asian, and nonwhite students enrolled in public schools in
Massachusetts between 1985 和 2013 are also plotted in ﬁgure 3. There has
been a fairly steady annual increase of about two thirds of a percentage point
in the percent nonwhites enrolled, rising from 13.4 百分比在 1985 到 34.0 每-
分在 2013. There has been only a slight increase in the percent black enrolled
(6.6 百分比在 1985 和 8.6 百分比在 2013), 但是一个 10.2 percentage point in-
crease in Hispanics between 1988 和 2013 (从 6.2 百分比到 16.4 百分).
Data from the 1980, 1990, 和 2000 Decennial Censuses and from the
2007–2011 American Community Survey (ACS) 5-Year Estimates are obtained

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Jeffrey Zabel

from the Census Bureau. The middle year is used as the year corresponding to
the ACS 5-Year data sets. The data from the 1980 和 1990 Decennial Censuses
are from the extra databases put together by Terry Long and available from
the Interuniversity Consortium for Political and Social Research and from the
National Historical Geographic Information System.3 I collect town-level data
on family income, 种族, 年龄, 教育, 人口, the number of housing
units, persons per unit, median house price, and percent renter. I use family
income because it is consistently available in all the data that I have collected.
The income data are reported as the number of families with income within
speciﬁc ranges. I use the midpoint of the ranges to assign income value and
then calculate the 10th, 25th, 50th, 75th, and 90th percentiles of the family
income distribution.

The unit of observation for this analysis is the town-year. The period of
observation varies depending on the variables used, though this generally
covers 1985–2012.

The variable OVERRIDEit is an indicator of whether or not there was
at least one override vote in town i in ﬁscal year t. WIN is an indicator of
whether or not there was at least one successful override vote. AMOUNTit
是个 (真实的) per capita total dollar amount of all override wins in town i and
year t. Similar variables are also generated for capital and debt exclusion votes.
Variable deﬁnitions and summary statistics are given in table 1.4

桌子 2 gives variable means for the 305 towns that ever put an override
on the ballot and the 46 towns that never did so. The p-value of the test of
equal population means is given in column 3. It is clear that these two groups
of towns are very different. Towns that never put an override on the ballot are
larger, have lower family incomes, and lower house prices (在 1980) 尽管,
出奇, they have a higher percentage of residents with a bachelor’s
程度. The middle panel provides the same information for the 252 城市
that ever had a winning override and the 53 towns that had only unsuccessful
overrides. These two groups are also quite different in similar ways as the
previous comparison between towns that ever/never voted on an override.

5. IDENTIFICATION STRATEGY
My strategy for identifying the causal impact of override wins on the racial
composition of enrollments relies on the difference-in-difference approach.
The identiﬁcation comes from comparing the change in the racial composition

3. See the National Historical Geographic Information System Web site at www.nhgis.org.
4.

I exclude two towns (Gosnold and Monroe) from the summary statistics for school data from the
MDOESE and NCES and from the regressions because their annual enrollment is never greater
than twenty and this skews some of the values of variables based on the percentage enrolled. 为了
例子, in Gosnold there were only three students enrolled in 2001 and all are recorded as special
education students.

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495

PROPOSITION 2 1
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AND SCHOOL SEGREGATION

桌子 1. Summary Statistics

姓名

Number Mean

标清

最小

Max

Override Data from MADOR

Override

Win (Override = 1)

10,881

1,790

0.16

0.64

0.37

0.48

Real Amount Per Capita (Override = 1)

1,790

38.27

113.86

0.00

2, 093.15

Debt Exclusion

Win (Debt Exclusion = 1)

Capital Exclusion

Win (Capital Exclusion = 1)

10,881

2,617

8,775

540

0.24

0.87

0.06

0.75

0.23

0.33

0.24

0.43

School Data from MDOESE and NCES

Percent Nonwhites Enrolled in Town Schools

7,361

11.19

14.99

Percent Blacks Enrolled in Town Schools

Percent Hispanics Enrolled in Town Schools

Percent Asians Enrolled in Town Schools

7,361

6,369

7,361

3.15

4.97

2.70

5.64

10.34

3.81

0.00

1980 Decennial Census Data

Population (1,000s)

Percent Nonwhite

Percent Black

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

Median Family Income ($1,000s) Median House Price ($1,000s)

Number of Housing Units (1,000s)

百分比 4 or more Persons Per Housing Unit

Population (1,000s)

Percent Nonwhite

Percent Black

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

351

496

16.34

35.82

0.06

0.00

1.91

3.45

0.00

4.14

6.25

3.56

2.00

4.30

4.53

11.57

10.87

4.93

2.08

0.93

11.73

27.91

21.23

24.34

20.76

47.94

16.00

22.92

6.29

14.92

0.08

32.07

7.47

10.34

1990 Decennial Census Data

17.14

36.54

3.58

1.28

13.04

22.85

28.10

16.31

5.45

2.44

4.33

3.68

13.25

8.04

0.10

0.00

2.67

6.12

6.94

2.22

94.30

54.00

90.40

34.20

562.99

36.04

22.46

30.15

36.98

61.72

64.73

45.00

131.55

241.44

50.65

574.28

49.00

25.54

34.06

32.65

68.50

53.30

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Jeffrey Zabel

桌子 1. Continued.

姓名

Number Mean

标清

最小

Max

Median Family Income (1,000s)

Median House Price (1,000s)

Number of Housing Units (1,000s)

百分比 4 or more Persons Per Housing Unit

Population (1,000s)

Percent Nonwhite

Percent Black

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

Median Family Income (1,000s)

Median House Price (1,000s)

Number of Housing Units (1,000s)

百分比 4 or more Persons Per Housing Unit

Population (1,000s)

Percent Nonwhite

Percent Black

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

Median Family Income (1,000s)

Median House Price (1,000s)

Number of Housing Units (1,000s)

351

45.37

10.84

26.25

87.50

154.58

55.85

58.13

447.92

7.04

15.66

28.20

6.29

0.08

6.25

250.86

44.04

2000 Decennial Census Data

18.09

37.33

6.20

1.50

13.39

24.70

7.49

2.76

4.45

3.95

34.51

15.57

11.32

6.73

0.06

0.00

3.45

8.10

9.84

0.64

589.14

51.25

24.94

36.10

33.72

83.41

43.41

61.46

17.89

17.50

175.00

184.98

88.38

70.00

727.27

7.47

15.79

26.47

6.33

0.07

5.97

251.94

41.04

2007–2011 5-Year American Community Survey

351

18.55

38.45

8.37

2.21

14.97

22.00

9.48

4.10

4.95

4.68

0.12

0.00

6.60

2.73

39.77

16.08

11.70

7.77

5.38

0.22

609.94

67.90

43.20

42.92

33.91

83.35

35.35

78.75

26.09

30.00

175.00

324.07 131.73 137.50

875.00

7.98

16.97

0.08

272.01

in town schools in Massachusetts before and after successful override votes.
Town-years without override votes are used to control for trends in the racial
composition of schools over the time period. The key is that the identiﬁcation
comes from within-town changes in the racial composition of schools as a
result of override wins.

To determine the impact of override wins on the enrollment of non-
白人, I regress the percent nonwhite enrolled in town school districts,

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497

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

桌子 2. Comparison of Means for 1980 Demographics

Variable

Percent Nonwhite Enrolleda

Percent Black Enrolleda

Percent Nonwhite Residents

Percent Black Residents

Population

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

Family Income – 10th pct

Family Income – 25th pct

Family Income – 50th pct

Family Income – 75th pct

Family Income – 90th pct

House Prices – 10th pct

House Prices – 25th pct

House Prices – 50th pct

House Prices – 75th pct

House Prices – 90th pct

数字

Variable

Percent Nonwhite Enrolleda

Percent Black Enrolleda

Percent Nonwhite Residents

Percent Black Residents

Population

Percent Aged 65 or Older

Percent Aged 17 or Less

Percent with Bachelor’s Degree (25 和更老的)

Percent with No High School Degree (25 和更老的)

Family Income – 10th pct

Family Income – 25th pct

Family Income – 50th pct

Family Income – 75th pct

Family Income – 90th pct

House Prices – 10th pct

House Prices – 25th pct

House Prices – 50th pct

House Prices – 75th pct

House Prices – 90th pct

数字

Never Override

Ever Override

p-valueb

7.14
2.90
2.65
1.37
45.43
26.40
12.76
32.40
15.01
6.14
11.96
19.67
27.47
35.79
25.55
33.69
42.68
53.67
66.75

4.59
1.85
1.99
0.86
11.98
28.14
11.57
23.13
22.16
7.60
13.51
20.92
29.34
39.19
27.86
37.30
48.74
62.66
80.04

305

0.07
0.14
0.24
0.11
0.00
0.01
0.08
0.00
0.00
0.00
0.01
0.11
0.07
0.02
0.15
0.07
0.02
0.01
0.00

Never Win

Ever Win

p-valueb

5.43
1.46
1.86
0.68
20.00
28.71
11.48
30.44
14.22
6.91
12.43
19.69
27.17
36.18
24.37
31.94
41.10
52.16
64.31

252

4.38
1.95
2.02
0.90
10.27
28.02
11.59
21.59
23.84
7.75
13.74
21.18
29.80
39.83
28.59
38.42
50.34
64.87
83.34

0.51
0.27
0.69
0.20
0.00
0.22
0.84
0.00
0.00
0.01
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Notes: aData are from 1985.
bp-value for test of equal population means across groups.

498

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Jeffrey Zabel

PCTNW_ENROLL, on whether or not the town passed at least one override vote
in a given year, WIN. I include time dummies in the model because the number
of successful override votes is not evenly distributed over time and hence the
impact might be confounded by the positive trend in PCTNW_ENROLL (看
ﬁgure 3). The distribution of PCTNW_ENROLL is right-skewed so I take the
日志, which has a distribution that looks much more like a normal distribution.
The difference-in-difference model is thus

ln (PCTNW ENROLLit) = β0t +

K(西德:2)

j=0

β1jWINi,t−j +

中号(西德:2)

j=1

β2jWINi,t+j

K(西德:2)

j=0

β3jLOSEi,t−j +

中号(西德:2)

j=1

β4jLOSEi,t+j

+ β5tXi,1980 + ui + vit

(1)

where Xi,1980 is a vector of demographic factors from the 1980 Decen-
nial Census. I use data from 1980 because it predates observed values of
PCTNW_ENROLL and hence can be considered to be exogenous. As in Lutz
(2011), the coefﬁcient (向量) on Xi,1980 is allowed to vary over time to control
for trends in PCTNW_ENROLL that are related to the demographic variables
in Xi,1980. I include the percent of residents aged 25 and over with a bachelor’s
degree and without a high school degree; the percent of residents aged 17 或者
less and aged 65 or older; the logs of median house value, median family in-
come, the number of housing units, 和人口; and the percent nonwhite
in the town and the percent of housing units with four or more people.

It is likely that PCTNW_ENROLL and WIN are affected by factors such
as population, state aid, 收入, house prices, and local economic conditions
such as the unemployment rate. 所以, despite the fact that I have allowed
the impact of Xi,1980 to vary over time, there are still concerns with omitted
variables bias if the contemporaneous values of these factors are excluded.5
The problem with including these variables in Equation 1 is they are likely
to be endogenous due to unobserved factors that affect residential sorting.
此外, these variables are, 他们自己, likely to be affected by WIN so
that controlling for them will not allow for an estimate of the full effect of
successful overrides on the racial makeup of schools (IE。, the direct effect
plus the indirect effects on PCTNW_ENROLL that arise from the changes
in population and the other variables due to successful overrides). It is likely
these factors affect PCTNW_ENROLL with a lag but even only including lags
will not fully solve the endogeneity problems. 任何状况之下, I will include

I thank the referee for raising this point.

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499

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

contemporaneous and lagged values of population, state aid, 收入, 房子
价格, and the unemployment rate to see if they affect the estimates of the
impact of successful overrides on PCTNW_ENROLL.

Passing an override might not have an immediate impact on the percent
nonwhite enrolled in schools, as sorting across towns takes time to fully play
出去. I therefore include four lags of WIN in the model. This will also allow
me to see if any change in PCTNW_ENROLL is maintained over time. I also
include two leads of WIN. If towns that passed overrides tended to do so when
the racial composition of schools is changing, then any changes in PCTNW
that are caused by WIN should be measured relative to any changes prior to
the successful override.

I also include the variable, LOSE (plus two leads and four lags), that is an
indicator that all override ballots in a given town-year were unsuccessful. 这
question to answer by including LOSE is “does losing all override votes in a
year affect the percent nonwhite enrolled in the town’s schools?” This could
be viewed as a negative signal that might deter households from moving to
小镇 (or vice versa).

The key to the difference-in-difference framework is the inclusion of the
town ﬁxed effects, ut in equation 1. This implies the identiﬁcation of β1j and
β2j comes from within-town changes in PCTNW_ENROLL before and after an
override win. Note that by including LOSE in equation 1, the “control” group
is town-years with no override vote.

6. 结果
The Impact of Overrides on Town Demographics

To get some idea of the impact of successful overrides on town residential
作品, I look at changes in percent nonwhite, percent black, 的百分比
residents aged 25 and older with a bachelor’s degree, percent renters, 大众-
的, the percent of residents aged 17 or less and aged 65 or older, real family
收入, real median house values, the number of housing units, and the per-
cent of housing units with four or more people across decennial censuses for
1980, 1990, 和 2000.6 I also use the ﬁve-year ACS for 2007–2011 to generate
values for 2009, though these are estimates and not actual values as is the case
for the decennial censuses. I regress the decadal change in these variables in-
cluding the 10th, 25th, 50th, 75th, and 90th percentiles, the interquartile range,
and the 10th to 90th percentile range of the town’s real family income, 在
number of years over the decade in which there was a least one successful
override, DSUM, and whether there was at least one successful override in the

6. Note that the model developed in the previous section (方程 1) does not apply because these
data are only available in the Decennial Censuses. This is the reason I consider a model based on
decadal changes in these variables.

500

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Jeffrey Zabel

十年, DWIN (separate regressions for each successful override variable).7 我
run the regressions for all three decadal changes combined. 因此, 有
a total of 1,053 observations. I include base-year dummies and run regressions
with and without base-year values of these variables.

There are signiﬁcant negative effects of DWIN and/or DSUM on the per-
cent nonwhite, percent black, and percent of residents aged 65 or older, 和
signiﬁcant positive effects on the percent of residents aged 17 或更少, the per-
cent of housing units with four or more people, 人口, median real house
价格, and number of housing units, but no signiﬁcant effects on the percent
with a bachelor’s degree or the percent renters. These results suggest house-
holds with school-aged children are moving into towns in response to success-
ful overrides. These households are likely both replacing older households
without children in existing units and moving into new units. 此外,
there are signiﬁcant decreases not only in the percent nonwhite and percent
black residents as a result of successful overrides but in the number of non-
whites and blacks that reside in towns that pass overrides. 因此, the evidence
supports the result that successful overrides lead to a decline in nonwhites and
blacks in an absolute sense and not just in a relative sense.

In the case of the family income distribution, successful overrides over the
decade are signiﬁcantly positively correlated with increases in the 10th, 25th,
and 50th percentiles but not with changes in the 75th or 90th percentiles. 它
appears that successful overrides raise the lower half but do not affect the upper
portion of the town income distribution. There is also some mild evidence that
override wins lead to more homogeneity in town incomes, as declines in the
interquartile and 10–90 percentile ranges are associated with override wins.
全面的, these results support the hypothesis that successful overrides have
affected sorting across towns in Massachusetts.8 Given that sorting across
towns and schools are related, this supports the next step, which is to look at
the impact of successful overrides on school segregation.

The Impact of Overrides on School District Enrollments by Race/Ethnicity

I now turn to the impact of override behavior on the racial composition of
学校. Data on school enrollments at the town level are required because
this is the level at which overrides occur. One advantage of focusing on
Massachusetts is that school districts tend to coincide with town boundaries.
仍然, 仅有的 172 的 351 towns in Massachusetts have their own K–12 schools.
The remaining 179 towns send their children to schools in another town or

7. 当然, the change from 2000 到 2009 is only for nine years.
8. Results available on request.

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501

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

to a regional school for at least one grade (usually at least high school).9 的
这些 179, 任何地方从 65 到 118 (depending on the year) report information
on the characteristics of the students enrolled in the town schools. Towns
that do not have their own schools for any grades obviously do not report this
information and hence are excluded for most of the analysis.10

Data on school district enrollments by race are available starting in 1985.
I estimate equation 1 using town ﬁxed effects with standard errors clustered
at the town level. This is the difference-in-difference model. 再次, the iden-
tiﬁcation comes from within-town changes in PCTNW_ENROLL before and
after a successful override vote. None of the lags of WIN are signiﬁcant at even
这 10 percent level. The coefﬁcients for LOSE and its lead and lags are also
not signiﬁcant.11,12

Overrides must explicitly state the use of the funds proposed in the vote.
This allows me to determine which overrides were intended for school im-
provement. These make up 25.7 percent of all the override votes. 有
1,790 town-years where there was at least one override vote and 47.2 百分
of these include at least one override vote where the funds were intended for
学校. 的 1,140 town-years with at least one override win, 40 百分
have at least one successful vote that was dedicated to schools. 此外,
I allow for the impact to be different for town-years where all successful over-
rides are dedicated to schools and town-years where at least one, but not all,
successful overrides are earmarked for schools. These impacts might be dif-
ferent if the information about the successful overrides for schools is not as
salient when there are other override wins as they are for town-years where
all the successful overrides are going to schools or if the total dollar amount
of the overrides that go to schools is greater in the latter versus the former
案件. 另一方面, one might be concerned that such differentiation
is nonrandom and hence any difference in the results is due to unobserv-
able differences in these towns rather than actual differences in the impacts

9. One might think that when regional schools need to increase spending there would be coordination
across member towns in proposing overrides, but this does not appear to happen (Christine M.
林奇, Director, School Governance, MDOESE, personal communication). 此外, 城市
with excess capacity might be able to increase property taxes without resorting to an override vote.
10. Note that enrollments in regional schools are broken down by town but not by race in each town.
11. A comparable model is based on ﬁrst differences versus ﬁxed effects. The ﬁrst-difference estimator
is more efﬁcient if the error term is serial correlated. I tested for serial correlation using the xtserial
command in Stata. The p-value of the test is 0.014; hence the absence of serial correlation is not
rejected at the 1 percent level. 仍然, I ran the model using the ﬁrst-difference estimator and the
results are similar to those using the ﬁxed effects estimator.

12. Baseline town-level demographic and economic characteristics whose impacts vary by year are
included to control for trends in PCTNW_ENROLL that are related to these variables. An alternative
approach to controlling for general trends in PCTNW_ENROLL is town-speciﬁc linear trends. 什么时候
I include these linear trends in the model, the results are very similar to the results without the
linear trends.

502

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Jeffrey Zabel

他们自己. This concern should be assuaged by the inclusion of the town
ﬁxed effects and the town-level demographic variables.13

To recap, town-years with successful overrides are divided into three
团体: (1) those where all wins were dedicated to schools, (2) those with
multiple wins where some (but not all) were earmarked for schools, 和 (3)
those where none of the wins were intended for schools. I also differentiate
between override losses that include ones that were earmarked for schools and
ones that were not. Failed override votes that are dedicated for schools could
be viewed as a negative signal that might deter households who are interested
in good schools from moving to the town.

Results are given in table 3. It does appear that the impact on percent
nonwhite is signiﬁcant when successful overrides are only earmarked for
学校. The coefﬁcient estimates for WIN and its four lags are all negative
and jointly signiﬁcant at the 1 percent level, and lags 3 和 4 are individually
signiﬁcant at the ﬁve percent level or better. 这 (gross) semi-elasticities are
given in column (2); they range from −1.4 percent for the ﬁrst lag of WIN to
−8.5 percent for the fourth lag. The coefﬁcient estimates for the two leads of
WIN are neither individually nor jointly signiﬁcant at even the 10 percent level.
Although not signiﬁcant, the ﬁrst lead of WIN is negative with a semi-elasticity
of −1.9 percent. This is a weak indication that, 一般, successful override
votes occur in town-years where the percent nonwhite enrolled is lower than
in town-years that did not have an override vote. 因此, I also calculate the
net semi-elasticity—the percent change in the percent nonwhite relative to the
change in the year prior to the override win. These are also listed in column
2 of table 3. The net semi-elasticity for the third and fourth lags of WIN is
−5.3 percent and −6.7 percent, 分别.

There is not a statistically signiﬁcant impact on PCTNW_ENROLL when
there is a mix of override wins where at least one is earmarked for schools.
再次, this indicates override wins dedicated to schools may not carry as strong
a signal when there are successful overrides earmarked for other purposes.
There is also no impact when all successful overrides are to be used for non-
school purposes. 此外, override losses have no impact on the percent
of nonwhites enrolled in schools regardless of whether any were earmarked
for schools (results not included in table 3). 所以, it does not appear that
losing an override vote has an effect on the percent nonwhite enrolled in the
town’s schools.

Given that successful overrides earmarked for schools result in a decrease
in nonwhite enrollments, does this lead to an increase in segregation across

13. A comparison of means of observable characteristics across town-years where all successful over-
rides are dedicated to schools, and town-years where at least one but not all successful overrides
are earmarked for schools, produces very few signiﬁcant differences.

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503

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

桌子 3. Regression Results Where ln(PCTNW_ENROLL) Is Dependent Variable

Override Win Type

Wins for Schools Only

Mixed Wins

No Wins for Schools

VARIABLES

Coeff
Est
(1)

Semi Elast
Gross/Net
(2)

Coeff
Est
(3)

Semi Elast
Gross/Net
(4)

Coeff
Est
(5)

Semi Elast
Gross/Net
(6)

赢

wint−1

wint−2

wint−3

wint−4

WINT+1

WINT+2

−2.0/−0.1 −0.031
(0.026)

−3.1/−5.2

−0.020
(0.024)

−0.014
(0.021)

−0.027
(0.026)

−0.074∗∗
(0.030)

−1.4/0.6

−2.6/−0.7

−7.1/−5.3

−0.089∗∗∗ −8.5/−6.7
(0.025)

−0.019
(0.023)

0.006
(0.022)

−1.9

0.6

1.3/−1.0

0.4/−1.8

1.5/−0.7

1.2/−1.1

2.2

−0.8

0.012
(0.025)

0.004
(0.026)

0.015
(0.023)

0.011
(0.029)

0.022
(0.020)

−0.008
(0.029)

0.641

0.363

−0.4/0.3

−0.3/0.4

−1.4/−0.7

−0.4/0.3

−0.5/0.2

−0.7

0.0

(0.017)
−0.003

(0.016)
−0.014

(0.017)
−0.004

(0.015)
−0.005

(0.019)
−0.007

(0.020)
0.000

(0.020)
(0.017)

0.967

0.831

p-value: WIN + lags = 0

0.009

p-value: WIN leads = 0

0.219

观察结果

Number of Towns

6,643

0.667

288

Notes: Also included as explanatory variables: LOSE and 4 lags and 2 leads for overrides, 时间
dummies, Percent nonwhite, Percent Aged 65 or Older, Percent Aged 17 or Less, Percent with
Bachelor’s Degree, Percent No HS Degree, Percent of housing units with four or more people, 和
the natural logs of median family income, median house price, and the number of housing units all
从 1980 Decennial census.
Standard errors in parentheses.
∗∗ Statistically signiﬁcant at the 5% 等级; ∗∗∗ statistically signiﬁcant at the 1% 等级.

城市? This would be true if these overrides occurred in towns where the
percent nonwhite enrolled in schools was below average (or median). 的
290 town-school districts where I have enrollment data by race, 106 had at
least one year with override wins only devoted to schools, 36 had at least one
year with a mix of wins devoted to schools and other purposes, 57 had override
wins only devoted to nonschool purposes, 和 91 never had an override win. 我
then compare the percent of nonwhite enrollments in the 106 towns that had
at least one year with override wins only devoted to schools with those in the
91 towns that never had an override win. Evidence supporting the hypothesis

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that successful overrides earmarked for schools lead to increased segregation
would show lower nonwhite enrollments in the former set of towns as com-
pared to the latter.

桌子 4 gives the means of PCTNW_ENROLL for these two groups of
towns for 1985, 1990, 1995, 2000, 2005, 和 2010. The mean (median) 的
PCTNW_ENROLL in towns where there was at least one year with override
wins devoted only to schools is always less than the mean (median) for towns
that never had an override win—this difference grows over time. 相似的
results hold for blacks and Hispanics but not for Asians (见表 4).

数字 4 provides a visual representation of this result. It displays the
cumulative distribution function (CDF) for PCTNW_ENROLL in towns where
there was at least one year with override wins devoted only to schools and
in towns that never had an override win for ﬁve-year intervals. 鉴于
PCTNW_ENROLL is lower in the former group of towns, then its CDF for
PCTNW_ENROLL should lie above the CDF for the latter group of towns. 在
事实, this is the case, and furthermore, the difference between the CDFs grows
随着时间的推移. 当然, the increase in these differentials could be due to factors
other than override wins, but what is clear is that those towns passing overrides
earmarked for schools have fewer minorities enrolled than towns that never
pass overrides.

What about reverse causality? Could an override be in response to in-
creasing enrollments (particularly those dedicated to schools)? I run a simple
regression of overrides dedicated to schools on the log of total enrollments and
its four lags and a separate regression on town enrollments and its four lags,
time dummies, and town ﬁxed effects. There is no signiﬁcant evidence that
enrollments in the current or previous four years have an effect on putting
an override on the ballot. 仍然, to account for any differences in the out-
come variable (whether it is enrollments or otherwise) I calculate net semi-
elasticities as the relevant measures of the impact of successful overrides as
they are measured with respect to the impact of the ﬁrst lead on the outcome
variable.

What about the role of state aid? Maybe there is a decline in state aid in
towns that pass overrides that might counteract any impact from the successful
override. I run a regression with the log of real net state aid per capita as the
dependent variable and WIN and its four leads and four lags, LOSE and its four
leads and four lags, time dummies, and town ﬁxed effects as the independent
变量. There is no signiﬁcant evidence of a reduction in state aid when an
override is passed or when it fails.

As I discussed in section 5, it is likely that PCTNW_ENROLL and WIN
are affected by factors such as population, state aid, 收入, house prices, 和
local economic conditions such as the unemployment rate. Excluding them

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505

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

桌子 4. Mean/Median for Percent Enrolled for Towns That Had at Least a Year with Override Wins
Earmarked Only for Schools and Towns that Never Had Override Wins

Never Win

At Least One Win for Schools

意思是

(1)

Median

(2)

意思是

(3)

Median

(4)

p-valuea

(5)

Percent Nonwhite Enrolled

6.22

10.13

13.12

15.45

18.67

24.05

2.13

2.98

3.77

4.30

5.01

4.98

0.75

1.38

6.23

7.35

9.30

12.29

1.23

2.48

3.00

3.55

4.04

4.33

4.02

5.23

5.93

6.73

8.69

12.73

Percent Black Enrolled

1.83

1.99

2.18

2.20

2.28

2.17

Percent Hispanic Enrolled

0.25

0.40

1.51

1.85

2.47

3.63

Percent Asian Enrolled

1.32

1.80

2.08

2.51

3.60

4.19

0.08

0.01

0.00

0.62

0.16

0.04

0.02

0.00

0.67

0.13

0.09

0.11

0.54

0.87

年

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

Notes: ap-value for the comparison of means for towns that never had a winning override (柱子 1)
versus towns that had at least one year with wins earmarked only for schools (柱子 3).

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Jeffrey Zabel

0
0
1

0
8

0
6

0
4

0
0
1

0
8

0
6

0
4

0
2

1985

20
80
Pct Nonwhite Enrolled

2000

20
80
Pct Nonwhite Enrolled

0
0
1

0
8

0
6

0
4

0
2

100

0
0
1

0
8

0
6

0
4

0
2

100

1990

20
80
Pct Nonwhite Enrolled

2005

20
80
Pct Nonwhite Enrolled

0
0
1

0
8

0
6

0
4

0
2

100

0
0
1

0
8

0
6

0
4

0
2

100

1995

20
80
Pct Nonwhite Enrolled

2010

20
80
Pct Nonwhite Enrolled

100

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Ever Pass Override

Never Passed Override

数字 4. CDFs for Percent Nonwhite Enrolled: Towns That Ever and Never Pass Overrides.

has the potential of generating omitted variables bias. Because these variables
are likely to be endogenous, I ﬁrst include only their lags and then add in
the contemporaneous values. In both cases none of the variables in levels or
lags are individually signiﬁcant at the 1 percent level and there is little impact
on the coefﬁcient estimates for the successful override variables. It appears
that allowing the impact of the 1980 Decennial Census variables to vary over
time has done a good job in accounting for the impact of factors such as
人口, state aid, 收入, house prices, and the unemployment rate on
PCTNW_ENROLL and WIN.

The impact of successful overrides could be tied to the size of the over-
ride. I replace WIN (an indicator of a successful override) 与 (真实的)
per capita amount of the successful override, AMOUNT_PC. I distinguish
between amounts dedicated to schools when all wins are only for schools,
AMOUNT_PC_SCHOOLS_ALL, when only some of the wins are for schools,
AMOUNT_PC_SCHOOLS_SOME, and when none of the wins are for
学校, AMOUNT_PC_OTHER. The results are comparable to those when
indicators of override wins are included;
the coefﬁcient estimates for
AMOUNT_PC_SCHOOLS_ALL and its four lags are all negative and jointly
signiﬁcant, and the third and fourth lags are individually signiﬁcant. 还,
evaluating the impacts at the median override amount for schools results in
similar impacts as those obtained using WIN (见表 3). 差异在于

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507

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

the impact on PCTNW_ENROLL for the 5th percentile override amount and
the 95th percentile override amount for an override that was passed four years
earlier is 2.3 百分. This compares with a net semi-elasticity of 6.7 百分
(桌子 3). So there is some heterogeneity in the impact on PCTNW_ENROLL
from an override win based on the amount of the override.

The median real per capita amount of the override when override wins
are only for schools is $26.20 and the median real per capita amount of the override when only some of the wins are for schools is $19.40. It could
是, 所以, that the reason override wins solely devoted to schools affect
enrollments and wins that are only partially earmarked for schools do not
is that the amount of money for schools is larger in the former case. 但
this reasoning is only partially correct because the coefﬁcient estimates for
AMOUNT_PC_SCHOOLS_SOME and its four lags are insigniﬁcant. 这是
also the case for AMOUNT_PC_OTHER.

I next look at the impact of successful debt, WIN_DEBT, and capital ex-
clusions, WIN_CAP, on percent nonwhite enrolled. 通常, expenditures
earmarked for schools from debt and capital exclusions go toward physical
plant upgrades, whereas overrides designated for schools go toward the school
operating budget, of which a large portion is teacher salaries. 因此, the impact
of a successful debt or capital exclusion on percent nonwhite enrolled could
differ from the impact of a successful override.

I estimate equation 1 replacing WIN with WIN_DEBT and WIN_CAP,
分别. In both cases, I differentiate override wins for schools only, a mix
of schools and other purposes, and no wins earmarked for schools. Neither
WIN_DEBT and its fours lags and two leads nor WIN_CAP and its fours lags
and two leads are individually or jointly signiﬁcant. 下一个, I include override,
debt exclusion, and capital exclusion wins in the same regression. 第一的, 这
results for successful overrides are essentially unchanged. 第二, debt and
capital exclusion wins still do not signiﬁcantly affect percent nonwhite enrolled
whether they are dedicated to schools or otherwise.

The impact of override wins might differ by race/ethnicity. I run the model
with differential effects by type of override using the natural log of the percent
黑色的, PCTBLACK_ENROLL, Hispanic, PCTHISPANIC_ENROLL, and Asian,
PCTASIAN_ENROLL, enrollment as the dependent variable. The results are
given in table 5. The impacts of override wins exclusively dedicated to schools
on the percent enrollments for blacks are all insigniﬁcant—the largest impact
(in magnitude) is −3.0 percent. Only the impact of the fourth lag of override
wins exclusively dedicated to schools on the percent enrollments for Hispanics
is signiﬁcant. The estimate for the ﬁrst lead is actually positive so the net semi-
elasticities are all negative, and the values for the third and fourth lags are
−4.6 percent and −8.8 percent, 分别. The impacts of override wins

508

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桌子 5. Regression Results for Black, Hispanic, and Asian Enrollments

Dependent Variable (in natural logs)

Percent Black

Percent Hispanic

Percent Asian

VARIABLES

Coeff

Semi Elast

Coeff

Semi Elast

Coeff

Semi Elast

Est

(1)

Gross/Net

(2)

Est

(3)

Gross/Net

(4)

Est

(5)

Gross/Net

(6)

赢

wint−1

wint−2

wint−3

wint−4

WINT+1

WINT+2

−0.030
(0.020)

−0.016
(0.022)

0.009
(0.027)

−0.031
(0.022)

−0.023
(0.022)

−0.003
(0.021)

0.002
(0.019)

p-value: WIN + lags = 0

0.287

p-value: WIN leads = 0

0.965

Wins Only for Schools

−3.0/−2.7

0.013
(0.025)

1.3/−1.0

−0.022
(0.024)

−1.6/−1.3 −0.008
(0.024)

−0.8/−3.0 −0.004
(0.023)

0.9/1.2

0.006
(0.027)

0.7/−1.6

−0.027
(0.023)

−3.0/−2.7 −0.025
(0.035)

−2.5/−4.6 −0.027
(0.028)

−2.2/−1.9 −0.070∗∗ −6.8/−8.8 −0.027
(0.029)

(0.030)

2.3

2.4

−0.3

0.2

0.022
(0.023)

0.023
(0.030)

0.032

0.616

赢

wint−1

wint−2

wint−3

wint−4

WINT+1

WINT+2

Mixed Wins

−0.026
(0.023)

−2.5/−4.0 −0.036
(0.031)

−3.5/0.3

0.026
(0.025)

0.005
(0.024)

0.008
(0.022)

0.015
(0.025)

0.016
(0.022)

−0.003
(0.024)

2.6/1.1

0.5/−1.1

0.8/−0.8

1.5/−0.1

1.6

−0.3

−4.1/−0.4

2.0/6.0

2.7/6.8

4.6/8.7

−3.8

−1.4

−0.042
(0.032)

0.020
(0.032)

0.027
(0.034)

0.045
(0.035)

−0.039
(0.031)

−0.014
(0.039)

0.028

0.355

p-value: WIN + lags = 0

0.309

p-value: WIN leads = 0

0.646

−2.2/1.9

−0.4/3.8

−2.7/1.4

−2.7/1.3

−2.7/1.4

−4.0

−1.9

0.4/−2.9

0.8/−2.6

1.1/−2.3

−2.6/−5.9

−2.4/−5.7

3.5

−0.9

−0.041∗
(0.021)

−0.020
(0.018)

0.462

0.136

0.004
(0.027)

0.008
(0.029)

0.011
(0.023)

−0.027
(0.022)

−0.025
(0.028)

0.034
(0.022)

−0.009
(0.024)

0.413

0.068

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509

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

桌子 5. Continued.

Dependent Variable (in natural logs)

Percent Black

Percent Hispanic

Percent Asian

VARIABLES

Coeff

Semi Elast

Coeff

Semi Elast

Coeff

Semi Elast

Est

(1)

Gross/Net

(2)

Est

(3)

Gross/Net

(4)

Est

(5)

Gross/Net

(6)

赢

wint−1

wint−2

wint−3

wint−4

WINT+1

WINT+2

0.025
(0.016)

0.014
(0.016)

0.000
(0.016)

0.001
(0.015)

0.019
(0.017)

0.007
(0.017)

0.005
(0.018)

2.6/1.8

1.4/0.7

0.0/−0.7

0.1/−0.6

1.9/1.2

0.7

0.5

p-value: WIN + lags = 0

0.419

p-value: WIN leads = 0

0.918

观察结果

Number of Towns

6,643

0.303

288

−2.3/0.2

−0.6/2.0

−0.1/2.4

−2.3/0.3

−1.7/0.9

−2.5

−1.7

No Wins for Schools

−0.001
(0.025)

−0.011
(0.024)

0.004
(0.025)

−0.1/−1.7 −0.024
(0.015)

−1.1/−2.7 −0.006
(0.015)

0.4/−1.2

−0.001
(0.016)

−0.007
(0.023)

−0.7/−2.3 −0.023
(0.015)

2.0/0.4

1.6

2.5

0.020
(0.024)

0.016
(0.026)

0.025
(0.025)

0.571

0.592

5,651

0.579

269

−0.017
(0.018)

−0.026∗
(0.015)

−0.017
(0.019)

0.190

0.222

6,643

0.406

288

Notes: Also included as explanatory variables: LOSE and 4 lags and 2 leads, time dummies, 百分比
nonwhite, Percent Aged 65 or Older, Percent Aged 17 or Less, Percent with BA, Percent No HS
Degree, Percent of housing units with four or more people, and the natural logs of median family
收入, median house price, and the number of housing units all from the 1980 Decennial census.
Standard errors in parentheses.
∗Statistically signiﬁcant at the 10% 等级; ∗∗statistically signiﬁcant at the 5% 等级.

exclusively dedicated to schools on the percent enrollments for Asians are all
negative but none are signiﬁcant. The coefﬁcient estimate for the ﬁrst lead is
negative and signiﬁcant so the net elasticities are all positive; override wins
exclusively dedicated to schools appear to occur in towns where the enrollment
rates for Asians are lower than in towns with no override votes.

Note that the R2 for the regression with percent black enrolled as the
dependent variable (0.303) is much lower than the regression using percent
nonwhite (0.667). This is likely due to the small percent of blacks (和它的

510

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Jeffrey Zabel

variation) enrolled in Massachusetts schools, which leads to a much worse
ﬁt for this regression and less precision in the coefﬁcient estimates. 这
may explain the imprecision of the coefﬁcient estimates for WIN and its
lags (though the magnitudes are also generally smaller than those for the
PCTNW_ENROLL regression).

As previously mentioned, 172 towns have K–12 schools and hence students
attend schools only in their town. One might believe the impacts of overrides
would be even stronger in these towns. I run regressions for (the natural log
的) PCTNW_ENROLL, PCTBLACK_ENROLL, PCTHISP_ENROLL, and PC-
TASIAN_ENROLL that are limited to towns with K–12 schools. If anything, 这
results are somewhat weaker for PCTNW_ENROLL and PCTHISP_ENROLL,
but they do show stronger results for PCTBLACK_ENROLL. It appears that
the negative impact of successful overrides dedicated to schools is felt most by
Hispanics and blacks but not by Asians.

7. 结论
This study investigated an unintended consequence of the property tax limita-
tion law in Massachusetts—the impact of successful overrides on segregation
in Massachusetts town school districts. I ﬁnd evidence that successful overrides
earmarked for schools do reduce nonwhite enrollments in a town’s schools.
The impact is largest four years after the win; nonwhite enrollments fall by
6.7 percent four years after the override is passed. The results show towns that
do pass overrides intended for schools have lower minority enrollments than
those towns that do not, and therefore the reduction in nonwhite enrollments
does result in more segregation in town-school districts. Given that the mean
number of years for a town with at least one override win is three, this can lead
to a substantial reduction in nonwhite enrollments over the long run.

The amount of the override does appear to matter. A successful override
at the 95th percentile of the override amount distribution will lead to a decline
in the percent of nonwhite enrollments of 7.9 百分, whereas a successful
override at the 5th percentile will lead to a decline in the percent of nonwhite
enrollments of 5.5 百分.

There does not appear to be a signiﬁcant impact of successful overrides on
enrollments when other successful overrides in the same year are earmarked
for nonschool purposes. There are two reasons why this might be the case.
第一的, for these overrides, the median real per capita amount of successful
overrides in a given year is $19.40, whereas the median real per capita amount of the override when wins are only for schools is $26.20. So the average
impact on enrollments of a successful override when there are other successful
overrides not dedicated to schools should be smaller than for override wins

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511

PROPOSITION 2 1
2

AND SCHOOL SEGREGATION

that are only for schools. 第二, it could be that the information about the
successful overrides for schools is not as salient when there are other override
wins as compared to when all the successful overrides are going to schools.

The results indicate that successful capital and debt exclusions earmarked
for schools do not impact school enrollments. Why might this be the case?
The main difference between overrides and capital and debt exclusions that
are earmarked for schools is that the latter are generally used for school-related
capital projects whereas overrides are used for noncapital school expenditures.
Note that my results are consistent with Cellini, 费雷拉, and Rothstein (2010),
who ﬁnd that the passage of bonds for school infrastructure spending results
in no impact on test scores, enrollments, or racial makeup in California school
districts. This supports the conclusion that additional capital school expendi-
tures that are the result of successful overrides/bond referenda do not affect
school characteristics, whereas successful votes for noncapital school expen-
ditures do have an impact. It appears that, in terms of school enrollments,
households react more to increases in spending on noncapital budget items
(such as more teachers) than they do to expenditures on new buildings.14

What does this say about policy? States that are thinking about implement-
ing property tax limitations should be aware that there may well be unintended
consequences of such laws. Depending on the goals of the law, 可能性
of increased segregation might make it less desirable.

One interesting issue for future research is whether there are spillover
effects of successful overrides on nearby towns. 尤其, are there any
spillover effects when one town in a regional school district passes an override
vote and another does not?

I would like to thank Eric Brunner, an anonymous referee, and the editors for their
helpful comments, and to participants at the Property Tax and Financing of K–12
Education conference at the Lincoln Institute for additional useful feedback.

参考
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14. My results are not consistent with Neilson and Zimmerman (2011), who ﬁnd that a large school
construction project in New Haven, CT, signiﬁcantly affected test scores and enrollments. 但是这个
is a large project in a single urban school district that did not arise through the referenda process.

512

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