How Do Elites Capture a Democracy?
Evidence from the Struggle to Control
Congressional Redistricting
Dahyeon Jeong∗
Ajay Shenoy†
September 4, 2016
First Version: June 20, 2016
Abstract
We test for whether political parties can exert precise control over the
outcomes of high-stakes elections. We study elections that determine which
party controls Congressional redistricting, which allows a party to construct
districts that favor its own Congressional candidates. There is a discontinuous change in a party’s control of redistricting when the share of seats
won in the state legislature exceeds 50 percent. We show that a state’s incumbent party can precisely sort onto the winning side of the discontinuity. Parties sort to control redistricting in states where they have suffered
recent Congressional losses. These declines are reversed by redistricting.
(JEL Codes: D72,D73,J11)
Preliminary and incomplete.
Comments and suggestions welcome.
∗
University of California, Santa Cruz; email at [email protected]
University of California, Santa Cruz; Corresponding author: email at [email protected].
Phone: (831) 359-3389. Website: http://people.ucsc.edu/∼azshenoy. Postal Address: Rm.
E2455, University of California, M/S Economics Department, 1156 High Street, Santa Cruz CA,
95064. We are grateful to Gianluca Casapietra, Afroviti Demolli, Benjamin Ewing, Samantha
Hamilton, Nicole Kinney, Lindsey Newman, Erica Pohler-Chapman, Abir Rashid, Kevin Troxell, Auralee Walmer, and Christina Wong for excellent research assistance on this paper. We
thank George Bulman, Carlos Dobkin, Justin Marion, Jon Robinson, Alan Spearot, and seminar
participants at U.C. Santa Cruz for helpful suggestions.
†
2
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JEONG AND SHENOY
Introduction
One hallmark of a true democracy is that the outcome of an election is the
choice of voters rather than politicians. Though politicians can influence the
outcome by choosing a popular platform or running an effective campaign, the
precise outcome depends on the attitudes of voters and thus remains uncertain. It is only in sham democracies—those in which the polls are rigged—that
politicians can choose the precise outcome they want. It is typically assumed
such precision is impossible in a real democracy.
This paper presents evidence to the contrary. We show that political parties can exert remarkably precise control over the outcomes of elections even
in the U.S., typically considered among the most free and fair of democracies.
Our approach exploits the natural experiment created by U.S. Congressional
redistricting—or rather, the desire to control redistricting. The U.S. Constitution mandates that Congressional districts must be redrawn every ten years to
ensure all districts contain the same number of people. The boundaries of a
district determine how many left- or right-leaning voters a candidate will face.
The party that controls redistricting can potentially redraw boundaries to favor
its own candidates, reaping a windfall in Congress. As a result, the two parties
have a strong incentive to seize or retain control of redistricting.
To test whether they succeed, our design exploits both the rules and timing of redistricting. In most states, a redistricting plan is passed as regular legislation by the state legislature. The party that controls any chamber of the
legislature—in particular, the lower house—has at least a veto over any redistricting plan. Control of the lower house passes discontinuously from Republicans to Democrats when their share of seats crosses the threshold of 50 percent.
If they win these seats in the state election just prior to redistricting, they also
discontinuously gain a veto over redistricting. In these crucial elections, each
party has a strong incentive to choose an outcome on one or the other side of
this 50 percent threshold.
We test for sorting at this threshold. To be precise, we test for whether predetermined outcomes—the identity of the incumbent party, the density of the
running variable, and the outcome of Congressional elections prior to redistricting—
jump at the threshold. Similar tests are commonly used to test for whether
HOW DO ELITES CAPTURE A DEMOCRACY?
3
teachers manipulate test scores or workers manipulate taxable income to put
themselves over a critical threshold. What is novel about our context is that
the running variable—the outcome being manipulated—is the outcome of an
election. If there is a discontinuity in predetermined outcomes, it suggests the
outcome of the election—that is, the identity of the ruling party—is nearly void
of uncertainty.
Could a lack of uncertainty be a natural feature of elections rather than the
result of conscious effort? We answer this question by exploiting the timing of
redistricting. It is the state assembly election just prior to decennial redistricting
that determines which party controls redistricting. If uncertainty is a natural
feature of elections there should be sorting in every election. But if the sorting
only appears in the crucial elections that determine control of redistricting, it
suggests there is conscious effort to capture that institution.
We find strong evidence of sorting in redistricting elections, most visibly on
the identity of the state’s incumbent party. The probability that the Democrats
are the incumbent, meaning that they won a majority in the lower house in the
previous election, jumps by 44 percent in redistricting elections. This jump remains even when we restrict our sample to elections won by a margin of less
than 4 percentage points. The discontinuity suggests that the incumbent party
is able to ensure with great precision that it remains on the proper side of the
threshold. The discontinuity is visible in the conditional density of the election
outcome. Among states where Democrats are the incumbent party, they are 3
times as likely to win 50 to 53 percent of the seats as they are to win 47 to 50
percent. We find that these large and statistically significant discontinuities appear only in redistricting elections. In elections that do not determine control
of redistricting, there is no evidence of a discontinuity. As a result, the autocorrelation in incumbency rises from less than 40 percent to more than 60 percent
in redistricting elections.
We provide some evidence of the potential mechanisms through which incumbents retain control. We find that in the most competitive states—those in
which both parties have a similar number of incumbents in the lower house—
there is a decrease in the rate at which incumbents choose not to seek reelection falls. The decrease is steeper in redistricting elections, suggesting the parties lean on their candidates to seek reelection in these crucial elections. We
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JEONG AND SHENOY
find a similar pattern for campaign contributions to candidates for the lower
house. Total receipts rise sharply in closely contested elections during redistricting years. Tools like incumbent reelection and campaign finance may give
the two parties legal means to ensure they land on the correct side of the discontinuity.
Having explored how parties capture redistricting, we then explore why they
do it. We show that the parties sort states around the 50 percent threshold to ensure they control redistricting in states where they have suffered recent losses
in U.S. House elections. We show that these losses are partly reversed immediately after redistricting. Assuming the pre-existing trends on which the parties
sorted did not immediately reverse for reasons unrelated to redistricting, this
result suggests parties redraw districts to their advantage.
To test whether this reversal might be caused by the manipulation of district
boundaries, we match geocoded census data to Congressional district boundaries. We show that African Americans, who overwhelmingly support Democrats,
are discontinuously more likely to be moved to a new district in states where
Republicans control redistricting. This pattern does not hold for other demographic groups. Conditional on being moved, African Americans are more likely
to be packed into racially segregated districts that minimize the number of House
races they can influence. Given that states are sorted on pre-trends around the
threshold, it is impossible to say with certainty that this difference in treatment
is caused by the difference in the party that controls redistricting. However, we
find no evidence to support the most plausible alternative explanations. For
example, we find no evidence that African Americans in Republican-controlled
states are more likely to live in over- or under-populated districts that need to
be redrawn.
Our key contribution is to show that when the stakes are high a ruling party
may, through legal but costly means, eliminate the uncertainty of an election.
They are willing to pay these costs when the outcome of the election determines
control of a key institution that can itself make elections less competitive. Prior
work has shown that elites may sway elections by redirecting spending or public goods, but that such efforts succeed mainly in immature democracies. (e.g.
Akhmedov and Zhuravskaya, 2004; Brender and Drazen, 2008). Our work suggests that modern campaign tactics can be no less effective when applied to
HOW DO ELITES CAPTURE A DEMOCRACY?
5
even a democracy as mature as the United States. Without resorting to fraud,
political parties can make the overall outcome of crucial elections almost certain.
1.1
Related Literature
This paper most directly contributes to the literature on how politicians use legal or illegal means to maintain control of elected office. This literature has
found that incumbents will increase spending in election years (Nordhaus, 1975;
Drazen and Eslava, 2010); allocate jobs, public goods or popular reforms to
swing districts (Folke et al., 2011; Bardhan and Mookherjee, 2006; Baskaran et al.,
2015; Nagavarapu and Sekhri, 2014); exploit the control of one level of government to increase the odds of winning at another (Curto-Grau et al., 2011); or
alter the electoral system to marginalize opposition (Trebbi et al., 2008). These
studies have found tactics to be effective.
But as noted earlier, other work has shown that the attempt may fail or even
backfire in mature democracies and in the presence of independent institutions
(Peltzman, 1992; Akhmedov and Zhuravskaya, 2004; Brender and Drazen, 2008;
Matsusaka, 2009; Durante and Knight, 2012; Fujiwara and Wantchekon, 2013).
Our work suggests these may simply be the wrong tactics for a mature democracy. By exploiting campaign financing and the overwhelming electoral advantage of incumbent candidates, the ruling party can maintain its majority.
Our work is also related to the recent literature in political science on whether
outcomes of close elections are as good as random. Using an approach similar
to ours, several papers have found evidence of sorting in close elections for the
U.S. House (Snyder, 2005; Caughey and Sekhon, 2011; Grimmer et al., 2012).
Other work has disputed their conclusions or shown that they are not a general
feature of close elections in other contexts (Eggers et al., 2015; de la Cuesta and
Imai, 2016). Our work is distinct in two ways. First, the papers cited largely focus
on the methodological question of whether the “close elections” approach first
used by Lee et al. (2004) is valid rather than the broader question of whether political parties can manipulate outcomes to seize control of crucial institutions.
Their focus on methodology is in part because of the second distinction: they
focus on the outcomes of individual races between candidates rather than the
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JEONG AND SHENOY
aggregate outcomes of elections. That the more experienced or better financed
candidate can edge out victory in a close election may have little impact on the
composition or the policies enacted by the legislature as a whole. By contrast,
we test whether the incumbent party can edge out victory to remain in control
of the legislature.
Finally, our work extends the vast empirical literature on partisan redistricting in the U.S. 1 The literature has generally taken two approaches. The first
uses simulations to evaluate the fairness of a redistricting plan (Gelman and
King, 1990, 1994a,b; Engstrom, 2006; Glazer et al., 1987; McCarty et al., 2009;
Chen and Rodden, 2013; Chen, 2016). The second compares actual election
outcomes under different redistricting plans (Brunell and Grofman, 2005; Hetherington et al., 2003; Grainger, 2010; Ansolabehere and Snyder Jr, 2012; Carson
et al., 2007; McCarty et al., 2009; Lo, 2013).
To our knowledge, however, the literature has not studied what actions political parties take to seize control of redistricting. Moreover, the most common
conclusion of the literature— that control of redistricting yields little benefit—
seems inconsistent with our finding that political parties take pains to control
it. The inconsistency may arise because, as we show, the parties aim to control
redistricting in states where they are losing seats in Congress. A research design
that does not account for this pre-existing trend may conclude that control of
redistricting has zero or even negative benefit.2
2
Background: Congressional Redistricting
Partisan redistricting, or “gerrymandering,” is at least as old as the Republic.
Its first victim was James Madison, the mastermind of the U.S. Constitution,
who was forced to run for office in a district drawn by his political opponents
(Weber, 1988).3 Ironically it was Madison’s future running mate, Elbridge Gerry,
1
There is a related but distinct literature on incumbent redistricting.Abramowitz et al. (2006),
Friedman and Holden (2009), and Carson et al. (2014) study whether politicians redraw districts
not to favor one party but to favor incumbents of all parties.
2
This paper is also related to theoretical work that identifies how a party should gerrymander. See, for example, Owen and Grofman (1988); Friedman and Holden (2008); Puppe and
Tasnádi (2009); Cox and Holden (2011); Gul and Pesendorfer (2010); Shotts (2001, 2002).
3
The district was drawn by the Anti-Federalists, led by Patrick Henry, as punishment for
Madison’s defense of the Constitution. Despite Henry’s efforts, Madison won nevertheless.
7
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 1
An Example of Gerrymandering
Outcome:
3 democrats
0 Republicans
D
Outcome:
2 democrats
1 Republicans
D
D
D
R
D
D
R
D
R
2
D
R
D
1
D
R
D
D
R
who as governor of Massachusetts signed into law the politically favorable but
salamander-shaped district that was first called the “Gerrymander.”
Figure 1 shows a simple example of how gerrymandering might work. The
top and bottom panel show the same hypothetical state under two redistricting plans. In the first plan, the state’s 6 Democratic and 3 Republican voters
are split evenly between three Congressional districts. Assuming all of them
vote, the Democrats will win all three seats. In the second plan, the lines are
contorted to give Republicans a bare majority in one district (labeled 1). As a
result, an equally contorted second district is created with only Democrats (labeled 2). This practice is commonly called “packing and cracking.” In this case,
Democrats have been packed into District 2 and cracked (given bare minorities) in District 1. As a result the Republicans, without gaining any additional
support, have gained a seat.
Such gerrymandering has two visible consequences. The first appears in the
distribution across districts of the opponents of the gerrymandering party. Suppose that in the absence of gerrymandering, the proportion of these opponents
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JEONG AND SHENOY
would have the distribution given in the top panel of Figure 2. Gerrymandering
would transform this distribution into that shown in the bottom panel. First,
they would move opponents out of districts in which they are a bare majority. This reduces the mass of districts just above 0.5. They would then combine
these opponents into districts where they form the overwhelming majority (like
District 2 in Figure 1), increasing the mass at the top of the distribution. Though
these districts would be lost with certainty, the benefit is that there would be an
increase in districts in which opponents are a minority (like District 1 in Figure
1). As a result, mass shifts from just above to just below 50 percent.4
The other visible consequence of gerrymandering is in the district boundaries themselves. Contorted districts, like those in the bottom panel of Figure
1, often have longer boundaries without having a larger area. One measure of
such contortion is the ratio of the perimeter to the square root of the area. (Taking the square root ensures both numerator and denominator have comparable
units.) Figure 3 shows several examples of this perimeter-area ratio. A square
has a perimeter-area ratio of 4. A relatively simple district, like the rectangular
Kansas 3rd district, has a perimeter-area ratio of 4.9—not much more complex.
But the Texas 18th district, with its irregular lines and gaps, has a ratio of over 36.
Figure 4 plots the median perimeter-area ratio of all districts over time. In
the early part of the past century, state legislatures made few changes to district lines to avoid having to face new voters. As a result, the ratio changes little
up through the 1960s. Only after the Supreme Court ruled in Baker v. Carr 369
(1962) and Wesberry v. Sanders 376 (1964) that their failure to redistrict was unconstitutional did states start redistricting regularly. After the ruling nearly all
states that were apportioned more than one Congressional district started redrawing their districts in the year after the decennial census.5 Starting in 1971,
the ratio jumps in the years ending in 1. By and large it jumps upward, suggest4
The “optimal” way to gerrymander, as described in Friedman and Holden (2008), is actually
rather more sophisticated than this. It requires using the party’s most ardent supporters to neutralize its most ardent foes. However, the common perception is that parties do not attempt this
more complex approach. As we show below, our results are consistent with the simpler practice
of packing and cracking. It is possible that constraints of both geography and information—the
would-be gerrymanderer might not be able to identify the strength of a voter’s left-right bias—
prevents optimal gerrymandering.
5
The sole regular exception is Maine, which redistricts two years afterward. There are some
cases (e.g. Texas in 2003) when states have chosen to redistrict again later in the cycle. We do
not exploit this variation, as the decision to redistrict may itself be endogenous.
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 2
Distribution of Political Opponents
after Gerrymandering
0.5
Density
Natural Distribution:
Density
Gerrymandered:
3. …to leave behind
more districts in which
they form minorities.
1. Move opponents out of
districts where they form
slight majorities…
2. …pack them into
segregated districts…
Proportion of Political Opponents
9
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JEONG AND SHENOY
Figure 3
Simple and Complex Districts
Perimeter-Area Ratio
4
Perfect Square
4.89619
Kansas 3rd District
78th – 87th Congress
Note: Perimeter-Area ratio is defined as P erimeter ÷
√
36.50831
Texas 18th District
103rd -104th Congress
Area.
ing districts have become increasingly contorted.6
3
Research Design
Our approach is to test for sorting in the margin of seats won by Democrats in
the lower house of the state legislature. We run this test separately for elections
that do and do not determine control of redistricting. This approach yields two
sources of variation: the rules of redistricting, which in most states allow redistricting through normal legislation; and the timing of redistricting, which
makes winning the election just before redistricting far more important.
Our first source of variation arises because control of the lower house of the
state legislature grants a measure of control—at least a veto—over redistricting.
Control of the lower house switches discontinuously away from Republicans
6
The year 1991 is an exception. This may be because in that year states tried to make their
districts more compact, or it may be a shortcoming of the perimeter-area ratio as a measure of
contortedness.
11
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 4
Median Perimeter-Area Ratio
7
7.5
6.5
8
District Boundaries Grow Increasingly Complex
6
Pre-Redistricting
1901
1921
1941
Note: Perimeter-Area ratio is defined as P erimeter ÷
√
Post-Redistricting
1961
Year
1981
2001
Area.
when Democrats win at least 50 percent of seats. Define the seat margin as
[Democratic Seat M argin](s,t) =
[Democrats in State Assembly](s,t) − 12 [T otal Assembly M embers](s,t)
[T otal Assembly M embers](s,t)
× 100%
(1)
If there is an uneven number of seats in the assembly we round 12 [T otal Assembly M embers](s,t)
up to the next integer, ensuring that the running variable equals zero when the
Democrats have a bare majority.7
We test for a discontinuity in pre-determined outcomes—most importantly,
the identity of the incumbent party—at the point where this margin equals
zero. Since this cutoff is arbitrary, in the absence of precise sorting all predetermined outcomes should be continuous in the margin of seats. Incumbency, demographics, or trends in the political leanings of the electorate may
influence how many seats are won by Democrats, but they will not create a discontinuity. Even the actions of political parties—the choice of platform and the
intensity of electioneering—will not create a discontinuity unless it is targeted
with great precision. As argued in [cite LEE 2008], as long as there is enough
7
In states where there is an even number of seats, a value of zero implies neither party has
a majority. Democrats effectively have a veto over redistricting. For example, after the 2000
election left Washington with a perfectly divided house the two parties elected co-speakers and
assigned each committee co-chairs from the two parties.
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JEONG AND SHENOY
Figure 5
Redistricting Cycle
1970: Decennial Census
1971: Plan proposed
1980: Last U.S. House election
under plan passed in 1971
1972: First U.S. House election
under plan passed in 1971
In most states:
State legislature proposes plan
Passes plan as a regular law
1970
1980
Cycle Repeats…
Assembly serves…
Elections to state assembly
uncertainty in the election’s outcome, parties should not be able precisely sort
themselves across the threshold. The presence of a discontinuity implies there
is less uncertainty than commonly believed. It would imply that through careful
electioneering, parties can effectively choose which elections they win.
One may wonder, however, if such sorting is a natural feature of elections
rather than the result of conscious efforts by political parties. For example, incumbent re-election rates may be so high that the incumbent party is almost
guaranteed to retain its power. We can test this hypothesis by exploiting our
second source of variation, the timing of redistricting. In most states, scheduled
Congressional redistricting happens in the year after each decennial census—
that is, in the years ending in 1. The new boundaries drawn in that year are
intended to hold until the next redistricting 10 years later. Figure 5 summarizes
the redistricting cycle.
As the figure suggests, it is the election just before redistricting that determines control of redistricting.8 This accident of timing makes the outcome
8
In many states the election is in years ending in 0, but a few states are irregular. Our definition of redistricting elections adjusts for the irregular cases.
HOW DO ELITES CAPTURE A DEMOCRACY?
13
of these elections especially important to the national Republican and Democratic parties. Even though the local party in each state may care about retaining control of the legislature in any election, the full resources of the national
parties will likely be marshalled only when state elections have consequences
for national elections—that is, in elections that determine which party controls
Congressional redistricting. Finding evidence of sorting in redistricting elections but not other elections would suggest sorting is not inevitable, but the
result of deliberate intervention.
The 50 percent cutoff is only worth sorting around if it triggers a discontinuous change in the redistricting plan. If at all points near the cutoff the two
parties pass a bipartisan plan, there would be no incentive for parties to sort at
the discontinuity. But if plans near the cutoff were passed with bipartisan support, one would expect a similar proportion of Democrats (or Republicans) to
vote for the redistricting bill on either side of the cutoff.
Figure 6 shows that this is not the case. Using data from the 2011 redistricting cycle—the only one for which we have consistent roll call votes—we divide
the running variable into 5 percentage point bins.9 We plot the average fraction
of Democrats and Republicans that vote in favor of the redistricting bill. When
Democrats gain control of the assembly they switch from near universal opposition to near universal support for the redistricting bill. The response of the
Republicans, though slightly less extreme, is stark nevertheless. This reversal
of support suggests that control of the assembly triggers a sharp change in the
type of plan proposed. Moreover, it suggests there is strong party unity—just
below the cutoff, 100 percent of Republicans and 0 percent of Democrats vote
for the bill. Such unity implies winning 50 percent of the seats really does confer
control over whatever redistricting plan ultimately passes the lower house.
At this cutoff, control of the assembly switches from Republicans to Democrats.
But since the redistricting bill is typically passed as regular legislation, it requires approval of not only the lower house but the upper house and the governor. Passing the threshold is best interpreted as giving the Democrats a veto
over redistricting. Figure 7 suggests this veto is important for Democrats. For
different values of the lower house margin for redistricting elections, the figure
9
These data were constructed from Vote Smart (2016), which has roll call votes on 51 bills
from 21 states for the most recent redistricting cycle.
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JEONG AND SHENOY
Figure 6
State Assmbly Members Vote Along Party Lines
0
0
20
40
60
80
Percentage Voting Yes in Assembly
40
20
60
80
100
Republicans
100
Democrats
-30
-20
-10
0
10
Margin of Seats Controlled by Democrats (%)
20
-30
-20
-10
0
10
Margin of Seats Controlled by Democrats (%)
20
plots the fraction of observations for which the Democrats control the upper
house and the governorship. In most states in which the Democrats control the
lower house they do not control the governorship; they control the upper house
in only about half. They have a strong incentive to take control of the lower
house, as it may be their only veto over redistricting. Likewise, the Republicans
have a strong incentive to deny them such a veto.
We estimate the regression discontinuity using a local linear regression with
a rectangular kernel as proposed in Lee and Lemieux (2010). As we discuss in
Appendix A.1.1, choosing an “optimal” bandwidth is complicated. The most
widely-cited methods disagree on the optimum. Instead, we use as our baseline a bandwidth of 18, which lies between the optimum of the different methods, and show that the main results are robust nearly any reasonable choice of
bandwidth.10 The precise equation we estimate depends on the outcome. For
10
This check is in the main text for our main result; we show the robustness of other outcomes
in Appendix A.1.1.
15
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 7
Democrats Often Do Not Control Other
Branches of State Government
1
Democrats Control State Senate (%)
.25
.5
.75
Democratic Control
0
Probability
Republican Control
-20
-10
0
10
Margin of Democrats in State Assembly (%)
20
1
Governor is a Democrat (%)
.25
.5
.75
Democratic Control
0
Probability
Republican Control
-20
-10
0
10
Margin of Democrats in State Assembly (%)
20
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JEONG AND SHENOY
the main result we estimate
[Democratic Control]s,t−1 = γ0 + γ1 [Democratic Seat M argin]s,t
+ γ2 [Democratic Seat M argin]s,t × [Democratic Control]s,t
(2)
+ β[Democratic Control]s,t + [Error]s,t
separately for redistricting and non-redistricting elections. The outcome, an
indicator for whether Democrats won a majority in the previous election, effectively gives whether the Democrats are the incumbent party.
In other specifications we swap this indicator for incumbency with the trend
in Republican Congressional gains leading up to redistricting or the outcomes
of Congressional races either before or after redistricting. For this last outcome
we use race-state-year data. Let [Democratic Seat M argin]Us,t be the number of
seats won by Democrats in the redistricting election of the upcoming redistricting year, and let [Democratic Seat M argin]Ps,t be the margin in the previous redistricting year. Let [Rep. W ins]r,s,t be a dummy for whether a Republican wins
race r in state s in election year t. We estimate equations of the form
[Rep. W ins]r,s,t = τ0 + τ1 [Democratic Seat M argin]U
s,t
U
+ τ2 [Democratic Seat M argin]U
s,t × [Democratic Control]s,t
+ β U [Democratic Control]U
s,t + [Error]r,s,t
(3)
[Rep. W ins]r,s,t = τ0 + τ1 [Democratic Seat
M argin]P
s,t
P
+ τ2 [Democratic Seat M argin]P
s,t × [Democratic Control]s,t
+ β P [Democratic Control]P
s,t + [Error]r,s,t
(4)
where (3) is estimated on elections 7 to 9 years before redistricting or 1 to 5
years before redistricting; (4) is estimated on the election immediately after redistricting or 7 to 9 years after redistricting. The estimates of β U are informative
about whether the parties aim to control redistricting in states where they have
sustained recent gains or losses in the House. It will only differ from zero if the
parties are able to sort around the threshold. The estimates of β P let us trace
out the consequences of their sorting and of the redistricting itself.
HOW DO ELITES CAPTURE A DEMOCRACY?
4
17
Data
Our main results use data compiled by Klarner (2013b) on the number of Democrats,
Republicans, and independents elected to the lower and upper house of the
state legislature. We restrict our attention to elections in or after 1962, which
yields elections leading up to the 1970 redistricting cycle up through 2015. Not
all states allow their Congressional districts to be drawn by the state legislature.
We identify and remove the exceptions from our sample using the comprehensive dataset on redistricting rules compiled by Levitt (2016). The exceptions are
generally independent or appointed commissions.11 We also discard states that
have only a single House representative, as these states have a single district
that consists of the entire state.12 As noted earlier, we restrict our attention to
regularly scheduled redistrictings in the year after the decenniel census.13
To test for sorting on the outcomes of Congressional elections before redistricting we compile a dataset on the vote share and party of each candidate that
ran for each district of the U.S. House from 1970 through 2012. We combine
the data from the Inter-university Consortium for Political and Social Research
(1995), which covers 1970 through 1990, with data from Kollman et al. (2016),
which covers 1991 through 2012.14
We draw on data for campaign finances and career paths for state legislators
from Bonica (2013). We compute the incumbent exit rate of state legislators
using a dataset of state legislative elections compiled from Klarner et al. (2013)
and Klarner (2013a). Finally, we measure racial gerrymandering by combining tract-level census data with Congressional district boundaries. The census
data come from the National Historical Geographic Information System (Minnesota Population Center, 2011). District boundaries for every U.S. Congress
11
Hawaii adopted the commission system since 1982, Washington since 1992, Idaho, New
Jersey, and Arizona since 2002, and California since 2012.
12
Alaska, Delaware, Wyoming, and North Dakota are excluded. Montana is excluded after
1991 reapportionment and South Dakota after 1981 reapportionment.
13
This excludes Hawaii, which would anyways be excluded for most elections because of its
independent commission, and Maine.
14
The ICPSR’s dataset includes the vast majority of House elections but, like any dataset, is
incomplete. However, it also contains several elections not contained in other data, such as that
of Lee et al. (2004). For that reason we choose the ICPSR data over other options. Nevertheless,
these two datasets agree on the vast majority of elections. Using the data of Lee et al. (2004)
for the years 1972 to 1992 (the years it covers) does not change the main results (see Appendix
A.1.3).
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JEONG AND SHENOY
come from Lewis et al. (2013). We assign each tract to whichever district contains its centroid; we do this for the district boundaries both before and after
redistricting to get the old and new district of each tract.
5
Main Results: Is there Capture, and How is it
Done?
5.1
Evidence of Capture
Figure 8 shows the main result. We split the running variable—the margin of
seats won by Democrats in the lower house—into bins with a width of 2 percentage points. Each dot shows the fraction of elections within one bin in which
Democrats are the incumbent party. This fraction can be interpreted as the
probability that the Democrats are the incumbent party. We estimate Equation 2 and plot the predicted values, which appear as lines on either side of the
cutoff (at zero). We report the regression discontinuity estimate (β in Equation
2) and its standard error. The right-hand panel shows the result when these
steps are applied to elections that determine which party will control the lower
house during redistricting. The left-hand panel applies the steps to the other
elections.
In non-redistricting elections (left-hand panel of Figure 8), the relationship
looks as expected. The more seats won by Democrats in the current election,
the more likely they are to be the incumbent party. The positive correlation is
not surprising; states that elect more Democrats in the current election probably elected more in the previous election, making it more likely the Democrats
controlled the lower house. But there is no discontinuity at zero. Democrats
are no more likely to be the incumbent party in elections they just barely win
versus those they just barely loose. This is what one would expect; regardless
of which party holds power going into the election, the uncertainty of the outcome is great enough to deliver narrow defeats as well as narrow victories to the
incumbent party.
By contrast, there is a large discontinuity in redistricting elections (left-hand
panel of Figure 8). The incumbent party is far more likely to enjoy a narrow
19
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 8
There is Sorting by Incumbecy
in Redistricting Elections
Redistricting Election
1
1
Non-Redistricting Election
0.095
(0.080)
Republican Control
.4
.6
.8
0.438
(0.136)
.2
.4
.6
.8
Democrats Are Incumbent Party
Discontinuity
.2
Democrats Are Incumbent Party
Discontinuity
Democratic Control
Democratic Control
0
0
Republican Control
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
Note: The figure depicts our estimates of Equation 2. Standard errors are clustered by state-redistricting cycle.
Bin size is 2 percentage points.
20
JEONG AND SHENOY
Figure 9
The Results are Robust to Choice of Bandwidth
Redistricting Election
1
.5
RD Estimate (90% CI)
-.5
0
.5
0
-.5
RD Estimate (90% CI)
1
1.5
1.5
Non-Redistricting Election
4
10
16
Bandwidth
22
4
10
16
22
Bandwidth
Note: Figure plots the estimate and confidence interval for the discontinuity using every bandwidth h =
{4, 4.5, . . . , 21.5, 22}. Standard errors are clustered by state-redistricting cycle.
victory than a narrow defeat. The discontinuity suggests that in these highstakes elections there is far less uncertainty about the outcome. The incumbent
party is able to sort itself onto the more favorable side of the discontinuity with
remarkable precision.
The result is not driven by the choice of bandwidth. Figure 9 re-estimates
Equation 2 for every bandwidth h = {4, 4.5, . . . , 21.5, 22}. We plot the regression discontinuity estimate and the 90 percent confidence interval against the
bandwidth. The left-hand panel confirms that at any but the widest choice of
bandwidth, there is no discontinuity in non-redistricting elections. By contrast,
there is always a large discontinuity in redistricting elections, though the estimates grow large and noisy when the bandwidth falls below 10.
Table 1 reports the estimates from the baseline specification and several robustness checks. Columns 1 and 2 give the same baseline estimates shown in
Figure 8.15 One possible concern with these estimates is that the presence of
15
All standard errors are clustered by state-redistricting year, the level at which there is variation in the running variable.
21
HOW DO ELITES CAPTURE A DEMOCRACY?
Table 1
Main Results and Robustness
Baseline
RD Estimate
Observations
Clusters
Control Mean
No Ind. Leg.
Drop VRA States
Republican Margin
(1)
Non-Red.
(2)
Red.
(3)
Non-Red.
(4)
Red.
(5)
Non-Red.
(6)
Red.
(7)
Non-Red.
(8)
Red.
0.095
(0.080)
0.438∗∗∗
(0.136)
0.109
(0.084)
0.548∗∗∗
(0.133)
0.072
(0.086)
0.436∗∗∗
(0.145)
-0.106
(0.085)
-0.404∗∗∗
(0.136)
535
178
0.48
137
137
0.35
482
168
0.46
122
122
0.27
468
154
0.48
120
120
0.31
528
176
0.71
135
135
0.88
Note: Outcome is a dummy for whether Democrats are the incumbent party (won the previous election). “Baseline” is the
same specification used to construct Figure 8. “No Ind. Legislators” drops elections in which independent legislators are
elected. “Drop VRA States” drops states that require pre-clearance from the Justice Department for any change in election
law. “Republican Margin” defines the running variable as the Republican rather than Democratic margin.
independent legislators (those unaffiliated with either major party) muddies
the partisan narrative of Section 3. Columns 3 and 4 show that dropping elections in which independents win seats makes little difference in the estimates.
Next we redo our estimates excluding the so-called preclearance states. These
states are required to submit changes to their voting rules for preclearance to
the U.S. Department of Justice (as per Section 5 of the 1965 Voting Rights Act).16
Columns 5 and 6 shows that the coefficient is effectively unchanged. Column
5 and 6 report the RD estimate using the fraction of Republicans rather than
Democrats as the running variable. The Republican seat margin is not precisely
equal to the negative of the Democratic margin because there are a few assembly members unaffiliated with either party. Nevertheless, the coefficient is essentially the negative of that in the baseline specification.
Figure 10 shows that the sorting by incumbency creates a visible discontinuity in the conditional density of the running variable. Each panel shows a
histogram for the seat margin of Democrats in elections that meet the condition
given in the title. Each dot plots the fraction of observations that falls within a 3percentage point bin. Atop these dots we plot the line of best fit. The left-hand
panels show the density for elections in non-redistricting years. Regardless of
which party is the incumbent, there is no large discontinuity in the density. By
16
These are Alabama, Alaska, Arizona, Georgia, Louisiana, Mississippi, South Carolina, Texas,
and Virginia. As a result of Shelby County v. Holder, 133 S. Ct. 2612 (2013), this requirement was
lifted in 2013.
22
JEONG AND SHENOY
Figure 10
Conditional on the Incumbent Party, there is
a Discontinuity in the Probability Density
Republicans Incumbent [Redistricting Election]
Fraction of Observations
0
.05 .1 .15 .2
Fraction of Observations
0
.05 .1 .15 .2
Republicans Incumbent [Non-Redistricting Election]
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
-20
20
Democrats Incumbent [Redistricting Election]
Fraction of Observations
0
.05 .1 .15 .2
Fraction of Observations
0
.05 .1 .15 .2
Democrats Incumbent [Non-Redistricting Election]
-10
0
10
Margin of Seats Won by Democrats (%)
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
Note: Each panel shows a histogram for the seat margin of Democrats in elections that meet the condition given in the title. The left-hand
panels show the probability mass in each bin for observations in redistricting elections, while the left-hand panels show non-redistricting
elections. The top panels show elections in which Republicans compete as incumbents, while the bottom panels show elections in which
Demcorats are the incumbents.
23
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 11
The Autocorrelation in the Identity of the Ruling Party
Rises in Redistricting Elections
Non-Redistricting Election
Redistricting Election
0
.2
.4
.6
Autocorrelation: Democratic Control
.8
Note: We estimate the autocorrelation of the indicator for Democratic control of
the lower house separately for redistricting and non-redistricting elections. The
solid dot marks the point estimate; the hollow dots mark the 90 percent confidence interval. The estimates are restricted to observations within 10 percentage
points of the discontinuity.
contrast, the right-hand panels show that there are large discontinuities in redistricting elections. When Republicans are the incumbent party, there is far
more mass just to the left of the threshold—that is, far more elections are barely
won than barely lost by Republicans. The converse is true when Democrats are
the incumbent party; there is far more mass just to the right of the threshold
Figure 11 shows the aggregate consequence of this sorting. For both redistricting and non-redistricting elections we estimate the autocorrelation of the
indicator for whether Democrats control the legislature. To be precise we estimate
[Democratic Control](s,t) = ρ0 + ρ1 [Democratic Control](s,t) + ζ(i,s)
for t ∈ {Red.} or t ∈ {N on − Red.}
and plot ρ̂1 with its 90 percent confidence intervals. The autocorrelation rises
from less than 0.4 to over 0.6 during redistricting elections. Thanks to sorting, a
redistricting election is far more likley to return the ruling party to power.
24
5.2
JEONG AND SHENOY
The Method of Capture
The results of Section 5.1 suggest that through concerted effort, incumbent
parties can nearly eliminate the natural uncertainty of the election’s outcome.
Since outright fraud is unlikely, these efforts must take the form of campaign
strategy. Any such strategy would likely center on two of the key features of
American politics: a high rate of incumbent reelection, and the key role of campaign contributions.
In lower house elections from 1968 to 2012, incumbents won 93 percent
of the elections they contested (compared to 26 percent for non-incumbents).
But near-guaranteed reelection for individual incumbents only implies victory
for the incumbent party if all of its incumbents seek reelection. We find that
roughly 22 percent of lower house incumbents do not seek re-election. In part
that is because many politicians see the lower house of the state assembly as a
stepping stone to higher office. Among lower house members who won office
in 2002, roughly 15 percent sought higher office over the next 10 years. Nearly
80 percent of them ran for the upper house of the state legislature, and over 10
percent ran for the U.S. House. But while these ambitions of higher office make
incumbent exit more likely, they also give the state and national political parties leverage over their incumbents. If running for higher office is easier with the
support of the party, the party might be able to pressure incumbents to remain
in the lower house in the crucial redistricting elections.
The top panel of Figure 12 shows that the evidence is consistent with this hypothesis. The figure plots the running mean of the exit rate of incumbent legislators as a function of the Democrats’ margin of victory in the previous election.
This margin essentially gives the number of Democratic incumbents. As the
margin narrows it becomes more important for the incumbent party to ensure
each incumbent candidate is reelected. The figure shows that, as expected, the
rate of incumbent exit is lowest in elections where the margin of incumbency
is narrow. This is especially true in redistricting elections. The bottom panel of
Figure 12 shows a similar pattern for campaign finances. In redistricting elections there is a spike in the total contributions to lower house members in states
where the margin of incumbency is slight. There is no similar spike in nonredistricting elections. The spike is especially pronounced among Republicans.
25
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 12
The Exit Rate Falls and Campaign Financing
Rises in Redistricting Elections
Incumbent Exit Rate
.35
Democrats
.35
Republicans
Redistricting Year
.2
.15
.15
.2
.25
Incumbent Exit Rate
.25
.3
.3
Non-Redistricting Year
-20
-10
0
10
Margin of Seats Won by Democrats, Previous Election (%)
20
-20
-10
0
10
Margin of Seats Won by Democrats, Previous Election (%)
20
Total Campaign Receipts: Candidates for State Lower House
10000000
Democrats
Redistricting Year Year
Non-Redistricting
0
0
2000000
4000000
6000000
8000000
Total Receipts to Lower House State Legislators
8000000
2000000
4000000
6000000
10000000
Republicans
-10
-5
0
5
Margin of Seats Won by Democrats, Previous Election (%)
10
-10
-5
0
5
Margin of Seats Won by Democrats, Previous Election (%)
10
Note: We plot the running average of the incumbent exit rate and total campaign receipts for state lower house members
against the margin of seats won by Democrats in the previous election. This margin effectively gives the margin of
incumbency.
26
JEONG AND SHENOY
In states where they enter the redistricting election with a bare majority of incumbents, their receipts among all candidates in the state spikes at roughly 10
million (in 1983 dollars). In non-redistricting elections their receipts are only
3.5 million dollars.
To assess whether such a campaign strategy can create a discontinuous advantage for the incumbent we run simple simulations (the details of which are
in Appendix B). In these simulations the two parties can choose to make strategic intervention by pressure incumbents to see re-election and channeling campaign funds to incumbents in close elections. The top panel of Figure 13 shows
the pattern of the incumbent exit rate and campaign finances (normalized by
their standard deviation) when they choose to intervene. The bottom panel,
which is constructed analogously to Figure 8, shows that such intervention can
generate a discontinuity. But even with a relatively generous intervention—the
incumbent exit rate, for example, falls by three times as much as in the data—
the discontinuity is smaller than produced in the data. This suggests there are
other mechanisms—for example, targetting specific candidates as well as specific states—that the simulation cannot capture. Nevertheless it implies that
electioneering can reduce election uncertainty enough to create a discontinuity.
6
What is the Objective of Capture?
Section 5 focused on what might be called the supply side of democratic capture. The evidence suggests it is less costly for the incumbent party to win the
lower house. It may achieve this by pressuring incumbents to remain in their
seats, and by channeling funds to states where the margin of incumbency is
narrow. This intervention allows the parties to sort themselves onto either side
of the discontinuity.
We now turn to the demand side of capture. In which battlegrounds do parties work to retain control of redistricting? And when they succeed, what do
they do with their power? We answer the first question we test for sorting on
the outcomes of Congressional elections prior to redistricting. We provide suggestive evidence to answer the latter by testing how election outcomes and the
27
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 13
In Simulations a Strategic Intervention by Parties
Creates a Discontinuity
Simulated Intervention
Campaign Finance (Simulated)
4
1
2
Change in Receipts
3
.2
.15
.1
0
.05
Change in Incumbent Exit Rate
.25
Incumbent Exit Rate (Simulated)
-20
-10
0
10
20
-20
-10
Margin of Seats Won by Democrats, Previous Election (%)
0
10
20
Margin of Seats Won by Democrats, Previous Election (%)
Effect of Intervention on Simulated Elections
Strategic Intervention
1
1
No Strategic Intervention
0.142
(0.026)
.2
.4
.6
.8
Discontinuity
0
.2
.4
.6
.8
Democrats Are Incumbent Party
-0.030
(0.030)
0
Democrats Are Incumbent Party
Discontinuity
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
-20
-10
0
10
Margin of Seats Won by Democrats (%)
20
Note: We simulate 10000 state-level elections of 100 lower house legislators. In half there is strategic intervention,
meaning parties channel funds and pressure incumbents to see re-election in close elections. In the other half there is
no such intervention. We test for a discontinuity exactly as in Figure 8. See Appendix B for details.
28
JEONG AND SHENOY
Figure 14
There is Negative Sorting on the Pre-Existing Trend;
It is Partially Reversed by Redistricting
Effect: Change From Before to After Redistricting
-10
0
Discontinuity
Democratic Control
0
10
Margin of Seats Won by Democrats (%)
20
-1.205
(0.531)
Republican Control
-.8
Republican Control
-20
.4
.8
0.540
(0.233)
-.4
Change in Republican Seats
from before to after Redistricting
.4
0
-.4
Discontinuity
-.8
Average Change in Republican Seats
(between Elections)
.8
Pre-trend: Change Leading up to Redistricting
-20
-10
Democratic Control
0
10
20
Margin of Seats Won by Democrats (%)
Note: Left: The outcome is the average change in Republican wins (seats) between the 5 regular Congressional
elections leading up to redistricting. Right: The outcome is the change in Republican wins between the Congressional election just before redistricting to the election just after. All standard errors are clustered by stateredistricting cycle. Bin size is 4 percentage points.
allocation of voters across districts changes from before to after redistricting.
The left-hand panel of Figure 14 suggests that the parties sort across the
threshold to ensure they retain control in states where they have lost strength.
We take the change in Republican victories in between each of the five U.S.
House elections preceding redistricting (there are five elections within a tenyear redistricting cycle). We average this change within each redistricting cycle,
then plot the average against the margin of seats won by Democrats in the state
legislature in the election that determines control of the next redistricting cycle.
In states just barely won by the Democrats, Republicans have increased their
House wins by half a seat per election (or 2.5 seats over the entire 5-election cycle) relative to states just barely won by Republicans. The result suggests each
party is working to ensure it wins in states where it has sustained recent losses.
The right-hand panel of Figure 14 suggests the parties take these losing states
and at least temporarily reverse some of their losses. We plot the change in Republican seats from before to after redistricting against the Democratic margin in the legislature during redistricting. It is the reverse of the pattern in
the left-hand panel. In the same states where Republicans suffered sustained
losses leading up to redistricting, they now enjoy an immediate gain. Crossing
HOW DO ELITES CAPTURE A DEMOCRACY?
29
the threshold from Republican to Democratic control costs the Republicans 1.2
seats. Given that there are pre-existing trends, one must be cautious in interpreting this effect. One can never prove it is caused by partisan control over redistricting. But given that the pre-existing trends are working against the party
that controls the legislature, it is hard to find a plausible alternative explanation
for the sudden reversal of fortune.
To make clear the timing of the effects and reduce the variance by increasing
the number of observations, we next estimate the race-level specifications (3)
and (4). The top-left panel of Figure 15 shows that there is no sorting on outcomes in the U.S. House 4 to 5 elections before redistricting. But the top-right
panel shows that the parties sort themselves to control redistricting in states
where they have lost House races in the 1 to 3 elections before redistricting. Republicans win 10 percentage points fewer races in the states where they barely
won the state legislature in redistricting elections.
But the bottom-left panel of Figure 15 shows that these losses are reversed in
the House election immediately after redistricting. Since the running variable
is effectively consistent across the figures, the races within each bin are always
from the same state-redistricting years. To the left of the threshold, averages
within these bins shift upward (towards a high fraction of Republican wins);
the opposite happens to the right of the threhsold. This reversal suggests the
parties may use redistricting to reverse their losses. However, the bottom-right
panel suggests the trends are not forestalled permanently. By the fourth and
fifth elections after redistricting, the gaps have re-opened.
The trend reversal shown in Figures 14 and 15 suggests the efforts taken by
parties to control redistricting is at least temporarily rewarded. But without direct measures of how the parties have redrawn districts, one may still doubt
that the reversal of trends is caused by redistricting. We construct direct measures from geocoded census data. We test for whether certain demographic
groups are more likely to be moved under Republican versus Democratic redistricting. As before, these results come with the caveat that, since there is sorting
around the discontinuity, it is impossible to be certain any effects we see are not
caused by selection bias. However, we provide evidence that the demographic
characteristics of states on either side of the discontinuity are similar, making it
unlikely a non-partisan redistricting plan would differ across the discontinuity.
30
JEONG AND SHENOY
Figure 15
-0.019
Discontinuity (0.042)
Republican Control
-20
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
[Upcoming Redistricting]
20
Election Immediately After Redistricting
Discontinuity
-0.004
(0.046)
Republican Control
-20
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
[Previous Redistricting]
20
Probability Republican Wins
0 .2 .4 .6 .8 1
4-5 Elections Before Redistricting
Probability Republican Wins
0 .2 .4 .6 .8 1
Probability Republican Wins
0 .2 .4 .6 .8 1
Probability Republican Wins
0 .2 .4 .6 .8 1
Race-Level Estimates Confirm Sorting on Trends,
which are Temporarily Reversed by Redistricting
1-3 Elections Before Redistricting
0.098
Discontinuity (0.044)
Republican Control
-20
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
[Upcoming Redistricting]
20
4-5 Elections After Redistricting
Discontinuity
0.118
(0.057)
Republican Control
-20
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
[Previous Redistricting]
20
HOW DO ELITES CAPTURE A DEMOCRACY?
31
Recall from Figure 2 that a party aiming to gerrymander would want to move
its opponents—those very likely to vote for the other party—to minimize their
influence. The demographic group whose party preference is most easily identified is arguably African Americans. In the 2014 election, 89 percent of African
Americans voted for a Democrat running for Congress—support comparable to
that of registered Democrats (92 percent).17 Since an African American is likely
to support Democrats, Republicans may try to move African Americans to minimize their influence.18
We can say a voter has been “moved” if her new Congressional district contains many voters that were not in her old Congressional district. To be precise,
for each census tract we define the fraction of the population in the new Congressional district that is “unfamiliar,” meaning the fraction not in the original
district. The benefit of this measure is that it by definition measures intent;
census tracts will only have high values on the measure if district boundaries
are changed. If existing districts are kept largely intact the measure will on average be low. If a district is dissolved and its constituent tracts are distributed
between adjoining districts, all of the tracts will have high value in this measure.
We test for a discontinuity in the measure using tract-level census data. Figure 16 shows how the average unfamiliar population changes at the discontinuity. The left-hand panel restricts the sample to tracts in which African Americans are a majority; the righthand panel shows all other tracts. When Republicans control redistricting, the new boundaries put majority black census tracts
into districts with 9 percentage points more unfamiliar people. This holds only
for African American tracts; other tracts show no discontinuity.
Are they being moved to districts that minimize their influence? We restrict
the sample to African American tracts put in districts in which more than half of
the population is unfamiliar, taking this to mean the tract has been “moved.” In
the lefthand panel of Figure 17 we test for whether, conditional on being moved,
majority black tracts are drawn into districts in which African Americans form
an overwhelming (more then 75 percent) majority. Recall from Figure 2 that
“packing” African Americans into a small number of districts minimizes the
17
According to CNN (2016), whose data are based on National Election Pool exit polls.
The ideal test would be to look at actual registered Democrats and registered Republicans.
But we do not have historical data on the number of registered Republicans and Democrats by
precinct or census tract.
18
32
JEONG AND SHENOY
Figure 16
African Americans are More Likely
to Be Moved to Unfamiliar Districts
Non-Black Census Tracts
-12
-6
0
6
Margin of Seats Won by Democrats (%)
.4
Discontinuity -0.018
(0.040)
Republican Control Democratic Control
.2
Republican Control Democratic Control
-18
.3
Fraction of New District that is Unfamiliar
.4
.3
Discontinuity -0.093
(0.046)
.2
Fraction of New District that is Unfamiliar
.5
.5
Black Census Tracts
12
18
-18
-12
-6
0
6
Margin of Seats Won by Democrats (%)
12
18
Note: Outcome: For each census tract we define the fraction of the population in the new Congressional district that is
“unfamiliar,” meaning the fraction not in the original district. We define a “black census tract” as one with a population
at least 50 percent African American. Standard errors are clustered by state-redistricting cycle.
number of elections in which their votes are pivotal. When Republicans barely
control redistricting, an African American census tract that is moved has nearly
a 35 percent probability of being moved into an overwhelmingly black district.
When Republicans lose the lower house this probability drops to zero.19
The righthand panel of Figure 17 shows the effect of Republican control on
the density of the African American population share in the new district. To
make the figure directly comparable to the prediction in Figure 2 we adjust the
estimates to show the effect of Republican control. As predicted, there is an
increase in the density of overwhelmingly black districts and districts in which
blacks are barely outnumbered. Meanwhile, the density of districts in which
African Americans are a slight majority decreases. In summary, the results suggest African Americans are moved into districts that minimize the number of
elections they sway.
But as noted earlier, the states on either side of the threshold are not necessarily comparable. At the very least they differ in the identity of the incum19
The regressions in Figure 17 narrow the bandwidth to 10 because there is essentially no
“packing” further away from the discontinuity.
33
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 17
Conditional on Being Moved,
Where Are African Americans Moved?
.5
.25
0
.15
.1
.05
0
-.25
Estimated Shift in Density (90% CI)
-0.230
(0.054)
-.5
.25
.3
.35
.75
Density of Black Population Share (New District)
.2
Discontinuity
-.05
Republican Control Democratic Control
-.75
Probability of Being Moved to a District > 75% Black
Moved to Overwhelmingly Black District?
-10
-5
0
Margin of Seats Won by Democrats (%)
5
10
0
.2
.4
.6
.8
People living in districts with ____ fraction black
Note: We restrict the sample to tracts in which more than half of the population is unfamiliar (see note of Figure 16).
Righthand panel: Coefficients give the estimated shift in the density of the districts to which African Americans are
moved by redistricting. We rescale the coefficients to show the effect when Democrats lose control of the assembly.
Standard errors are clustered by state-redistricting cycle.
bent party and in the pre-trends of election outcomes for the U.S. House. Is it
possible that they also differ in their demographics? For example, if the old
districts in states barely controlled by Republicans on average contain more
African Americans, it may be almost mechanical that they would be more likely
to be moved during redistricting. The left-hand panel of Figure 18 suggests this
is not so. We test for whether at the threshold there is a discontinuity in the fraction of a district’s population that is African American. We use the old district
boundaries to avoid contaminating the estimates with the effect of redistricting.
There is no evidence of a discontinuity.
Though African Americans may comprise a similar portion of the total population near the threshold, is it possible that they are distributed less evenly
than the rest of the population? For example, if migration patterns differ across
the threshold, it is possible that in Republican-controlled states African Americans have segregated themselves into heavily over- or under-populated districts. These districts would have to be broken up during redistricting. We test
this hypothesis by calculating for each census tract the absolute deviation from
the mean in the population of its pre-redistricting district. If African Americans
1
34
JEONG AND SHENOY
Figure 18
No Evidence that African Americans Need
to be Moved More on One Side of the Threshold
Blacks Are No More Likely
to Live in Large or Small Districts
.1
Democratic Control
Republican Control
Democratic Control
0
0
Republican Control
Discontinuity 0.012
(0.018)
.05
Deviation in District Population
.1
Discontinuity 0.006
(0.018)
.05
Fraction of District Population
.15
.15
Fraction of Population Black
is Similar at Threshold
-20
-10
0
Margin of Seats Won by Democrats (%)
[Year District Was Drawn]
10
20
-20
-10
0
Margin of Seats Won by Democrats (%)
[Year District Was Drawn]
10
20
Note: Standard errors are clustered by state-redistricting cycle.
are concentrated in malapportioned districts before redistricting in Republican
states, majority-black census tracts should have a higher absolute deviation to
the left of the threshold. The right-hand panel of Figure 18, which tests for such
a deviation in African American census tracts, suggests no such deviation exists. There is no clear non-partisan reason why African Americans must more
often be redistricted in states controlled by Republicans. Moreover, it is hard to
see a non-partisan reason why, as shown in Figure 17, they must be moved into
racially segregated districts.
7
Conclusion
Even in a democracy as mature as the United States, political parties can exert
remarkably precise control over the outcomes of elections. They need not resort to fraud, but rather can use modern campaign tactics to great effect. Our
claim is not that incumbent parties can guarantee the outcome of an election,
but that they can ensure a disproportionate share yield the desired outcome.
When such an election determines control of an institution that itself determines whether elections are competitive, voters may be left with even less sway
HOW DO ELITES CAPTURE A DEMOCRACY?
35
over elections. Whether these patterns may be reversed by policy—term limits
or caps on campaign finance—is a question we leave to future research.
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A
Empirical Appendix
This appendix shows additional figures and tables referenced in the main text.
1.1
Verifying the Results are Not Driven by Choice of
Bandwidth
1.2
Verifying the Results are Not Mechanical because of Small
Sample Size
This appendix verifies that the main result—that there is a discontinuity in incumbency in redistricting elections—is not driven by small sample size. One
may worry that it is mechanically more likely that we would find a larger discontinuity in these elections because there are fewer of them. To test this hypothesis we test for whether we can produce an equally large discontinuity by
discarding some of the non-redistricting elections.
To be precise, we start with the dataset of all non-redistricting elections. We
randomly select a subsample of these elections of the same size as the set of
redistricting elections. We then estimate the discontinuity. We repeat this procedure 2000 times. Figure 24 plots the histogram of the 2000 estimates. The red
line marks the actual estimate from the redistricting elections. Only a negligible fraction of the 2000 estimates is larger than the actual estimate, suggesting
it is very unlikely our result is driven by sample size. It is unlikely that the redistricting elections are drawn from the same data-generating process as the
non-redistricting elections.
38
JEONG AND SHENOY
Figure 19
Pre-Trend
-1.5 -1 -.5 0 .5 1 1.5
RD Estimate (90% CI)
Robustness to Bandwidth: Figure 14
8
11.5
15
18.5
22
18.5
22
Before to After
-2.5-2-1.5-1 -.5 0 .5 1
RD Estimate (90% CI)
Bandwidth
8
11.5
15
Bandwidth
39
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 20
Robustness to Bandwidth: Figure 16
RD Estimate (90% CI)
-.4 -.3 -.2 -.1 0 .1
Black Census Tracts
4
10
16
22
Bandwidth
RD Estimate (90% CI)
-.4 -.3 -.2 -.1 0 .1
Non-Black Census Tracts
4
10
16
Bandwidth
22
40
JEONG AND SHENOY
Figure 21
-.5
-.4
RD Estimate (90% CI)
-.3
-.2
-.1
0
Robustness to Bandwidth: Left-hand Panel, Figure 17
4
6
8
Bandwidth
10
12
41
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 22
Estimated Shift in Density (90% CI)
-.75 -.5 -.25 0 .25 .5 .75
Estimated Shift in Density (90% CI)
-.75 -.5 -.25 0 .25 .5 .75
Estimated Shift in Density (90% CI)
-.75 -.5 -.25 0 .25 .5 .75
Robustness to Bandwidth: Right-hand Panel, Figure 17
Density of Black Population Share [Bw=10]
0
.2
.4
.6
.8
People living in districts with ____ fraction black
1
Density of Black Population Share [Bw=8]
0
.2
.4
.6
.8
People living in districts with ____ fraction black
1
Density of Black Population Share [Bw=6]
0
.2
.4
.6
.8
People living in districts with ____ fraction black
1
42
JEONG AND SHENOY
Figure 23
Robustness to Bandwidth: Figure 18
RD Estimate (90% CI)
-.15 -.075 0 .075 .15
Fraction of Population Black is Similar at Threshold
4
10
16
22
Bandwidth
RD Estimate (90% CI)
-.15 -.075 0 .075 .15
Blacks are No More Likely to Live in Large or Small Districts
4
10
16
Bandwidth
22
43
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 24
3
Subsamples of the Non-Redistricting Election Rarely
Produce Discontinuities as Large as in the Redistricting Elections
0
1
Density
2
Redistricting
Year Estimate
-.4
-.2
0
.2
Estimate
Note: See text for details.
.4
.6
44
1.3
JEONG AND SHENOY
Verifying the Results with Different Data
As noted in Section 4, the ICPSR Constituency data we use for our analysis is
not a complete dataset of all elections. To validate that these missing elections do not bias the results we merge our data with that of Lee et al. (2004) for
the years 1972 to 1992 (the years for which the two datasets overlap). The two
datasets have in common 4,544 elections. Their data contain 114 elections not
contained in ours, whereas our dataset contains 186 elections not contained in
theirs. Among the elections contained in both the two datasets agree on the
outcome of 99.45 percent of elections.
To verify that the results are not driven by these minor discrepencies, we
redo our analysis by replacing our data with that of Lee et al. (2004) for the years
that they overlap. In the main text we restrict our attention to elections in which
there is only one Democrat and one Republican. Since the data of Lee et al.
(2004) do not identify these elections we use all elections in this analysis. (The
main results using our dataset are unchanged if we use all elections.) Figure 25
shows that the main results are unchanged when we use the alternative dataset.
B
Simulation Appendix
[In Progress]
45
HOW DO ELITES CAPTURE A DEMOCRACY?
Figure 25
The Main Results Hold with the Alternative Dataset
Discontinuity
0.001
(0.040)
Republican Control
-20
1-3 Elections Before Redistricting
Probability Republican Wins
0 .2 .4 .6 .8 1
Probability Republican Wins
0 .2 .4 .6 .8 1
4-5 Elections Before Redistricting
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
20
Discontinuity
Republican Control
-20
Republican Control
-20
0.005
(0.045)
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
20
4-5 Elections After Redistricting
Probability Republican Wins
0 .2 .4 .6 .8 1
Probability Republican Wins
0 .2 .4 .6 .8 1
Election Immediately After Redistricting
Discontinuity
0.104
(0.045)
20
Discontinuity
Republican Control
-20
0.107
(0.055)
Democratic Control
-10
0
10
Margin of Seats Won by Democrats (%)
20