The electoral risk of redistricting: Evidence from the United States

Electoral Studies (1992), 11:2, 122-137

The Electoral Risk of Redistricting: Evidence from the United States

CLYDE BROWN*

Department of Political Science, Miami University, Oxford, Ohio 45056, USA

This article investigates whether redistricting poses an electoral risk to the reelection of incumbent US representatives. Incumbency is conceptual- ized as a ‘treatment’ that representatives can apply to congressional districts through traditional means of district attentiveness and representativeness. Redistricting is conceived as a naturally occurring phenomena, that is, a quasi-experiment. It is used to measure the risk of redistricting by comparing the electoral support received by incumbents in their ‘old’ territory, which has been previously exposed to the treatment of incumbency, to the level of support they receive in various kinds of ‘new’ territory added by redistricting, which has not yet been exposed to the effects of their incumbency. Analysis of county election returns for six American states from 1972 to 1984 indicate that incumbents initially receive marginally less support in the ‘new’ territory when compared with the ‘old’ but that incumbents are able to quickly eliminate the difference between the two kinds of territory. The analysis concludes that redis tricting does not pose a serious threat to incumbent re-election.

The politics of reapportionment has come to the forefront again as the United States conducts its 1990 decennial census. Cities and states are challenging the enumer- ation techniques and preliminary reports of the Bureau of the Census to ensure that millions of their citizens, mostly the urban poor, will be included in the count and that the jurisdictions will not lose representation in Congress and funding for programmes which use population as part of the funding formula. The US Supreme Court has issued recent rulings in Davis v. Bandemer and Badbam v. Eu which upheld highly partisan redistricting plans in Indiana and California. Preliminary census population counts have been announced which indicate states that will likely lose or gain congressional seats after the 1990 reapportionment and politicians are jockeying to preserve their districts. The two major parties put in place organizational efforts for the 1990 elections to win state legislature and/or governorships to protect themselves in the redistricting process which will define legislative districts for the next decade. Political consultants offer services such as

* I would like to thank two anonymous reviewers for their comments. Herb Waltzer read two drafts of the manuscript and made many helpful suggestions. In addition, discussions with Susan Kay and Doug Shumavon at a critical juncture helped resolve a major conceptual difficulty. All errors, of course, remain my responsibility.

0261-3794/92/02/0122-16/$03.00 0 1992 Huttetworth-Heinemann

CLYDE BROWN 123

redistricting software and ‘insiders’ offer advice to incumbents on ‘how to protect their turf (Beiler, 1989; Peck, 1989; and Lewin, 1989).

This article investigates a central and practical question: What is the electoral threat of redistricting to the reelection of American congressional Incumbents? Is all the political speculation, interest and effort (fear and glee), surrounding the 1990 Census justified? How does redistricting impact incumbents: does it put them at risk, contribute to their advantage or have little Impact?

Finding answers to these questions using a new data source is the purpose of this article. Redistricting is used as a quasi-experiment with which to measure the electoral risk it poses to incumbency. The level of support for representatives seeking re-election in their ‘old’ territory, which has been previously exposed to the treatment of incumbency, is compared with the level of support in various kinds of ‘new’ territory added by redistricting, which has not yet been exposed to the effects of this treatment. If incumbents receive substantively less support in the ‘new’ territory, we would have evidence that redistricting constitutes a threat to reelection. If there is no difference in electoral performance in the two kinds of territories, we would have evidence that redistricting poses little risk to representatives seeking reelection. Of course, the ‘newness’ of territory in a district is only one trait of that territory; other relevant characteristics might include the area’s partisanship, previous representational history, and socioeconomic composition. For Instance, we would expect the partisanship of an area to be very important, everything else held constant. In the analysis that follows, regression models with and without the above variables as controls are estimated to test for the electoral effects of redistricting using county level congressional election returns for six American states for the 1972-84 time period.

Redistricting

The effects of congressional redistricting on the electoral fortunes of candidates for the US House of Representatives has been a popular research topic among political scientists for more than two decades. Following the decennial census of Ameri- can citizens, the Census Bureau allocates legislative seats to the states subject to congressional approval. Following reapportionment, states that gain or lose seats in the House must redraw district boundary lines for congressional districts; many states also redraw legislative districts for the state legislature at the same time. Redis tricting is typically done by the state legislature or a board composed of state elected officials, but the courts have been increasingly involved because of legal challenges to specific redistricting plans.

Historically, redistricting has been a partisan process which has raised issues of gerrymandering, that is, ‘the deliberate and arbitrary distortion of district boundaries and populations for partisan or personal political purposes’ (Kirkpatrick u. Preisler, 394 U.S. 526 (1969)). The redistricting process is significant because of its impact on the distribution of political power and partisan advantage, on the career ambitions of politicians, and on political representation for groups in society. Since Wesbeny u. Sunders, 376 U.S. 1 (1964), the US Supreme Court has required that congressional districts be substantially equal in population. In Kirkpatrick u. Preisler, the court rejected the notion that small population variances be considered de minimis and reiterated recently in Karcber u. Daggett, 426 U.S. 1975 (1983), that states have to rationally justify any deviations from ‘one-man, one-vote’.

124 The Electoral Risk of Redistricting

However, the requirement of population equality among districts has not proved to be a significant constraint on the ability of state legislatures to engage in poh’ti- cal gerrymandering when they are so inclined. In Davis v. Bandemer, 106 S. Ct. 2797 (1986) where Democrats unsuccessfully challenged the constitutionality of Indiana’s 1981 state redistricting plan, the Court ruled that intentional partisan gerrymandering ‘does not constitute an equal protection violation, instead the effect must be evident at the polls’ for a series of elections before a plan would be held unconstitutional.

After the 1990 census, approximately 20 House seats are expected to shift between states. Internal shifts of population within states between urban, suburban and rural areas will require additional redistricting. In the 1990 election, the political parties targeted state legislatures and gubernational races with the hope of controlling the redistricting process in specific states. Clearly, the political struggle surrounding this issue will continue until the redistricting process is complete.

Research on Incumbency and Redistricting

It has long been known that incumbents are extremely successful in getting re- elected and that few congressional districts are ‘marginal’, that is, incumbents win handily (Mayhew, 1974). The literature is so extensive that long ago Charles 0. Jones (1981) suggested that one more article on incumbency advantage would produce ‘a spontaneous primal scream’ in the profession.

Redistricting as it relates to legislative electoral competition is of special interest because it changes the make-up of the district and poses a potential risk to an incumbent’s re-election. Bullock (1975) has written:

changes in the boundaries of a legislator’s district have potentially great significance. The advantage of incumbency-the accrual of political debts and name recognition-are, with some exceptions, limited to the confines of a member’s district. If new areas are added to a district, the legislator may begin a reelection campaign with much less advantage over his challenger. When redistricting removes some areas from a district, the advantage of incumbency may be reduced to the extent that portions housing a legislator’s strongest supporters are removed.

Despite the potential threat redistricting is to incumbents (and all the concomi- tant activity by political parties, consultants, candidates, etc. surrounding the reapportionment process), most research has found only a limited impact for redistricting. Bullock (1975) found no statistically significant difference in the re-election rates of redistricted versus non-redistricted incumbents between 1962-72. Cover (1977) found no difference between redistricted and non-redistricted incumbents in beating inter-election partisan vote swings. Ferejohn (1977) reached the same conclusion regarding the growth of incumbency advantage. Glazer, Grofman and Robbins (1987) determined that partisan change in congressional districts following the 1972 redistricting had minimal impact on the re-election of incumbents. Gopian and West’s (1984) analysis concluded that almost equal numbers of incumbents were advantaged and disadvantaged by the 1982 redistricting. These studies are all based on the analysis of aggregate electoral returns for congressional districts. As such, it is not possible to infer from them where vote shifts come from-the ‘old’ territory the incumbent previously represented or ‘new’ territory added by redistricting?

CLYDE BROWN 125

In an interesting study, Born (1985) conducted a series of analyses investigating the impact of reapportionment from 1952 to 1982 in I6 states and concludes that partisan attempts at gerrymandering have failed because voters seem to be respond- ing to incumbency cues. Part of his analysis shifts to the county level to determine the impact of added territory on vote outcomes; the issue is whether it matters that the territory appended to an incumbent’s district came from an incumbent of the same party, an open seat or the opposition party. He documents that it does matter with incumbents benefiting when they inherit counties previously represented by a representative of the same party. Additionally, incumbents have improved their ability to pick up these votes when the first elections following each redistricting are compared. This analysis is a significant improvement over earlier work (this paper uses a similar approach) in that it disaggregates election results from the district level to the county level. However, it has two shortcomings: (1) by only analysing territory added to the district a comparison between ‘old’ and ‘new’ territory was not made, and (2) by only looking at the first election following reapportionment it is not possible to analyse electoral change over the entire ten-year apportionment period in terms of the kinds of territory which make-up the district.

Part of the puzzle of the failure to find an effect for redistricting at the district level can be eliminated by understanding various strategies used in redistricting. A partisan gerrymander has the majority party creating the maximum number of marginally safe seats for itself and creating the minimum number of overwhelm- ingly safe seats for the minority opposition (Erickson, 1972: 1237; and Cain, 1985: 321). In this case the majority party is trading off electoral security for some of its candidates (including some incumbents) so as to maximize its opportunities to win marginal seats. In such a situation it is possible to mistakenly conclude that the minority party is better off because its party’s incumbents are receiving greater average district-wide support (Cam, 1985). A bipartisan gerrymander which has as its purpose the protection of incumbents regardless of party would not be subject to this possible misinterpretation. Incumbents of both parties would be gaining electoral security.

Several methods of measuring the effects of redistricting have been tried; a partial list of attempts to account for shifts in territory would include: (1) recomputing voter registration figures, (2) recomputing vote totals based on a party’s previous congressional candidates, (3) recomputing measures of partisanship on the basis of a previous statewide candidate’s performance, frequently a recent presidential candidate, and (4) computing an expected vote for the new district based on demographic and political variables. (See Cain, 1985: 322-3 for an excellent discussion of these techniques.) Born (1985: 309) points out the difficulty of using registration data (often non-existent) and recomputed presidential or congressional vote totals (comparable only when counties remain intact between reapportionment plans) when conducting longitudinal research on redistricting. He also questions ‘the legitimacy of using retabulations based on statewide candidates to make inferences about redistricting changes intended at the House level of competition’ given the different sources of constituency support for different candidates.

Research Design

The question is what to make of this situation? Perhaps, simplicity can be a virtue. The suggestion is that the risk of redistricting to incumbency be measured not by


comparisons between elections but within one election, not by utilizing recomputed measures but by using the actual vote split for candidates, and not by focusing on the entire district but instead by focusing on the composite parts of the district. A measure of the risk of redistricting to incumbency can be obtained by simply comparing how an incumbent runs in his ‘old’ territory with how he runs in his ‘new’ territory: what percent of the vote did incumbents receive in the two kinds of territory that make up a reconstituted district?

For each redistricted incumbent who receives additional territory to his congressional district, a portion of the district has been previously exposed to the treatment of incumbency and another portion has not received the treatment. An analogy would be to compare the yield (% of vote) on a fertilized field (‘old’ territory exposed to the treatment of incumbency) with that of an unfertilized field (‘new’ territory). Redistricting becomes a naturally recurring phenomenon which permits a quasi-experimental research design with which the perils of redistricting can be estimated. It delineates the ‘risk’ ‘new’ territory poses to incumbent re- election prospects.

Within this general framework, a number of specific analyses can be conducted utilizing the mean percent of the two major party vote for incumbents in counties which remain intact between reapportionment plans. The operationalization of the incumbency measure makes sense given the discussion above and follows Alford and Hibbing’s (1981: 1045) decision: ‘It would seem reasonable, therefore, to proceed with an analysis of that average electoral margin rather than an examination of unnecessarily complex measures.’

The analysis that follows is conducted on six states (Arkansas, Iowa, North Carolina, North Dakota, Utah and West Virginia) using historical county election returns for congressional elections supplied by the Inter-University Consortium for Political and Social Research (ICPSR Study 13: ‘General Election Data for the United States, 1970-1985’). The states were selected because in 1972 they redistricted entirely along county lines and with minor exceptions (five counties out of 353 counties) redistricted again in 1982 using counties as intact units.

It needs to be noted that the counties selected do not represent a random sample which obviously would be preferred. The use of a ‘convenience’ sample which in essence defines itself is not uncommon but it has the potential for bias. With the exception of North Carolina, the states included are smaller in population and more rural than many other states. Counties within these states do exhibit considerable variation in population (major metropolitan areas exist) and other demographic characteristics. However, redistricting practices in metropolitan areas which frequently cut across county lines and data considerations (the nonexistence of computerized election returns and other demographic characteristics at the sub- county level, that is, precinct level, with which partial county returns could be recomputed) makes the use of a random sample an impossibie task.

Each county was coded:

Incumbent congressional candidate’s % of 2-party vote INCxx VOTE % (where xx represents a designated year), the dependent variable

Type of territory, TERRxx (0 = old; 1 = new) Source of territory, SOlJRCExx [ 1 = old; 2 = new, inherited from a congress-

person of the same party; 3 = new, inherited from a congressperson of the opposite party; missing = an ‘open’ seat in 1972 or 1982, or inappropriate

CLYDE BROWN 127

for further analysis because the county was split during redistricting in 1982 or a state (North Dakota) did not redistrict in 1982.1

Party of the incumbent, PARTYxx (0 = Republican; 1 = Democrat) Partisan base, BASExx (% of 2-party vote the incumbent’s presidential candidate

received in 1968 for the 1972 redistricting and in 1980 for the 1982 redistricting)

URBANxx (% of population living in Census Bureau’s definition of an ‘urban area’) in 1970 and 1980

FAMPOVxx (% of families living under the Census Bureau’s definition of the poverty line) in 1969 and 1979

Incumbent vote share (INCxx VOTE %) is the dependent variable. Type (TERRxx) and Source (SOURCExx) are the primary independent variables of interest. The other variables serve as controls in the models that follow. Controlling for the partisanship of a county, BASExx, is especially critical in isolating the independent effects of TERRxx and SOURCExx since on the basis of theory partisanship can be expected to be an important determinant of incumbent electoral performance.

Table 1 contains a summary of the frequency of the type of territory variable (TERR72 and TERR82) and the source of territory variable (SOURCE72 and SOURCE82) for the two reapportionments.

First, simple ‘dummy’ regression can be performed to determine whether incumbents receive statistically different levels of electoral support in the ‘old’ territory versus the ‘new’ territory immediately following redistricting. The strategy employed here and below follows the advice of Achen (1982: 13-30) that regres sion techniques may be used for essentially descriptive purposes and inferences may be drawn ‘from the overall pattern, not just from particular coefficients’. The emphasis is not on modelling the dependent variable or maximizing explained variance, but instead on the pattern of estimated coefficients associated with particular variables.

TABLE 1. Summary of counties in the analysis

TERR72 Old New

Frequency

311 100

TERR82 Old 296 New 52 Missing/excluded 5

SOURCE72 Old New-same New-opposite Missing/excluded

SOURCE82 Old New-same New-opposite Missing/excluded

294 39 41 37

249 26 23 55

Percent

76 24

84 15

1

72 10 10 9

71

7 16


Recognizing the extensive literature on ‘incumbency advantage’, it is hypothesized that incumbents will run better in the ‘old’ parts of their districts than in the ‘new’ parts in the first election following redistricting.

Hyp. 1: The mean level of support for incumbents in the ‘old’ territory will be greater than the mean level of support in the ‘new’ territory in the first election following redistricting.

Given the coding, TERRxx will be negatively associated with INCxx VOTE % at the beginning of a reapportionment cycle. If confirmed by the data, a negative coeff- cient will indicate that redistricting is a risk to re-election; the magnitude of the coefficient will indicate how great the risk is. (This relationship could also be inter- preted as a measure of incumbent advantage: how much better does an incumbent run in territory previously exposed to his incumbency versus untreated territory?)

Second, although incumbents are expected to run worse in the new’ than in the ‘old’ territory, it is still expected that they will run well in the ‘new’ territory, perhaps even garnering a majority of the two party vote. A finding that incumbents win even in ‘new’ territory would be very strong evidence that redistricting does not pose a meaningful threat to re-election. This can serve as a ‘litmus’ test of the risk of redistricting.

Hyp. 2: The mean level of support for incumbents in the ‘new’ territory will be greater than 50% of the major two party vote.

Third, Born’s (1985) differentiation of new counties on the basis of their previous ‘status’ is suggestive and permits the testing of an hypotheses that incumbents will run best in their ‘old’ territory followed by ‘new’ territory inherited from an incumbent of the same party followed by ‘new’ territory from an incumbent of the opposition party. Again, regression provides the most direct means to test the following hypothesis for transitivity:

Hyp. 3: The mean level of support for incumbents in ‘old’ territory will be greater than the mean level represented by an incumbent of the same party which will be greater than the mean level of support in ‘new’ territory previously represented by an incumbent of the opposition party.

The hypothesis implies that SOLJRCExx will be negatively associated with INCxx VOTE %.

It has been noted that since the early 197Os, redistricted incumbents have been legally allowed to ‘frank’ mail into and solicit casework from territory which will be in their new district for the coming election. To my knowledge the extent of such practices have not been documented. Clearly, such activities could represent an ‘incumbency-like’ treatment which could distort and hide the relationships being tested above. The magnitude of such effects are unknown. However, until evidence to the contrary, it is reasonable to expect that the effect of these activities and the effect of campaigning are small compared to the cumulative effects of incumbency. Since these effects are relatively new, earlier tests (1970s compared with 1980s) of this hypothesis will suffer less from this confounding effect.

Fourth, it is hypothesized that over time differences in the level of support for incumbents in the types of ‘new’ territory compared with the ‘old’ territory will diminish as incumbents are able to turn their attention to the ‘new’ territory. Such a ‘conversion’ hypothesis makes logical sense if one assumes that incumbents are

CLYDE BROWN 129

risk adverse and that the treatment of incumbency will show greater marginal returns of support in the ‘new’ territory than in the ‘old’. Alford and Hibbmg (1981) have documented diminished marginal returns to electoral margin associated with increased tenure in office and Jacobson’s (1987) analysis supports the view that incumbents are risk adverse. If incumbents are risk adverse and the ‘new’ territory is the weak lii in their electoral armour, then it is reasonable to expect incumbents to take steps to bring the ‘new’ territory into line with the level of support they receive elsewhere. Another possibility is that incumbents ‘leave no stone unturned’ and do everything possible to stay in office. In this event, ‘old’ and ‘new’ territory would receive the treatment of incumbency equally within the constraints of resources. The ‘Law of Diminishing Marginal Returns’, however, leads to the conclusion that with equal allocation of resources between the two types of territory the weaker new’ territory (from the incumbent’s perspective) will become like the stronger ‘old’ territory over time. The hypothesis is that the initial lower level of incumbent support in the ‘new’ territory in comparison with the ‘old’ territory will diminish the longer the ‘new’ territory is in the incumbent’s district. The ‘conversion’ hypothesis suggests that the ‘new’ territory will become like the ‘old’ territory in time.

Hyp. 4: The difference in mean level of support for incumbents in ‘old’ and ‘new’ territories will be reduced over time.

Fifth, incumbents have been coded to control for partisan differences in the hypotheses above. National conditions are known to have favoured particular political parties in specific election years by affecting the quality of challengers and voters preferences. Jacobson (1990:76) has succinctly summarized the concept of national tides in House elections: ‘Historically, unpopular presidents or presidential candidates, unpopular or failed national policies, and poor economic performance have all cost the administration’s party seats in Congress. Successful presidents and policies have, in the short term as well as the long run, added to a party’s congressional strength.’ Inter-election vote swings have been calculated which estimate the degree to which nationai tides favoured candidates of one party or the other. The swing ratio for the party gainmg votes in the House of Representatives favoured the Democrats in 1974, 1976 and 1982, the Republicans in the other years in the study (Jacobson, 1990; and Stanley and Niemi, 1990). Variation from national trends is possible given the sample of congressional districts in the analysis, still there is an expectation that PARTY= will capture the known pattern of inter-election vote swings.

Hyp. 5: Party of the incumbent will be positively associated with incumbent vote share in 1974, 1976 and 1982 and negatively associated in 1972, 1978, 1980 and 1984.

Party of the incumbent is included as a control variable. It is not expected that a consistent sign favouring one party will appear. It can also serve as a general check on the representativeness of the included states.

Findings

Analysis 1: Simple Regression Models

Table 2 presents the results of regressing TERRxx and SOURCExx on INCxx VOTE % (including PARTYxx as a control) for the seven congressional elections from 1972

130 Zbe Electoral Risk of Redistricting

TABLE 2. Summary of simple regression models

INC72 VOTES %

INC74 VOTE %

INC76 VOTE %

INC78 VOTE %

INCSO VOTE %

Intercept 63.25*

(86.86) 62.93*

(46.47) 62.83*

(74.83) 63.53*

(67.76) 63.19*

(41.36)

Intercept 59.08*

(55.58) 60.14*

(29.43) 49.85*

(55.67) 49.oa*

(48.77) 46.25*

(26.80)

Intercept 66.13;

(72.15) 67.81*

(41.75) 61.72‘

(52.64) 60.60’

(4 1.07) 59.40*

(24.14)

Intercept 67.94’

(69.49) 69.61’

(36.95) 68.88’

(50.29) 71.15’

(44.28) 75.23’

(28.17)

Intercept 60.85’

(3 1.42) 56.35’

(7.33) 68.61*

(13.98) 68.61*

(13.94) 65.76 (7.45)

TERR72 -2.36***

(-1.64)

-2.42” (-1.68)

TERR72 -0.83

(-0.83)

2.68** (1.66)

TERR72 -1.18

(-0.64)

2.25 (1.25)

TERR72 -3.77”

(- 1.86)

-5.41*

(-2.59)

TERR72 4.50

(0.65)

5.85 (0.86)

SOURCE72 PARTY72

-0.21 (-0.24)

-0.25

(-0.29)

SOURCE72

-0.43

(-0.34) -0.62

(-0.49) -0.47

(-0.37)

PARTY74

-0.92 (-0.68)

2.47’ (2.44)

SOURCE72

21.38* (15.51) 21.70’

(15.63) 22.10’

(15.80)

PARTWG

-1.35 (- 1.39)

1.16 (1.07)

SOURCE72

6.75* (4.49) 7.58’

(4.62) 7.76’

(4.38)

PARTY78

- 1.85*** (- 1.60)

-3.45’ (-2.75)

SOURCE72

-3.35** (-1.86) -4.81*

(-2.59) -5.73”

(-2.91)

PARTY80

4.50 (0.65)

5.86 (0.86)

-8.59*.’

(- 1.63) -9.12**

(-1.71) -9.12**

(-1.71)

RZ N ,008 334

,000 334

,000 334

,008 334

,000 334

R* N ,001 282

,002 282

,462 282

,467 282

,473 282

RZ ,003

,013

,118

.127

.124

R2 .020

,015

.020

.056

.081

R’ ,009

.009

.051

-065

.065

N

153

153

153

153

153

N

175

175

175

175

175

N 51

51

51

51

51

INC82 VOTE %

Intercept 63.15*

(72.43) 67.76’

(36.56) 58.37*

(45.88) 59.17.

(45.32) 64.21*

(32.10)

INC84 VOTE %

Intercept 65.20’

(72.23) 67.72*

(36.99) 68.91’

(58.35) 69.83

(54.13) 72.94’

(36.07)

CLYDE BROWN

TBRR82 SOURCE82 -4.92,

(-2.29) -4.27’

C-3.16)

-5.01* (-2.39)

-4.67# f-3.55)

TBRR82 SOURCES2 -1.85 (0.96)

-2.23*’ (- 1.78)

-3.13” f-1.71)

-2X$7* (-2.43)

PARTY82

6.39

“Z:Z * (4.02) 6.52’

(4.12)

PARTY84

-6.94’ (-4.53) -7.33’

(-4.76) -7.40*

(-4.85)

R2 ,017

.032

.050

.068

.086

131

N 298

298

298

298

298

R2 N ,005 160

.020 160

.114 160

.131 160

.I48 160

t-statistics reported in parentheses ‘p c.05, **.05 < p < .lO, ***. 10 c p q.15 {2-tail tests)

to 1984. In the analyses performed, incumbents are tracked through time; if an incumbent is defeated the counties in that congressional district are subsequently dropped or if an incumbent runs unopposed in a particular election the counties are excluded from that year’s analysis. The 1970s redistricting is anaiysed first.

Hypothesis 1 is confirmed for the 1972 election: incumbents did run better in the ‘old counties when compared with the ‘new’ counties. Although not of large substantive impact, incumbents are estimated to have run about 2.4 per cent worse in the ‘new’ territory. Except for very close elections, the 1972 redistricting would not pose a significant risk to re-election.

Corroboration is found for Hypothesis 4: the initial diierence in incumbent support in the two kinds of counties dissipated in subsequent elections. Although 1978 is an exception, the general pattern supports the ‘conversion’ hypothesis in that ‘ERR72 is not negative and statistically significant in the other three elections.

Hypothesis 2 which suggests the incumbents would on average win majority support in the ‘new’ counties is supported. Incumbents, according to the model, received in excess of 50 per cent even when TERR72 or SOURCE72 is negative and statistically significant as in 1972 and 1978. (Incumbent support in the ‘new’ territory can be calculated by multiplying the coefficient estimate for the variable by its coded value and then subtracting it from the intercept term.) Given the small values of the coefficients, it is clear that on average incumbents won the ‘new’ territory.

Hypothesis 3 receives little support here. The previous partisan representational history of a county did not have an independent effect on incumbent vote share except in 1978. In general, ‘new’ territory regardless of source was not a threat to re-election in the 1970s.

Party of the incumbent @ARTYxx) is statistically significant and in the predicted direction in four of the five elections. The exception is 1972, it is in the predicted


direction but insignificant. Often the coefficients are very sizeable as in 1974 in the wake of the Watergate scandal which was an especially favourable year for Democrats or in the 1980 Reagan landslide when Republicans gained 33 seats in the House. Hypothesis 5 is confirmed providing evidence that the counties in the analysis generally behaved as the nation did over time.

The impact of the 1980s redistricting can be investigated by examining the 1982

and 1984 elections. Redistricting did not pose a substantive risk to incumbents in the 1970s: What was the situation in the early 198Os?

Incumbents in 1982 ran about 5 per cent worse in their newly added territory when compared with counties they had previously represented as shown by the coefficients on TERR82. This fmding provides additional confirmation of Hypoth- esis 1: redistricting poses a small threat to incumbent re-election.

Unlike the 1970s analysis of these two elections reveals an impact for the previous partisan representation of a county. The statistically significant negative coefficient on SOURCE82 indicates that incumbents do slightly worse in counties they inherit from members of the opposition party. While Hypothesis 3 was not confirmed for the 1970s it is supported in the 1980s.

Hypothesis 4 receives confirmation. The 1984 election documents a diminished negative impact of the ‘new’ counties over time. The smaller negative coefficients in 1984 compared with 1982 for TERR82 (approximately -3% compared with -5%) and SOURCE82 (about -2.5% versus -4.5%) supports the contention that incumbents are able to minimize the electoral difference between the different types of counties over time. Subsequent elections, however, need to be examined before a clear determination can be made about the 1980s.

Again, as in the 1970s the magnitudes of TERR82 and SOURCE82 are small both in an absolute sense and in relationship to the intercept term. This indicates that incumbents received a majority of the vote on average in the ‘new’ counties. This finding is consistent with Hypothesis 2.

Incumbents in the sample were affected by the general political climates surrounding the two elections. As suggested by Hypothesis 5, Democrats benefited from the national tide in 1982, Republicans in 1984.

In conclusion, the 1980s as the 1970s did not pose a meaningful electoral threat to incumbent re-election. Although incumbents do receive less support in the ‘new’ territory as hypothesized, the decline in electoral performance is small. Clearly, incumbents are not at risk of losing re-election. Furthermore, they are able to ‘convert’ the ‘new’ territory so that it performs like the ‘old’ in short order.

Analysis 2: Multivariate Regression Models

It is possible that the results presented in Table 2 might be spurious since the models are very simple, involving either bivariate regression or the use of the incumbent’s party affiliation as a control. To test the robustness of the findings on the type and source of counties composing the redistricted districts, multivariate regression models were specified:

INCxx VOTE % = a + b, TERRxx + bz BASExx + 6, PARTYxx + 0, FAMPOVxx + Oj URBANxx + e

INCxx VOTE % = a + 0, SOURCExx + 0, BASExx + b, PARTYxx + 6, FAMPOVxx + b, URBANxx + e

CLYDE BROWN 133

where a = intercept, bs’ = coefficients to be estimated, e = error term, and all other terms are as defined earlier. The results of the first specification appear in Table 3 and of the second equation in Table 4. Of primary interest are the coefficients on TERR which tests for the difference between ‘old’ and ‘new’ counties and SOURCE which tests for a difference between previously represented counties, counties inherited from an incumbent of the same party, and counties inherited from an opposition-party incumbent. It is not advisable to include TERR and SOURCE in the same model because of severe multicollmearity between the two variables.

The models contain controls for a partisan political baseline (BASE), party label for the incumbent (PARTY) and two demographic characteristics-extent of family poverty (FAMPOV) and degree urban (URBAN). BASE controls for partisanship of individual counties expressed in terms of electoral strength of the incumbent’s presidential candidate in the presidential election ~med~tely preceding redistricting. This is an important addition because it will account for the variation in the dependent variable attributable to the general strength of the political parties in each county. PARTY controls for the differences between Republican and Democratic incumbents. The two demographic variables are included as controls for the social composition of the electorate in each congressional district and to compensate in part for the utilization of a ‘convenience’ sample, that is, the possibility that the demographic character of the six states included biases the results. Separately and together these controls should heip provide a truer estimate of the variables of interest, TERR and SOURCE. In addition, the inclusion of these variables should result in increased explained variance. Examination of the coefficients of determination (Rz) in the three tables shows this to be the (often dramatic) case.

The results of the first multivariate specification are documented in Table 3. TERR is negative and statistically significant for the 1972 and 1982 elections providing a confirmation of Hypothesis 1 which expected incumbents to receive less support in counties added to their district by redistricting. In the first election following redistricting, incumbents did three to five per cent less well in the ‘new’ counties. As in the simpler models, redistricting is not much of a threat to incumbents.

TABLE 3. Summary of regression models with type of territory (‘IERR)

Intercept TBRR BASE PARTY FAMPOV URBAN R* N INC72 39.22’ -3.03* 0.34’ 2.6.Y 0.39” 0.02 .13 324 VOTE % (10.04) (-2.06) (5.75) (1.74) (4.25) (0.71) INC74 29.36 0.64 0.36* 24.39: 0.3s* 0.10 .54 282 VOTE % (5.59) (0.42) (5.68) (14.69) (4.43) (0.99) INC76 35.61’ -1.32 0.40’ 11.45’ 0.32* -0.03 .36 153 VOTE % (8.46) (-0.81) (5.82) (5.79) (3.86) (- 1.03) INC78 50.61’ -6.24* 0.19* -4.76% 3.86* 0.09’ .I8 175 VOTE % (9.75) (-2.96) (2.21) (-2.37) (4.66) (2.41) INC80 20.09* 3.89 0.69 -3.31 0.79* 0.03 .52 51 VOTE % (2.11) (0.75) (5.03) (-0.75) (4.13) (0.49) INC82 19.64’ -5.31’ 0.75’ 13.02* -0.15 -0.01 .46 296 VOTE % (6.08) (-3.22) (13.84) (9.54) (- 1.22) (-0.54) INC84 42.38* -2.56’* 0.43’ -3.49; 0.22 -0.00 .48 160 VOTE % (12.17) (- 1.78) (7.39) (-2.17) (1.28) (-0.03)

t-statistics reported in parentheses * p < .05 ** .05 < p < .lO (Ztail tests>


TABLE 4. Summary of regression models with source of territory (SOURCE)

Intercept SOURCE BASE PARTY FAMPOV URBAN RZ N INC72 39.59 0.15 0.31’ 2.73** 0.38* 0.03 .12 324 VOTE % (9.89) (0.16) (5.23) (1.76) (4.16) (1.00) INC74 22.49’ 1.07 0.35’ 24.47* 0.38’ 0.03 .55 282 VOCJ06% (5.31) (1.08) (5.50) (14.75) (4.42) (1 .OS)

37.73’ -1.12 0.41’ 11.11’ 0.32* -0.03 .36 153 VOTE % (8.55) (-1.13) (5.89) (5.51) (3.90) (-1.07) INC78 54.99 -3.83’ 0.20’ -5.56* 0.50* 0.08* .18 175 VOTE % (10.37) (-2.97) (2.27) (-2.71) (4.49) (2.34) INCBO 23.99* 0.79* 0.03 .52 51 VOTE % (2.88) (Z) (z;; (3:::) (4.13) (0.49) INC82 25.19’ -4.03’ 0.75’ 13.12* -0.14** -0.03 .46 296 VOTE % (7.24) (-3.88) (13.72) (9.79) (-1.88) (-1.02) INC84 44.28* - 1.73** 0.47* -2.13 -0.03 -0.01 .48 160 VOTE % (11.61) (-1.83) (7.89) (-1.22) (-0.23) (-0.42)

t-statistics reported in parentheses * p < .05 ** .05 < p <.lO (2-tail tests)

The results in Table 3 support the contention found in Hypothesis 4 that differences between ‘old’ and ‘new’ territory will be reduced over time. Again, as in the simple regression models, 1978 is an exception, but the overall pattern supports the conclusion that even the small differences between counties reported immediately above were eliminated in subsequent elections. In 1974, I976 and 1980, there is not a statistically significant difference between the two types of counties; incumbents ran equally well in both types of counties. In 1984, the second election under the 1980s redistricting, the statistically significant coefficient is halved from the 1982 estimate (from -5.31% to -2.56%).

One should probably not make too much out of the 1978 election. Statistical analysis operates on the assumption of probabilistic generalizations rather than immutable laws; the search is for patterns, albeit less than perfect, in the data. Given incomplete knowledge about the social world, there are bound to be ‘exceptions’ to the general patterns in the data (Achen, 1982). Even with that said, however, it is hard not to hazard a guess that the source of the 1978 ‘anomaly’ lies in the specific counties included in the analysis. The increase in the number of counties in the analysis in 1978 over I976 (a reversal in the progression of fewer counties with each subsequent election) results from the net combination of three factors: (1) counties coming back into the analysis when incumbents faced challengers in 1978 but not in 1976, (2) counties dropping out of the analysis when incumbents faced opposition in 1976 but not in 1978, and (3) counties dropped from the analysis because incumbents’ political careers ended in 1976 by defeat or retirement. Some variation is inevitably introduced by the counties included from election to election; the 1978 election is notable (and volatile) in this regard as four districts returned to the analysis and four districts were dropped. In particular, a combination of several strong incumbents who did not face opposition in 1978, and unopposed incumbents in 1976 who might have actively utilized the advantages of incumbency in the interim leading up to the 1978 election together would diminish the relationship expected by Hypothesis 4.

CLYDE BROWN 135

Evaluating Hypothesis 2 is a bit more complicated, but it is evident that redistricting did not substantially jeopardise incumbent re-election chances when the impacts of other variables are considered. Of much more substantive impact on incumbent vote share (INC VOTExx %> is the partisan strength of the county (BASE). The magnitude of the impact of this variable can be determined for each county by multiplying the coefficient for BASE by the electoral strength in percent- age points of the incumbent’s presidential candidate in 1968 or 1980 (ranging from 30% to 75%). This explains why the intercept estimates are so much smaller in Tables 3 and 4 than in Table 2. Not surprisingly, BASE is statistically significant for all the elections. Similarly, partisan differences between incumbents (PARTY) generally conform to empirical understandings of which way the political winds were blowing at the time. The estimates are significant and in the hypothesized direction for five of the seven elections. In the other two cases, the Democratic incumbents in the sample ran better than their party’s incumbents did nationally. When the partisan baseline support for counties and the inter-election vote swings are taken into account, it is clear that incumbents are not put at risk by the addition of ‘new’ territory.

The results of the second multivariate model are found in Table 4. (The coefficient estimates in Tables 3 and 4 should be and are, very similar, since only one variable is being recoded.) Table 4 reveals little support for a differential effect for the three kinds of counties (SOURCE) for the 1970s redistricting; only 1978 has a significant coefficient of the correct sign. For the 1980s districting cycle SOURCE was significant in 1982 and 1984, but the coefficient was substantially cut from -4.03 to -1.73 in 1984. Hypothesis 3 is supported for the 1980s but not the 1970s.

It should be noted that Born’s (1985: 316-17) analysis of this hypothesis and the research reported here had only one state in common. Furthermore, this study provides a more elaborate set of statistical controls, including the partisanship variable, to isolate the effect of SOURCE. The difference between the two studies is undoubtedly explained by the samples and statistical models used. The stronger effect of SOURCE over time is interesting and consistent with Born’s evidence.

Likewise, Hypothesis 1 is sustained for 1982 but not 1972. The source of a county did not have an independent effect on incumbent vote share in 1972. It did matter whether a county was ‘new’ or ‘old’ as revealed in Table 3 but the previous partisan representational history did not matter. In 1982, knowing whether a county was inherited from an incumbent of the same or opposition party helps to explain incumbent vote share.

The partisan nature of the county (BASE) is again the dominant variable in the model. It is statistically significant for all elections. The coefficients on incumbent partisanship (PARTY) mirror those found in Table 3. When the contributions of these variables are added to the intercept term it is clear how strong incumbents run across the board, supporting Hypothesis 2. Checks on the impact of vote swings generally confirm Hypothesis 5.

Conclusion

The research reported here was undertaken to provide a measure of incumbency risk by explicitly identifying the electoral threat of redistricting. That risk can be measured by how poorly incumbents run in their ‘new’ territory compared with their ‘old’ and how long it takes them to eliminate that risk, if it exists, by


converting the ‘new’ territory so that it provides electoral support comparable with that of the ‘old’ territory.

The research finds little impact for redistricting. Incumbents did run less well in the ‘new’ territory in 1972 and 1982, but the difference was only a few percent- age points in elections which incumbents were winning handily. It is possible that the difference between types of counties starts out much greater, but that incumbents are able through their activities, congressional and electoral, to quickly culti- vate the ‘new’ territory. When these ‘new’ counties are combined with counties the incumbent has previously represented, they pose no meaningful threat to re- election. Interestingly, Born’s (1985) hypothesis regarding the source of ‘new’ territory was not confirmed for this sample in the 1970s; it was supported in the 1980s consistent with his evidence that this effect has become stronger over time. Even so, counties inherited from an incumbent of the other party represent little risk to re-election. Furthermore, incumbents appear able in a very short time to ‘convert’ the ‘old’ territory so that it performs electorally like any other territory in the district. By the second election, the difference in incumbent support in the different types of territory had disappeared in the 1970s and had been drastically reduced in the 1980s.

The finding that redistricting is of little risk to incumbent reelection using a new data source is consistent with previous research by political scientists on this topic. All the activity and speculation (the fear and glee spoken of earlier) associated with reapportionment, the immense efforts of political parties and candidates (challengers and incumbents) in response to the potential opportunities and threats of redistricting, results in little actual change in electoral competition or the partisan distribution of seats in Congress. Although the details by which this outcome is obtained need additional study, the ‘election day reality’ (Born, 1985) is that redistricting poses little risk to incumbents.

References

Christopher H. Achen, Interpreting and Using Regression, Sage University Papers on Quanti- tative Application in the Social Sciences, No. 29, (Beverly Hills and London: Sage Publi- cations, 1982).

John R. AIford and John R. Hibbing, ‘Increased Incumbency Advantage in the House’,Journal of Politics, 43:4, November 1981, pp. 1042-61.

Michael Barone and Grant Ujifusa, The Almanac of American Politics, (Washington, DC: Barone & Company, 1986 and earlier years).

David Beiler, ‘The National Political Sweepstakes’, Campaigns & Elections, 9:6, January/February 1989, pp. 13-19.

Richard Born, ‘Partisan Intentions and Election Day Realities in the Congressional Redis- tricting Process’, American Political Science Review, 79:2, June 1985, pp. 305-19.

Charles S. Bullock III, ‘Redistricting and Congressional Stability, 1969-72’, Journal of Politics, 37:2, May 1975, pp. 569-75.

Bruce Cain, ‘Assessing the Partisan Effects of Redistricting’, American Political Science Review, 79:2, June 1985, pp. 320-33.

Congressional District Atlas (Washington, DC: Government Printing Office), various years. Albert D. Cover, ‘One Good Term Deserves Another: The Advantage of Incumbency in

Congressional Elections’, American Journal of Political Science, 21:3, August 1977, pp. 523-41.

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John A. Ferejohn, ‘On the Decline of Competition in Congressional Elections’, American Political Science Reuiew, 71:1, March 1977, pp. 166-76.

CLYDE BROWN 137

Amihai Glazer, Bernard Grofman and Marc Robbins, ‘Partisan and Incumbency Effects of 1970s Congressional Redistricting’, American Journal of Political Science, 31:3, August 1987, pp. 680-707.

J. David Gopoian and Darrell M. West, ‘Trading Security for Seats: Strategic Considerations in the Redistricting Process’, Journal of Politics, 46:4, November 1984, pp. 1080-96.

Gary C. facobson, The Electoral Origins of Divided Government, (Boulder, Co: Westview Press, 1990).

Gary C. Jacobson, ‘The Marginals Never Vanished: Incumbency and Competition in Elections to the U.S. House of Representatives, 1952-82’, American Journal of Political Science, 31:1, February 1987, pp. 126-41.

Charles 0. Jones, ‘New Directions in U.S. Congressional Research’, Legislative S&dies Queers, 63, 1981, pp. 455-68.

Michael Lewin, ‘Defending Your Turf, Campaigns G ELections, 9:6, January/February 1989, pp. 16-17.

David Mayhew, ‘Congressional Elections: The Case of the Vanishing Marginals’, Polity, 6:3, Spring 1974, pp. 295-317.

Louis Peck, ‘Project 500 and the 1991 Initiative’, Campaigns G Elections, 96, JanuaryiFebru- ary 1989, p. 15.

Harold W. Stanley and Richard G. Niemi, Vital Statistics on American Politics, (Washing- ton, DC: Congressional Quarterly Press, 1990).

Documents

The electoral risk of redistricting: Evidence from the United States