Split-Ticket Voters, Divided Government, and Fiorina's Policy-Balancing ModelAuthor(s): Richard BornSource: Legislative Studies Quarterly, Vol. 19, No. 1 (Feb., 1994), pp. 95-115Published by: Comparative Legislative Research CenterStable URL: http://www.jstor.org/stable/439802 .Accessed: 04/01/2011 02:37

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RICHARD BORN Vassar College

Split-Ticket Voters, Divided Government, and

Fiorina's Policy-Balancing Model

To account for the increase in divided government in the United States, Fiorina has advanced a purposive theory of split-ticket voting that emphasizes voters' desire to balance the relative policy extremism of the Democratic and Republican par- ties. This study uncovers little empirical evidence to substantiate the policy-balancing model. Respondents' issue-scale placements of the president and federal government challenge the premise that national policy is perceived as a weighted average of the indi- vidual positions staked out by the executive and congressional branches. More impor- tantly, conditional logit analysis in three of the five presidential-year elections from 1972 to 1988 provides no support for Fiorina's central tenet that voters will endorse the presidential-House pair for which the averaged partisan position is closest to their own ideological preference. Finally, there is only scattered support for the propositions that are developed as logical extensions of this theory.

Noting the rising tide of split-ticket voting for federal offices in the 1950s and 1960s, political observers sometimes posited that the electorate was unable to discern meaningful differences between par- ties (Broder 1972, 13; Phillips 1975, 134-35). In the 1956 election, for example, Broder sees this lack of interparty differences-as well as Eisenhower's vast personal popularity, which transcended partisan loyalties-as explaining the failure of the victorious presidential party (for the first time in 108 years) to capture either congressional cham- ber (1972, 12-13). Ironically, a prominent new theory has been advanced by Morris Fiorina, contending that the even higher levels of split-ticket balloting in more recent elections result from the parties' increased ideological estrangement from one another and from the electorate (1988, 442-53; 1989, 24-28; 1992, 73-82). Other contem- porary scholars, while unwilling to rule out competing theories, have used arguments similar to Fiorina's (Ladd 1985, 23-24). In fact, Erikson (1988, 1027) has suggested that votes against the president's party in off-year House elections may be a kind of lagged ticket- splitting behavior designed to moderate White House initiatives.

LEGISLATIVE STUDIES QUARTERLY, XIX, 1, February 1994 95

Richard Born

Fiorina's policy-balancing model has at its heart the hypothe- sis that voters view policy as a simple weighted mean of the stances taken by the party of the president and the congressional majority party. The weighting factor for the president's position (q) is presumed to exceed that for Congress (1 - q) because executive policy is per- ceived as preeminent. Graphically, then, the four partisan combina- tions of governance (where the first party in each pair controls the presidency and the second controls the Congress) may be plotted on a simple left-right scale (1988, 443; 1989, 25; 1992, 76):

DD DR RD RR I I I I I I I

M1 M M2

(DR is closer to DD than to RR, and RD nearer to RR than to DD, because of the q > .5 assumption). According to the model, citizens will vote for the pair of candidates whose parties' average, weighted position is closest to their own position; thus, those between the DD-DR midpoint M, and the overall midpoint M will cast a Democratic-Republican ballot, those between M and the RD-RR midpoint M2 will vote Republican-Democratic, and voters more extreme than M or M2 will vote straight-party Democratic or Republi- can, respectively.

Jacobson (1989, 144; 1990a, 106), in developing a ticket- splitting theory based on voters' self-contradictory policy goals, has observed that survey data supporting Fiorina's model are rather slight and that the model demands an unrealistically high degree of calcula- tion on the voter's part. Alongside these criticisms, another question may be posed: why, if citizens increasingly desire a Congress of the opposing party to balance the relative extremism of presidential initia- tives, do they split their votes more often in Senate-House balloting as well? Such divided-party voting in presidential-year elections, like ticket splitting in presidential-House elections, has virtually doubled in frequency from 1952-68 to 1972-88 (Stanley and Niemi 1990, 132).

Criticisms like these, however, take account of two tests per- formed by Fiorina that appear to substantiate the policy-balancing model. The hypotheses that Fiorina constructs as logical outgrowths of his model need to be assessed as well. Attention will turn to these mat- ters in this paper.

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Split-Ticket Voters

Fiorina's Two Experiments

Fiorina initially ascertains whether empirical support exists for his basic postulate that policy may be conceptualized as a weighted average of the positions attributed to the presidential and congressio- nal majority parties. If this postulate were true, he properly reasons, estimates of the federal government's location on various issue scales should less powerfully relate to estimates of the president's position when there is split-party, rather than one-party, control of government. This expectation is met, in fact, but only weakly: on three of the four issue scales presented by the CPS/ANES to respondents in both 1980 and 1984, Fiorina finds the correlation of presidential placement with government placement to be larger in 1980, a year of united Demo- cratic control (1988, 449). In view of this less than conclusive out- come, extending the test to the 1988 presidential election seems an advisable next step. These results are included in Table 1. Years before 1980 cannot be examined, because the survey included no question dealing with government issue placement. All 1988 correlations turn out to be at least somewhat stronger than those in the 1980 election, contrary to what was anticipated. The midterm correlations do seem to offer some support for Fiorina's theory, in that all those computed in 1982, 1986, and 1990-years of divided government-are lower than the 1980 correlations for the same issue scale. With only one exception, however, the midterm r values are also weaker than the comparable values in either 1984 or 1988, probably indicating that it is more difficult to assess both government and presidential positions in years without a presidential race to showcase national issues. It is thus necessary to single out presidential-year elections for study, and the 1988 results challenge Fiorina's basic postulate.

Fiorina's more elaborate second test is more central to his the- ory, because it directly examines whether voters indeed endorse the pair of candidates for president and representative whose parties' average weighted position appears closest to their own ideological pref- erence (1988, 450-52). Employing 1984 data, Fiorina first estimates a binomial logit equation with presidential choice as the dependent var- iable (1 if Republican, 0 if Democratic). Three right-hand side varia- bles indicate evaluations of President Reagan's job performance (each, in turn, taking the value of 1 for those approving strongly, approving not strongly, and disapproving not strongly). Two variables tap party affiliation (the first coded 1 for Republicans, the second coded 1 for independents). A final variable gauges the respondent's relative dis- tance from the two parties (measured as the absolute difference

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Richard Born

TABLE 1 Correlations between Respondents' Placements of the Federal

Government and the President on Issue Scales (numbers of respondents in parentheses)

Issue Area 1980 1982 1984 1986 1988 1990

Defense Spending .594 .410 .621 .404 .608 .509 (1124) (997) (1791) (1747) (1572) (1489)

Government Aid to .508 .386 .445 .606 .400 Minorities (1002) (980) (1738) (755) (1340)

Government-Guaranteed .562 .396 .469 .589 Job and Good Standard (944) (1045) (1714) (1436) of Living

Women's Role/Social .486 .482 .457 and Economic Status (992) (976) (1630)

Note: Data are from the CPS/ANES survey. Correlations are calculated for all respon- dents rather than for voters alone. The aid to minorities questions in 1988 were adminis- tered to Form B respondents only. In 1980 and 1982, the women's issue was support for an equal role; in 1984, it was government efforts to improve women's social and eco- nomic status. In some years no questions were asked related to a particular issue area.

between self-placement on a 7-point liberal-conservative scale and the position the voter assigned to the Democratic party, subtracted from the absolute difference between self-placement and the Republican party's position. Likewise, a parallel equation for House voting is esti- mated, with the same variables as above, as well as a variable designat- ing the incumbency status of the contest in the respondent's district (coded 1 for respondents in districts represented by a Republican incumbent, 0 for those in open-seat districts, and -1 for those with a Democratic incumbent). To derive a summary measure of how well the separate estimations forecast presidential-House ballot combina- tions, Fiorina identifies respondents whose true choice for each office is correctly determined by each equation. Among those voting for both a presidential and a House candidate, the percentage who twice are accurately forecast can then be computed.

For comparison, Fiorina also computes the overall predictive power of a single-equation model incorporating his assumption about the public's simultaneous resolution of presidential and House choices. McFadden's conditional logit technique is applied here, wherein each possible voting pair is postulated to be a function of its utility to the respondent with regard to all independent variables used in the two binomial logit equations above. This model correctly

98

Split-Ticket Voters

projects the decisions of only slightly fewer respondents (74.2%) than do the separate equations in tandem (74.4%). Considering what he deems the rather restrictive policy-balancing hypothesis that gave rise to the utility function used in the joint decision model, Fiorina views his findings as "at the least highly suggestive" of the hypothesis's valid- ity (1988, 451).

A somewhat questionable facet of this analysis is that it includes respondents in districts lacking two-party House competi- tion. Such people are unable to balance their presidential choice with support for a House candidate of the opposing party if that party is absent a candidate. But since only 7.2% of the congressional electorate analyzed (or 7.1% of those voting for both offices) come from unop- posed districts, the problem is unlikely to be consequential.

Of greater import is an inadvertent error Fiorina makes in operationalizing the key ideological distance utility function, one that jeopardizes the analysis as a test of the policy-balancing hypothesis. For each partisan composite of presidential and House choice, Fiorina gauges the ideological utility to the respondent as follows.

Presidential Choice House Choice Ideological Distance Utility Function

Republican Republican q X - R + (1 - q) Ix - R Republican Democratic q X-R + ( - q)-D Democratic Republican q X - D + (1 - q)X - R Democratic Democratic q X - D + (1 - q)X - D

(As before, q is the president's weight in determining policy; D, X, and R are the ideological scale placements for the Democratic party, the respondent, and the Republican party.) Lower utility function values connote more preferable ballot options. The overall coefficient of this function (p) and the magnitude of q are computed indirectly. Two sep- arate ideological distance variables are inserted into the data matrix, each corresponding to one of the absolute value terms above. Since the conditional logit parameter estimated for the first absolute value term (a) must thus equal pq, while the parameter for the second (y) will be P(l - q), simple manipulation of the identities finally yields the values of p and q.

As an example, consider someone who places the Democrats, himself, and the Republicans, respectively, at 2, 4, and 6 on the ideo- logical scale. For any value of q, the ideological utility to this person of each of the four partisan combinations will be identical: q(2) + [1 - q(2)] = 2. This contravenes the logic of the policy-balancing model, since anybody equidistant from the Democratic and Republi-

99

Richard Born

can positions must find both split-party options to be precisely at his ideal point, equally preferable to either straight-ticket pair.

The proper utility specification is in the table below.

Presidential Choice House Choice Ideological Distance Utility Function

Republican Republican X - (qR + (1 - q)R) Republican Democratic X - (qR + (1 - q)D) Democratic Republican X - (qD + (1 - q)R) Democratic Democratic X - (qD + (1 - q)D)

In other words, rather than being a weighted average of a respondent's distance from the presidential and House party positions, the function should be the respondent's distance from the weighted average of these positions. For the situation where q = .5 and the voter, as before, is equidistant from Democratic and Republican locations of 2 and 6, the split-party utilities will thus be 0, less than the value of 2 assigned to each straight-party choice. Any other q value here, except in the trivial case where it is 0 or 1, will still make both split-party options more attractive than the others; the same, as Fiorina intends, must hold true as long as the respondent is anywhere between the two parties' positions.2

It appears to be impossible to estimate ,3 and q for this valid utility function delineation in any way that is analogous to the approach taken by Fiorina. No amount of manipulation will permit the term appearing in either split-ticket line to be recast as a simple sum of absolute values, as before. Nor does any other solution to the estimation problem seem feasible. Still, it is possible to experiment with various predefined values of q in order to develop a sense of how well a model properly embodying policy-balancing assumptions fore- casts voters' behavior. Data for 1984 are once again utilized, this time deleting respondents in unopposed House districts.

The results of this exercise are presented in Table 2. (Only the binomial equation parameters appear; conditional logit parameters obtaining at specific values of q are available on request.) The q values cover a broad range (.50-.85), wherein the true weight accorded the president as controller of policy almost certainly lies. Calculations at .05 intervals of q are reported; those carried out within these intervals never produced a forecast that was worse than the very worst achieved at a .05 cutting point or better than the very best. The range of predic- tive accuracy is very narrow (between 73.9 and 74.1%), and the values are very close to the 74.4% that Fiorina reached with the independent binomial equations for presidential and House voting. Thus, in redo-

100

Split-Ticket Voters 101

TABLE 2 Replication of Fiorina's Voting Equations for 1984, with

Respondents in Uncontested House Districts Removed and the Correct Utility Function for Ideological Distance Employed

(standard errors in parentheses)

Binomial Logit Analysis

Presidential Voting House Voting Variables Equation Equation

Constant -3.439*** -2.314*** (.388) (.348)

Evaluation of President's Job Performance

Approve Strongly 5.003*** 2.804*** (.458) (.399)

Approve Not Strongly 3.281"*' 2.354**' (.391) (.382)

Disapprove Not Strongly .437 1.203* (.466) (.415)

Party Identification Republican 2.377*** 1.406***

(.364) (.285) Independent 1.508*** .645**

(.259) (.252) Ideological Distance -. 188*** -.075*

(.039) (.034) Incumbent in House Contest 1.387***

(.129) N 1024 821 Percentage Correctly Predicted

by Each Equation Separately 90.9 80.5 by the Two Equations in Tandem 74.4

Conditional Logit Analysis

q Computed with Fiorina's Specification of Utility Function .738 Percentage Correctly Predicted with Respecification of Utility Function

q = .50 74.1 q = .55 74.0 q =.60 74.0 q =.65 74.0 q = .70 74.0 q =.75 73.9 q = .80 74.1 q = .85 74.0

Range of Predictive Accuracy from q = .50 to q = .85 73.9-74.1 N 800

*p .05 (one-tailed test). p < .01 (one-tailed test).

**p < .001 (one-tailed test).

Richard Born

ing Fiorina's original analysis with the properly specified ideological distance utility function, I found no basis for rejecting his hypothesis of simultaneity in the electoral choice process.

What would result, though, if the same procedure were applied to the remaining elections in which the CPS/ANES asked respondents ideological placement questions concerning themselves and the par- ties (i.e., the 1972, 1976, 1980, and 1988 elections)? Two minor modi- fications are required to arrive at an answer. First, Fiorina in 1984 relied upon the branching format version of the liberalism/ conservatism scales, whereby voters who first select liberal or conser- vative to describe their ideology are then asked whether the relevant ideology is strong or not strong. Those who answer moderate or are unable to render a placement are prompted to say whether they see themselves or the parties as at least leaning toward liberal or conserva- tive. Presidential-year election scales based on this particular branch- ing format, however, exist only in 1984; thus, analysis in other years must use the standard one-step items soliciting placements along each 7-point scale. (To make comparison possible, the 1984 analysis will now rely on the standard 7-point scale as well.) Responses for all years must also be modified to conform to the simple approve/disapprove version of presidential job evaluation used in 1972 and 1976. Respon- dents had no opportunity to elaborate on the strength of their senti- ment until subsequent years.3

The forecasting power of the relevant binomial and condi- tional logit equations is exhibited in Table 3.4 The analysis hardly changes our earlier results for 1984. The predictive accuracies across differing q values produced by conditional logit (73.7-74.2%) vary lit- tle, as before, and fall close to what would be divined by the baseline binomial equations (74.3%). Support for Fiorina's hypothesis is also provided by the analogous 1976 results; here, the results of the condi- tional logit analysis (67.9-68.3%) actually surpass that for the bino- mial logit analysis (67.4%). The results for the 1972, 1980, and 1988 elections, however, do not square with such a pattern. Even the best prediction within the broader span of values obtaining in each year (68.2-70.3%, 65.3-66.3%, and 72.0-73.0%, respectively) is apprecia- bly worse than the corresponding baseline percentage (71.4%, 68.9%, and 74.7%, respectively).5 To put these differences in perspective, con- sider the predictive accuracies that would result were no ideological measure whatsoever included in the equations: 68% in 1972, 65% in 1980, and 72.8% in 1988. Thus, the major substantive step of entering ideology into the conditional logit equations produces only limited gains in forecasting voting behavior; in contrast, abandoning the con-

102

Split-Ticket Voters

ditional logit formulation of voting in favor of simpler binomial logit estimation improves predictive accuracy to a striking degree, espe- cially in 1980 and 1988. In summary, then, we must conclude that the main test for verifying the policy-balancing model fails in three of the five elections studied.

Appraising the Propositions Generated by Fiorina's Theory

Fiorina has explored some logical outgrowths of his model. The five discrete propositions that emerge, however, have not been subjected to more than passing attempts at confirmation. The first examined in detail here-and probably the most fundamental-is that voters perceiving their own ideology to fall between the two par- ties' should be more prone to split-ticket voting than should others (1989, 25). This proposition, of course, stems from the assumption that voters choose a given ballot combination as a kind of average of the ideological locations of the parties in a pair of contests for national office (see spatial diagram on p. 96). Voters between the DD-DR mid- point M, and the RD-RR midpoint M2 are predicted to split their bal- lots because they are nearer to the relevant divided government position than to that of unified government; similar reasoning argues that people more extreme than DD or RR vote a straight-party ticket.

But what of voters between DD and Ml or between RR and M2? While more moderate than either party, they still should vote a straight ticket because they are closer to one of the united party posi- tions than to a divided government position. In fact, very few voters of this sort are likely to exist as long as the presidential weighting factor q is > .5 and < 1.0. Consider interparty respondents perceiving four points of separation between Democrats and Republicans on the 7-point scale (e.g., those with a self-placement of 3, 4, or 5 who see the parties at 2 and 6). Because of the .5 < q < 1.0 assumption, the rele- vant divided government position DR or RD for the 3's or 5's must be less than one scale point away from them, while DD or RR is precisely one unit beyond. For those who place themselves at 4 on the scale, divided government will be less than two units distant, while unified government will be two full units removed. Analogously, all interparty voters seeing the parties separated by two or three points must be closer to the appropriate divided government position.6 Finally, when the parties are five or six units apart, only those with a position one point more centrist than that of a party will vote a straight-party ticket. In the former case (e.g., respondents who place themselves at 3 or 6 and place the parties at 2 and 7), straight-party voting is expected only

103

TABLE 3 Binomial Logit Analysis of Presidential and House Voting, 1972-88

(standard errors in parentheses)

1972 Voting Equations 1976 Voting Equations 1980 Voting Equations 1984 Voting Equations 1988 Voting Equations

President House President House President House President House President House

Constant

Evaluation of President's Job Performance

Approve Strongly

Approve Not Strongly

Disapprove Not Strongly

Party Identification Same as that of President

Independent

Ideological Distance

Incumbent in House Contest

N Percentage Correctly Predicted

by Each Equation Separately by the Two Equations in Tandem

-2.760*** -1.916"** -3.220*** -2.195*** -3.662*** -1.445*** -3.014*** -1.985*** -3.275*** -2.306*** (.466) (.355) (.309) (.272) (.472) (.276) (.379) (.349) (.358) (.382)

3.722*** 1.104*** 3.096*** 1.701*** 3.580*** .151 5.033*** 2.371"** 3.401*"* 2.030*** (.461) (.340) (.287) (.277) (.686) (.488) (.501) (.409) (.389) (.477)

2.783*** .022 3.084*** 2.067*** 2.329*** 1.577*** (.365) (.319) (.382) (.379) (.365) (.439) 1.052*** .210 .255 .867* 1.198** 1.390** (.364) (.292) (.471) (.422) (.430) (.492)

3.641** 2.162*** 2.162*** 2.505*** 2.837*** 2.063*** 2.037*** 1.142*** 2.979*** 1.746*** (.870) (.370) (.305) (.318) (.463) (.356) (.387) (.296) (.339) (.381) .865* 1.144*** 1.336*** 1.159*** 1.592*** 1.066** 1.332*** .627'** 1.282*** .983*

(.372) (.326) (.255) (.266) (.461) (.292) (.273) (.260) (.270) (.354) -.704*** -.199** -.388*** -.091 -.466*** -.220"* -.358*** -.222*" -.441** -.320*** (.118) (.077) (.065) (0.058) (.083) (.061) (.072) (.057) (.067) (.074)

.775*** 1.208*** 1.194*** 1.375*** 1.776*** (.154) (.138) (.136) (.131) (.173)

438 390 758 629 529 478 981 793 843 588

90.4 77.7 85.0 78.7 88.5 76.8 71.4 67.4 68.9

91.1 80.6 74.3

88.7 83.0 74.7

*p -< .05 (one-tailed test). *p < .01 (one-tailed test). ***p < .001 (one-tailed test).

Variables

-

sr

TABLE 3 (continued) Conditional Logit Analysis of Presidential and House Voting, 1972-88

Specification of Model 1972 1976 1980 1984 1988

q Computed with Fiorina's Specification of Utility Function .780 .841 .674 .575 .549 Percentage Correctly Predicted with Respecification of Utility Function

q = .50 68.5 67.9 66.0 74.1 72.7 q = .55 68.5 67.9 66.3 73.9 72.7 q = .60 68.5 68.0 65.8 73.8 72.0 q = .65 68.2 68.3 65.5 73.7 72.3 q= .70 69.3 68.0 65.3 73.9 72.5 q = .75 69.8 68.0 65.8 73.9 72.8 q = .80 70.1 68.2 65.8 74.1 72.7 q = .85 70.3 68.2 65.8 74.2 72.7

Range of Predictive Accuracy from q = .50 to q = .85 68.2-70.3 67.9-68.3 65.3-66.3 73.7-74.2 72.0-73.0 N 381 616 409 771 578

Note: The analyses in Table 3 use data only for respondents voting in the presidential and House elections in districts with two-party competi- tion. Ideological distance variables were constructed from standard 7-point liberal/conservative scales. In 1972 and 1976, "approve strongly" refers to a simple dichotomous item used in those years, differentiating only between those who said they approved of presidential job per- formance and those who said they disapproved. In 1988, the best prediction (73.0%) in the conditional logit analysis was obtained at q values of.51-.54 and .73-.74.

'I _. c_-

CD <:

Ct CD rA

Richard Born

if q < .6. In the latter situation, limited to respondents who place themselves at 2 or 6 and place the parties at 1 and 7, a straight-party choice will occur only if q < .67. A person's q value, of course, is unknowable. However, if one considers all voters who meet the two cri- teria of perceiving the Democrats and Republicans as at least five units apart and seeing themselves as one unit more moderate than a party, the maximum percentage who could vote for unified govern- ment according to Fiorina's model averages only 6.0% from 1972 to 1988 and never exceeds the 1984 figure of 8.5%.

Thus, this category of voters should pose no problem for the test about to be performed. The decision whether to cast a divided bal- lot is set forth as the dependent variable (1 represents a split-ticket vote) in a binomial logit analysis with the following right-hand side variables:

INTERPARTY = 1 if voter's ideology lies between those of the two parties, 0 otherwise;

AGE = voter's age in years; GOVTPOWER = 2 if voter believes government is getting too

powerful; 1 if "other," "depends," or "doesn't know"; 0 if government is not seen as getting too powerful;

PUBAFFAIRS = 4 if voter follows government and public affairs most of the time, 3 if some of the time, 2 if doesn't know, 1 if only now and then, 0 if fol- lows hardly at all;

CAREPRES = 2 if voter cares not very much about which party wins presidency, 1 if doesn't know, 0 if cares a good deal;

TRUST = voter's mean score across items comprising trust in government scale; each item calibrated so that 2 is the least trusting response and 0 the most trusting;

EXTEFFICACY = voter's mean score across items comprising external efficacy scale; each item calibrated so that 2 is the least efficacious response and 0 the most efficacious;7

OPPINC = 1 if member seeking reelection in voter's dis- trict is from party opposite that of voter's presi- dential choice, 0 otherwise;

NOINC = 1 if member does not seek reelection in voter's district, 0 otherwise.

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Split-Ticket Voters

Age is used because younger voters have had less chance to "act out" any partisan attachments in the form of straight-ticket voting (Parker 1988, 7, 9-10). Respondent feeling about government power and about the presidential election's importance, as well as scores on the trust and external efficacy scales, are entered to tap alienation toward the political system. Fiorina, relying upon tabular analysis, has found a propensity for the more alienated to divide their ballots more in 1980 (1989, 32-34). On the other hand, including interest in political affairs reflects Fiorina's concomitant observation that ticket splitting is somewhat more likely among those with greater political awareness.8 The final two variables control for incumbency by allowing, in effect, for the influence of the other independent variables to be registered within each group of districts marked by a distinct incumbency status.

Table 4 shows that respondents' own ideology relative to their images of where the parties stand affects ticket splitting in the hypoth- esized direction in four out of the five elections. Only in 1988, how- ever, is the relationship significant.9 Most of the other variables have weak or inconsistent effects. Plainly, many of the causes of divided- party voting remain to be discovered.10

The second proposition that grows out of Fiorina's model is based on the assumption that voters see the president as influencing policy more than Congress does. Split-ticket voters, as a result, should choose a presidential candidate whose party's position is closer to their own position than is the party position of the House candidate they choose (1988, 445). For the same reason, the dominant split-ticket pat- tern should involve a presidential vote for the party that is nearer to respondents in the aggregate (1989, 26; 1992, 78-79). Of course, the results of the binomial logit analysis presented in Tables 2 and 3 indi- rectly suggest that both of these expectations will be upheld; in that analysis, relative ideological distance from the parties-at least for the straight-ticket and split-ticket voters analyzed together there-proved to be a stronger determinant of presidential than of House voting. To test these new expectations explicitly, a logit analysis was performed on split-ticket voters only, with the pattern of divided voting as the dependent variable (1 for Democratic-Republican, 0 for Republican- Democratic) and respondent distance from the Republican party rela- tive to distance from the Democrats as the key independent variable (this measure has the same form as the ideological distance measure used in Tables 2 and 3). In addition, incumbency effects are controlled with a variable for an incumbent of the Republican party (REPINC) and the variable for open-seat elections (NOINC) described above. Table 5 reveals solid support for Fiorina's model: ticket-splitters' ideological

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Richard Born

TABLE 4 The Effects of Being between the Parties

Ideologically on Ticket Splitting (binomial logit parameters; standard errors in parentheses)

Variable 1972 1976 1980 1984 1988

Interparty Voter .076 .035 -.038 .098 .250* (.148) (.107) (.133) (.135) (.134)

Voter's Age -.004 .005* .006 -.003 -.004 (.004) (.003) (.004) (.004) (.004)

Voter Believes Government -.123 .001 .023 -.073 .104 Becoming Too Powerful (.079) (.062) (.086) (.090) (.085)

Voter Follows Public Affairs -.039 .056 .040 -.011 .045 (.067) (.054) (.053) (.058) (.058)

Voter Cares Which Party .119 .183*** .146* .184** .288*' Wins the Presidency (.074) (.055) (.070) (.077) (.077)

Voter Trusts Government -.098 -.152 -.057 -.121 -.302 (.162) (.130) (.161) (.155) (.156)

External Efficacy .065 -.039 .015 .090 .015 (.096) (.078) (.090) (.072) (.075)

District Incumbent from 1.301*** 1.137*** 1.214*** 1.277*** 2.094** Party Opposite Voter's (.176) (.128) (.157) (.164) (.242) Choice for President

No Incumbent Candidate .563* .379* .671" .570* 1.283** in District (.208) (.170) (.250) (.245) (.304)

Constant 4.104** 3.572*** 3.337*** 3.929*** 2.938** (.356) (.302) (.353) (.321) (.377)

N 383 627 405 392 580

Note: The analysis uses data only for respondents voting in both the presidential and the House election in districts with two-party competition. Only Form 2 respondents in 1972 and only personal interview respondents in 1984 are used.

p < .05 (one-tailed test). *p c .01 (one-tailed test).

***p < .001 (one-tailed test).

affinity with a party is significantly associated in every year with a greater chance of favoring the presidential candidate of that party." Furthermore, at the aggregate level of analysis, the results show that the Democratic-Republican mode of ticket splitting is more popular at the same time that voters are closer to the ideological position of the Republican party.

In yet another proposition emerging from the policy-balancing theory, ticket splitting is expected to increase when the parties are fur-

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TABLE 5 The Relationship between Ticket-Splitters' Relative Distance

from the Parties and Their Pattern of Divided Voting (binomial logit parameters; standard errors in parentheses)

Variable 1972 1976 1980 1984 1988

Relative Distance from .232** .214*** .213** .258*** .313* Republican Party (.064) (.061) (.073) (.071) (.140)

Republican Incumbent 1.488*** 1.602** 1.594** 1.693** 2.848*** Candidate in District (.257) (.234) (.308) (.269) (.496)

No Incumbent Candidate .858*** 1.555'** .446 .748* 1.540** in District (.264) (.377) (.434) (.342) (.575)

Constant 4.944*** 4.323*** 4.669*** 4.693*** 3.922*** (.161) (.157) (.194) (.194) (.317)

N 213 177 115 191 122

Percentage of Ticket Splitters Ideologically Closer to

Republican Party 41.9 43.8 47.0 45.6 50.2 Ideologically Closer to

Democratic Party 33.6 26.5 34.5 35.3 33.4 Ideologically Equidistant

from Both Parties 24.5 29.7 18.4 19.0 16.4 Voting Republican for

President and Democratic or House 79.9 62.3 66.3 76.4 73.0

Note: The analysis uses data only for respondents splitting their ballots in districts with two-party competition. Ticket-splitting percentages for 1976 have been weighted. p < .05 (one-tailed test). p .01 (one-tailed test).

***p .001 (one-tailed test).

ther apart in ideology (Fiorina 1988, 445; 1989, 25; 1992, 77, 82), since more voters would be situated in the interparty territory where ticket splitting is supposedly most common. Our tests have shown that such people have only a modestly higher propensity to split their votes, however. Table 6 reports the correlation between mean perceived interparty distance and the percentage of ticket-splitters. Since a lim- ited number of cases is available for testing chronologically based propositions derived from Fiorina's model, the result must be read cir- cumspectly. The r-value is substantially negative, however, contrary to expectations based on Fiorina's model.

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TABLE 6 The Relationship between Perceived Interparty and Ideological Distance and the Percentage of Voters Splitting Their Ballots

Percentage of Voters Splitting Distance between Mean

Ballots for President Perceived Ideologies of Republican Year and for House Candidate and Democratic Parties

1972 26.3 1.966 1976 26.1 2.170 1980 26.7 2.123 1984 23.8 2.176 1988 22.2 2.410

r = -.828

Note: Analysis based only on respondents voting for president and House in districts with two-party competition. 1976 results have been weighted. Percentage of ticket splitters = 46.916-10.095 * Interparty distance

Fiorina's fourth proposition is linked to the one just covered. If party polarization grows because only one party becomes more extreme, then straight-ticket ballots for this party should decline and straight-ticket ballots for the other party should proliferate (1988, 446-47). As one party moves farther from the center, it drags along with it both the M, and M2 midpoints (see the spatial diagram on p. 96). Consequently, the migrating party's straight ticket zone shrinks and the stable party's lengthens.

To perform a systematic analysis, we must amend Fiorina's proposition slightly and hypothesize that the party with the smaller relative drift from the center will have more growth in straight-party voting. Thus, the dependent variable is the quadrennial change in the percentage of voters voting for both Democratic candidates, minus the change in the percentage voting for both Republican candidates. The independent variable is the degree to which the Democratic position has moderated (i.e., become less liberal) over the four years, minus the extent to which the Republican position has moderated (i.e., become less conservative). Since positive values on the dependent variable mean that straight-party Democratic voting has risen more (or fallen less) than has straight-party Republican voting and positive values on the independent variable denote that the Democratic party has mode- rated more (or veered from the center less) than the Republican party has, a positive correlation is expected. However, the r-value in Table 7 is extremely negative; with only four cases, the relatively large and oppositely signed values of the two variables for 1972-76 and 1976-80

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TABLE 7 The Relationship between Relative Change in the Perceived Ideological Position of the

Democratic Party and Relative Change in the Percentage of Straight-Ticket Democratic Voters

Change in Percentage Voting Change in Perceived Position

Straight-Ticket Straight-Ticket Net Democratic Republican Net Period Democratic Republican Change Party Party Change

1972-76 7.5 -7.2 14.7 -.161 -.043 -.118 1976-80 -6.9 6.3 -13.2 .255 -.208 .463 1980-84 2.9 -0.1 3.0 -.026 -.027 .001 1984-88 6.4 -4.8 11.2 -.087 -.147 .060

r = -.933

Note: The analysis uses data only on respondents voting in both the presidential and the House election in districts with two-party competition. Results for straight-ticket voting in 1976 have been weighted. Positive change in a party's perceived ideological position indicates movement toward the center. The regression analysis uses the data on net change in straight-ticket voting and perceived party position:

Relative change in the percentage voting a straight Democratic ticket = 8.590-45.965 * Relative change in perceived position of Democratic party

C)

0 0)

rD

Richard Born

dominate the calculation. The 1972-76 case, it is important to note, is an excellent example of the particular ideological scenario envisioned by Fiorina. The Republican party position is virtually stationary at the same time that the Democrats-despite predictions made in the wake of the 1972 McGovern candidacy-come to be seen as even more lib- eral. Yet the net change in straight-ticket voting is more heavily Demo- cratic over this interval than over any other.

Our final test is based on Fiorina's argument that ticket- splitting will be influenced by perceptions of the relative power of the president and of Congress (1988, 447). As q-the weight assigned to the president's policy position-approaches 1.0, the DR and RD loca- tions move farther apart and become less distinct from DD and RR, respectively. M, (the point halfway between DD and DR) is pushed to the left, and M2 (the midpoint between RD and RR) to the right; thus, both split ticket zones expand. (See the spatial diagram on p. 96.) As previously underscored, of course, the q values computed from Fiorina's conditional logit technique are inaccurate. Let us assume, however, that these q's (reported in Table 3) are not too much at odds with reality and very briefly indicate here the relevant relationship involving them. From 1972 to 1988, r equals .803, a value consistent with the idea that ascribing more power to the president indeed pro- duces more ticket splitting.

Summary and Conclusions

Overall, the results of these tests of Fiorina's policy-balancing theory raise doubts about the theory as an explanation of ticket- splitting behavior. In 1980, when Democrats controlled both the exec- utive branch and Congress, voters saw less linkage between the federal government's issue positions and those of the president than they did in 1988, when there was divided control. These outcomes are not what one would expect if policy were, in fact, conceived to be an amalgam of the stands taken by the presidential and congressional parties. More important, the utility function for ideological distance was misspecified in Fiorina's test of the premise that the partisan voting combination seen as nearest to the voter's own ideology will be the one actually selected. Rectifying this error does not challenge his verdict that the public in 1984 may indeed have behaved this way. The correc- tion is crucial, however, when the elections Fiorina did not consider are studied as well; the best possible predictive values of the properly specified conditional logit equations fall short of the forecasts yielded by independent binomial equations in 1972, 1980, and 1988.

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Finally, of the five propositions Fiorina generated as logical extensions of the policy-balancing model, only two were supported in our tests: that ticket splitters are ideologically closer to the party of their presidential choice than that of their House choice and that divided voting will occur more often when the ratio of the president's perceived power to that of Congress is larger. The first result is not remarkable, in view of the oft-cited finding that ideological and national issue concerns are most relevant for voters in their presiden- tial balloting. The second result depends upon the supposition that the faulty q values derived from Fiorina's conditional logit procedure approximate the true policy weights attached to the president.

Why does voters' behavior not fit the policy-balancing model? At the core of the model is the thesis that voters see their choice of House candidate as tilting Congress somewhat in the policy direction they favor. But how realistic is this assumption if ideology and national issues sway House voting decisions only modestly? For example, using 1988 CPS/ANES data, Jacobson constructs eight variables touching upon respondents' evaluations of party ability to deal with issues or their affinity with party issue positions; he found that six significantly affected presidential selection but only one influenced the House vote (1 990b, Table 11). Instead, House elections hinge on more local mat- ters, such as opinions of the incumbents' solicitude toward district eco- nomic interests or the quality of their casework. In short, even if it were true that voters wanted the House to be controlled by a different party from that controlling the executive to balance the relative extremism of a president, nothing ensures that voters will dedicate their own con- gressional votes to furthering this end.

For citizens who do see an outlet for their ideological and national issue convictions in their House choice, however, one must ask why they would attempt to temper the policies of a president by casting a vote for a particular party in the congressional election rather than by weighing the positions of the two congressional candidates. Even if House nominees' views-especially those of nonincumbents- are unknown to many people, better-informed voters may achieve some degree of balance if there is meaningful divergence between the presidential and House candidates of the same party. Something akin to the old-style southern electoral behavior comes to mind here, in which support for a more conservative House Democrat could be seen as balancing the pro-civil rights impulses of such presidential candi- dates as Truman and Kennedy. Such a policy-moderating voter would thus be acting on a natural desire to back the more proximate House contender, rather than being forced counterintuitively, as Fiorina's

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model dictates, to vote for whatever House candidate happened to rep- resent the party farther removed from the voter's own political thinking.

Richard Born is Professor of Political Science, Vassar College, Poughkeepsie, New York 12601.

NOTES

1. Fiorina uses data from preelection surveys for his independent variables. With regard to partisanship, independents leaning toward a party are grouped with pure independents. Our replications of his analysis will proceed the same way. (In 1972, how- ever, ideological scales exist only in the postelection survey.)

2. Note that, when the voter's stance is identical to a party's position or when the voter is to the left or right of both parties, it does not matter which of the two func- tions is used to generate utility values for any of the four voting options.

3. For 1972 and 1976, furthermore, this study gauges the partisanship of respondents' House votes in the way suggested by Eubank (1985, 959-60): first, it uses the initial CPS/ANES variable combining the name of the candidate for whom the respondent reported voting with the correct (CPS-supplied) party of the candidate, and then, for vot- ers not able to remember the name of this candidate, it turns to the succeeding item asking the person to identify the party he or she supported in the House election. Eubank con- vincingly argues that this approach yields the most accurate measures of actual voting behavior. Reliance only on the former variable means excluding the many people unable to recall their candidate's name; reliance only on the latter would mean recording wrongly the votes of those who misconstrue their candidate's party. More recent surveys have sim- ply asked respondents to locate their choice on a list of candidate names.

4. Coding for variables in the 1980 equations has been adjusted from that used by Fiorina in the 1984 equations because, in this one election, the White House party is Democratic. Thus, with respect to the dependent variables, a value of one in the two binomial equations represents a Democratic vote, while the ordering of the condi- tional logit alternatives becomes DD, DR, RD, and RR. Similar changes have been made, wherever relevant, in constructing the independent variables.

5. Computing the predictive power of the 1972-84 equations at .05 intervals of q, as in Table 2, yielded maximum and minimum forecasting percentages that were not eclipsed by any of those calculated at points within intervals. In 1988, however, the best accuracy (73.0%) occurred at q values of .51 to .54 and .73 to .74.

6. Here, it is necessary only that q < 1.0. 7. Five items formed the additive scale for the TRUST variable in the equation:

Do people in government waste tax money (used in the surveys 1972-88); how often can the government be trusted to do what's right ( 1972-88); is the government run by a few big interests (1972-88); do almost all people in government know what they're doing ( 1972-80); are quite a few people running government crooked (1972-88)? Three items formed the scale for the EXTEFFICACY variable: do public officials care much what people think (1972-88); do congressmen lose touch with the people very quickly (1972-80); are parties only interested in people's votes but not their opinions (1972-80)?

8. Since the survey items used to develop the GOVTPOWER and TRUST variables were asked only of Form 2 respondents in 1972, and the GOVTPOWER item was asked only of those assigned to the personal interview sample in 1984, other respondents in these two years have been dropped from the analysis here. In all elections, "don't knows" on

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the items comprising GOVTPOWER, PUBAFFAIRS, CAREPRES, TRUST, and EXTEFFICACY are included so that the number of cases does not fall to unacceptably low levels. "Don't knows" on any ideological scale pertinent to the INTERPARTY variable, however, are removed because that scale is paramount to the proposition being tested.

9. When the ticket-splitting frequency of voters between and not between the parties is compared with no controls applied, interparty voters do split more in all years, but the difference is significant at least at the .05 level only in 1984 and 1988.

10. Strength of partisanship-a likely predictor of ticket splitting and of being between the parties ideologically-was left out of the equations in order to maxi- mize the odds that any relationship between the INTERPARTY variable and the dependent variable would be manifested. Its inclusion (2 if voter is independent leaner or pure independent, 1 if weak partisan, 0 if strong partisan) changes the results only marginally; the INTERPARTY parameters are weakened a bit over all, but the variable continues to have a significant effect in 1988.

1 1. In an analysis comparing respondent distance from each party within sep- arate groups of Republican-Democratic and Democratic-Republican voters, all 10 com- parisons were found to be significant in the hypothesized direction at least at the .01 level.

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