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Balancing in the States, 1978-2009

Michael A. Bailey

Department of Government &

Public Policy Institute

Georgetown University

Intercultural Center 681

Washington, DC 20057

(202) 687-6021

[email protected]

Elliott B. Fullmer

Department of Government

Georgetown University

Intercultural Center 681

Washington, DC 20057

(202) 687-6130

[email protected]

AUTHOR INFORMATION:

Michael A. Bailey is the Colonel William J. Walsh Professor of American Government in the

Georgetown University Department of Government and the Georgetown Institute of Public

Policy. His research covers Congress and the Supreme Court, methodology, welfare, trade,

and inter-state policy competition.

Elliott B. Fullmer is completing his Ph.D. in American Government at Georgetown University.

His research interests include state politics, voting systems, congressional behavior, and the

presidential nomination process. His dissertation examines the effects of early and absentee

voting laws in the U.S.

mailto:[email protected]:[email protected]

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Abstract

Since the Civil War, the presidents party has lost seats in the House of Representatives in all but

three midterm election cycles. Many attribute this pattern to balancing by moderate voters who

prefer a Democratic Congress when Republicans control the White House, and vice versa

(Fiorina 1988, 1992, 1996; Alesina and Rosenthal 1995). Although a number of scholars have

tested the balancing hypothesis, the debate remains unsettled. We argue that states provide an

excellent way to further analyze the issue. Similar to the national government, states feature an

executive and (in most cases) a bicameral legislature which comes up for election in both

gubernatorial and state midterm years. If voters balance, we ought to observe such behavior in

state elections when an executives partisanship is known and a legislative choice is necessary.

We examine state legislative elections from 1978-2009 and find evidence consistent with the

balancing hypothesis.

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One of the clearest patterns in U.S. politics is that the presidents party fares poorly in

midterm elections. In fact, since the Civil War the president has lost seats in all but three

midterm cycles (1934, 1998, and 2002). Some scholars explain this phenomenon in terms of

balancing, the idea that voters balance a president of one party by voting for members of

Congress from the other party. In this view, voters expect that a Congress controlled by the

opposite party of the president will moderate policies that emerge from the legislative process.

For all the plausibility of the balancing hypothesis, however, doubts remain. The presidents

party has picked up seats in two of the last three midterms. In addition, research on the issue has

yielded differing conclusions (Beck et. al 1992; Sigelman, Wahlbeck, and Buell 1997; Burden

and Kimball 1998; Roscoe 2003; Lewis-Beck and Nadeau 2004).

In this paper, we explore the issue in a new way. The balancing hypothesis is not specifically

about Congress or the presidency, but rather about voters and the interaction of their choices in

legislative and executive branch elections. Therefore, we examine state elections in order to

vastly increase the sample size of cases in which voters have a chance to balance. We focus on

state legislative elections during gubernatorial midterm years, as the balancing hypothesis

suggests that voters should reward the party opposite of that of the sitting governor in such cases.

We assess legislative outcomes in each chamber of U.S. states from 1978 to 2009 and find

evidence that voters balance. In most specifications, we find that Democrats in a state legislature

are at a disadvantage in midterm elections when there is an incumbent Democratic governor and

vice versa. The balancing effect is typically around two or three percent of a chamber, but in

some reasonable specifications is as large as a thirteen or fourteen percentage point difference in

the proportion of a legislature that is Democratic.

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Balancing: the evidence so far

The balancing hypothesis was first introduced and tested as an explanation for split-ticket

voting (supporting one party for president and another for Congress) during presidential election

cycles. Fiorina (1988, 1992, and 1996) argued that many moderates engage in split-ticket voting

in presidential election years out of a desire to have divided government. Slightly right-of-center

voters who are inclined to support Republican presidential candidates may prefer a Democratic

House member because a divided government headed by a Republican chief-executive will

appear to represent them better than a unified Republican one. Conversely, slightly left-of-center

voters who are inclined to support Democratic presidential candidates may prefer a Republican

House member because a divided government headed by a Democratic chief-executive will

appear to represent them better than a unified Democratic one.

Analyzing the 1984 and 1988 presidential elections, Fiorina found some support for his

hypothesis and argues that while most voters do not consciously balance, a preference for

divided government influences the way a small segment of voters perceive candidates and

ultimately make voting decisions.

Additional evidence on this question has come in one of three forms: studies of ticket

splitting, survey experiments and analysis of national election results. The most expansive

literature addresses ticket splitting. This literature typically assesses the degree to which self-

reported ideology and perceptions of the ideological positions of the two major parties relate to

split-ticket voting. Those who perceive the parties as highly polarized and themselves as fairly

moderate, the argument goes, should be more supportive of divided government and more likely

to split their presidential and congressional tickets on Election Day. While some have found

support for this idea through survey research (Smith et. al 1999; Lewis-Beck and Nadeau 2004;

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Saunders et. al 2005), most have reported little or no evidence of such behavior (Beck et. al

1992; McAllister and Darcy 1992; Alvarez and Schousen 1993; Born 1994, 2000; Petrocik and

Doherty 1996; Sigelman, Wahlbeck, and Buell 1997; Lacy 1998; Burden and Kimball 1998;

Roscoe 2003).

There have also been a number of survey analyses that focus more directly on balancing.

Here again, we see no consensus as to whether voters balance. On the one hand are several

papers that support the idea. Carsey and Layman (2004), for example, argue that the

aforementioned ticket splitting findings should not represent the death knell for the balancing

hypothesis. It is quite possible in their view that voters may possess balancing preferences and

still engage in straight-ticket, rather than split-ticket, voting. In 1996, for example, most voters

arguably knew that incumbent Bill Clinton was likely to be reelected. Interested in balancing the

government, slightly right-of-center voters would be expected to vote for a Republican member

of Congress. Knowing that Clinton would win, however, they could also vote their conscience

for president by casting their respective ballots for Dole. Conversely, some may engage in either

split or straight-ticket voting for reasons other than balancing.

Carsey and Layman therefore focus simply on whether voters ideological locations and their

perceptions of the two parties influence voter attitudes regarding partisan or divided control of

government. The authors conduct a telephone survey of Illinois adults on the eve of the 2000

presidential election. In the survey, they ask If [George W.] Bush is elected president, which

type of Congress would you prefer? They later ask the same question, substituting Bush for

Vice-President Al Gore, the Democratic nominee. Carsey and Layman find that 17% of the

sample supports a divided government under all conditions. These respondents prefer a

Democratic Congress if Bush were elected, but a Republican one if Gore won.

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Lacy and Paolino (1998) reach a similar conclusion in their analysis. Echoing Downs (1957),

they find that presidential vote choices depend more on the perceived distance between voter

ideal points and expected policy outcomes under each candidate than on the distance between

voter ideal points and candidate platforms. The authors analyze a 1996 Texas poll in which

voters were asked to identify the positions of Bill Clinton and Bob Dole, along with the overall

ideological position of the government if either was elected. While many respondents found

Clinton to be left-of-center, a sizable number perceived that the government (given their

expectation that Republicans would control Congress) would be decidedly more moderate under

him than Dole. This expectation was found to aid Clintons support level with many voters closer

to Dole on the ideological spectrum.

On the other hand, several papers find mixed or no survey evidence for balancing. Saunders

et. al (2005) test the degree to which balancing is more prevalent in years featuring close, or

unpredictable, presidential elections and find mixed evidence for balancing only in 1996. Geer et.

al (2004) expose a sample of undergraduates to varying polling data concerning the upcoming

congressional and presidential races on the eve of the 2000 presidential election. This allowed

them to manipulate expectations concerning the victors of the looming elections. The authors did

not find those who believed Democrats would control the White House to be more eager to elect

a Republican Congress, or vice versa.

The third approach to assessing the balancing hypothesis is to look at election outcomes.

Alesina and Rosenthal (1995) argue that balancing is more likely to occur during midterms, as

voters no longer face uncertainty regarding control of the executive branch. In presidential

election years, voters are (at least somewhat) uncertain who will control the presidency and

Congress, forcing moderate voters and leaners to hedge their bets by supporting the party

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closest to their preferences in both races. In midterm elections, this uncertainty disappears, and

voters can act to balance the federal government.

This, Alesina and Rosenthal predict, should lead to losses for the presidents party in

midterm cycles. They study district-level election results in midterm elections from 1950 to 1986

(excluding years just after redistricting) and find that

With a Democratic president at midterm, Republicans record a higher two-party vote

share than they did in the previous on-year election (when the presidency was

uncertain) in a large majority of congressional districts.

With a Republican president at midterm, Democrats record a higher two-party vote

share than they did in the previous on-year election (when the presidency was

uncertain) in a large majority of congressional districts.

The authors also provide an additional analysis in which they control for incumbency effects.

They find that voters balance in two primary ways: 1) voters send the presidents party a signal

that the electorate wishes to moderate policies by diminishing the vote shares of some

incumbents and 2) voters defeat the presidents party in marginal open seat races, or those seats

in which a party won with less than 55% of the vote share in the previous contest.

The very plausibility of these results we know, of course, that the presidents party tends to

lose during midterms elections is also one of the ideas limitations. The hypothesis was derived

in part based on the observation of midterm losses by the presidents party. How can we be sure

that balancing is not simply an ex-post rationalization of a well-known behavioral pattern?

Balancing: a state-level analysis

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States provide an ideal opportunity for assessing the balancing hypothesis as they feature

voters making choices in separate elections for legislative and executive branch officeholders.

Examining balancing in the states therefore provides a new venue for testing the hypothesis a

venue that was not the source of the idea. In addition, states provide dramatically more

observations and variation on the circumstances of interest. At the federal level, for any given

election, the balancing hypothesis predicts movement in the same direction (away from the

presidents party in a midterm); at the state level, we can have numerous states with Democratic

incumbents (where we would expect balancing to advantage Republicans in the legislature),

other states with Republican incumbents (where Democrats would be advantaged in the

legislature) and states with no incumbent (where neither party should have an advantage

legislatively). This provides a much richer environment for testing the idea.

Model

With panel data, there are a number of different specifications that have been used in the

literature. In particular, the literature has models with and without lagged values of dependent

variables, with and without differencing of the dependent variable and with and without fixed

effects. Our approach is to estimate models across the range of specification approaches;

fortunately, the effects are generally consistent across approaches.

One question in the literature is whether or not we should include a lagged dependent

variable in the model. Oppenheimer, Stimson and Waterman (1986) argue that a lagged

dependent variable is very important to include in a model of one partys legislative results, as

this variable captures the exposure of the party. When the party has more seats than usual, it

likely includes a number of marginal seats that it is more likely to lose in the future.

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On the other hand, Achen (2001) counsels caution when considering inclusion of lagged

dependent variables. He derives the asymptotic bias of coefficients on the lagged dependent

variable and other variables and shows that there can be considerable bias when there is serial

autocorrelation and trending in the independent variable. In this case, the lagged dependent

variable does not conduct itself like a decent, well-behaved proxy. Instead it is a kleptomaniac,

picking up the effect, not only of excluded variables, but also of the included variables if they are

sufficiently trended. As a result, the impact of the included substantive variables is reduced,

sometimes to insignificance (Achen 2001).

Our approach on this question is to report both types of models. In the models without the

lagged dependent variable, we control for first-order autocorrelation. As we later discuss,

including a lagged dependent variable seldom affects the results, but it does occasionally reduce

both statistical significance and the magnitude of coefficients.

A second question is whether or not to difference the data. Differenced data in a panel

context is the results for a given year and state minus the previous results in that state. The

alternative is a level model in which the dependent variable is simply the result for a given

year. These models are absolutely equivalent when a lagged dependent variable is included

(Allison 1990), so there is no need to concern ourselves with this question for those

specifications. When the lagged dependent variable is not included, both differenced and level

models are unbiased and consistent under standard assumptions; the models do differ in their

robustness to certain assumption violations and we follow Allisons (1990) preference for

differenced models (see also Wooldridge 2009).

Another specification issue is whether or not to include fixed effects. We generally believe

inclusion of these controls is prudent as they capture two classes of unmeasured factors that

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could be correlated with the included variables and cause bias. First, we include year fixed

effects; these effects control for all factors common to a given year across all states, including

the national economy, national scandals or simply national political moods that may make some

years better or worse for Democrats. Second, we include state-level fixed effects. These account

for any influence on legislative outcomes that is fixed for a state across our time frame. For

example, the political culture of a state may make it consistently more Democratic than one

would predict based only on our measured variables. However, some argue that fixed effects

have some kleptomaniac tendencies as well (Beck and Katz 2001). Hence, we also report results

without fixed effects in order to provide a sense of how dependent our results are on the use of

these models.

In our study, we use data on the partisan composition of state legislatures before and after

elections since 1978 (The Book of the States 1978-2009; Klarner 2009). Our core model is

stst4

stst3stst2stst10st

X

)NotGovMTx (DemGov)GovMTx (RepGov)GovMTx (DemGovDemLeg

where DemLeg is the percentage point change in the percent of a legislative chamber that is

Democratic, DemGov is a dummy variable for Democratic incumbent governors, GovMT is a

dummy variable for a gubernatorial midterm election, RepGov is a dummy variable for

Republican incumbent governors and X is a vector of control variables, described below. The

excluded category in our formulation is a non-gubernatorial midterm with a Republican

incumbent. We omit elections which take place while an independent is serving as governor.

This is not a large enough number to merit another set of variables, and there is no obvious

reason to include independents with one party or the other.

The balancing hypothesis predicts that Democrats in legislatures do worse when there is a

sitting Democratic governor (1< 0 ) and better when there is a sitting Republican governor (2 >

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0). Or, most precisely, we predict that 2> 1. This means that we test for balancing not by

interpreting the coefficients themselves, but rather their differences. In our tables reported below,

the test of balancing will be an F-test reported at the bottom of each table. Following Alesina

and Rosenthal (1995), we focus on state midterm races because voters have certainty regarding

partisan control of the executive branch.

We include several control variables in order to account for other influences on state

legislative composition. First, we account for possible surge and decline trends in the

electorate. This idea, advocated by Campbell and others, contends that the electorate differs

between presidential and midterm election cycles (A. Campbell 1966; J. E. Campbell 1985, 1987,

1991, 1997; Born 1990). Presidential cycles are high-stimulus affairs and feature high levels of

campaign activity and voter turnout. Conversely, national midterm cycles involve low-stimulus

elections, and thus produce less interest and turnout (Campbell 1997).

In midterm years, the electorate is comprised of core voters, or those highly interested in

politics. In presidential years, however, these core voters are joined by peripheral voters, who

are stimulated by the perceived importance and media attention inherent in presidential races.

While core voters are likely to have stable partisan feelings and voting patterns, peripheral voters

are often guided by short-term forces in particular campaigns. In presidential election years, the

victorious candidate is elected largely with the help of peripheral voters, who in the process help

members of his party perform well in congressional races. Two years later, in midterm cycles,

these peripheral voters are absent and the core voters determine election outcomes. This

inevitably brings losses for the presidents party, as it is no longer helped by the tide of

peripheral voters that supported it in the previous election (Campbell 1997).

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If surge and decline indeed affects elections, we should expect it to impact state legislative

races which are held concurrently with national races as well. In presidential election years, we

expect to see a surge when the Democratic candidate does well: the in-state performance of the

presidential candidate should positively affect the performance of state Democratic legislative

candidates. In non-presidential election years in which the previous state legislative election was

in a presidential year, the success of a Democratic presidential candidate in the previous election

should hurt the party in the current election. Therefore, we include the Democratic share of the

two-party presidential vote at the state level for the current year in presidential election years. In

non-presidential years in which the previous state legislative election occurred during a

presidential election, we include the Democratic share of the two-party presidential vote in the

previous election.

We control for state economic conditions with a measure of change in state unemployment.

We use unemployment because it is highly politically salient and data on it is available for the

entire span of years we cover (from the Bureau of Labor Statistics). We created two variables

unemployment change x Democratic governor and unemployment change x Republican

governor so that unemployment increases would be allowed to have different effects depending

on which party controlled the governorship. We prefer using change variables as we feel they

capture the mood of the electorate; for example, while the level of unemployment was higher in

1984 than in 2008, the trend was better in 1984 and we believe the politics of the times

responded accordingly. Using levels of unemployment instead of changes, however, has little

impact on the results.

After we present a number of specifications based on the core model, we also report

specifications with additional variables. The limitation of these variables is that they are not

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available for the entire time period of our analysis. One factor that could be very important is that

not all governors are created equal. Some are very popular; some are unmitigated disasters.

Hence, the extent to which voters feel the need to balance may depend on the popularity of the

governor. Certainly the evidence is strong that executive popularity influences the executives

partys performance in legislative elections, both at the federal (Arcelus and Meltzer 1975;

Bloom and Price 1975; Tufte 1975; Kernell 1977; Fiorina 1981; Kiewiet 1983; Abramowitz

1985) and state-level (King 2001). We therefore include a measure of gubernatorial approval in

the weeks preceding an election (Beyle et. al 2009). Because this polling data is not universally

available, there is a considerable drop off in sample size when we include these data, causing a

concomitant drop in statistical power.

Changes in state partisanship and ideology could also explain legislative elections. We

already have a number of controls that are related to such changes, but we also report results in

which we include changes in state net Democratic identifiers (Democrats minus Republicans)

and state net liberals (liberals minus conservatives) using data from Wright, McIver and Erikson

(2004). This data is based on an aggregation of CBS News/New York Times national polls to

create large state-level samples. The data is only available through 2003, again causing a

significant drop off in sample size when included in the model.

Finally, redistricting could conceivably affect legislative outcomes and be correlated with our

main variable of interest, gubernatorial control. We therefore use data from Michael McDonald

on partisan control of the redistricting process (see Bailey and McDonald 2006). There are two

variables one is an indicator for when Democrats controlled the most recent redistricting

process; the other is an indicator for when Republicans controlled the most recent redistricting

process. Often these are both zero as neither party had complete control over the process. This

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data is not available for all years, again causing a significant drop off in sample size when these

variables are included.

Results

Table 1 presents the results for the main model for state assemblies from 1978 to 2009.

Column 1 presents an extremely sparse model; the idea here is to see the relationship in its

rawest form, relatively free of modeling assumptions. The F-test lines at the bottom indicate that

the Democratic share of state assemblies is 2.6 percentage points lower when there is a midterm

and a Democratic governor compared to when there is a midterm and a Republican governor.

This difference is highly statistically significant (p = 0.001).

Of course, we wish to know if this result is robust to inclusion of controls for other

determinants of state assembly elections. The next columns add increasingly more

comprehensive controls. Column 2 adds variables for state unemployment, state-level

presidential results and state and year fixed effects; the balancing effect is smaller, but the results

are generally similar to column 1. Column 3 controls for first order autocorrelation. Some data

are lost in the process, but the results do not change appreciably. Adding exposure in column 4

lowers the effect size to 1.4 percentage points and it remains statistically significant.

Table 2 presents results for the same specifications for state senates. The results are quite

similar as for state assemblies. The sparse model in column 1 indicates that the difference

between Democratic and Republican governors in gubernatorial midterms is about 2.4

percentage points and this difference is highly statistically significant (p = 0.002). Adding

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independent variables and fixed effects in column 2 moderates the results a bit, but they are still

significant. Correcting for autocorrelation in column 3 maintains an effect of 2.2 percentage

points, despite a drop in sample size. As with the assembly results, the effect drops when the

exposure variable is added in column 4, but it remains statistically significant.

The results so far support the idea that voters balance, albeit somewhat modestly as the effect

size is somewhere in the neighborhood of one to three percentage points. Translating this into

terms of the U.S. House of Representatives, this would mean that a Democratic president would,

all else equal, expect to lose between 4 and 13 House seats in a midterm. Applying the results to

the context of an average-sized state assembly (about 110 seats), a Democratic governor would

lose 1 to 3 seats. The results are statistically significant, but not breathtaking.

We now wish to see if the results are robust to alternative specifications and if there are

conditions under which balancing effects seem to be larger or smaller. In Table 3 we add

additional controls for state assemblies. Columns 1 and 2 report results when gubernatorial

approval ratings are included. The magnitude of the effect in column 1 (4.7 percentage points) is

on the high end of anything we have seen. This effect is significant, as well. While the sample

changes with the inclusion of the governor approval data, it appears that the changes in results

are due to including the approval data; running the model on the 384 observations for which we

have governor approval data, but not including the approval data (a model similar to column 3 of

Table 1), yields a balancing effect of about 2 percentage points, nearly half of what we see in

column 1. Column 2 adds a lagged dependent variable and the balancing effect is no longer

significant. Although the results are often similar with and without a lagged dependent variable,

when they do differ, they differ in this way: the balancing effect is smaller and sometimes

insignificant when the lagged dependent variable is included.

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In columns 3 and 4, we report results in which we include state-level partisanship and

ideology variables. This sample has cut off all years after 2003 due to data availability, but it

does not seem to change the results from what we saw in Table 1. The partisanship and ideology

variables are not very significant, implying they do not explain legislative results over and above

the other controls already in the model.

Columns 5 and 6 of Table 3 report models in which redistricting variables are included. The

redistricting variables are available for only a subset of observations used in Table 1, but even

with that reduction in sample size, we observe similar results as before. The balancing effect is

between 2.5 and 3 percentage points and significant.

In Table 4 we conduct similar exercises for state senates. Columns 1 and 2 report results

when gubernatorial approval ratings are included. The pattern is quite similar to what happened

with state assemblies: the balancing effect is quite large in column 1 -- 5.9 percentage points

and statistically significant. Again, it appears the gubernatorial variables are doing the work here,

not the change in sample: running the model on data for which we have gubernatorial approval

data but not including it yields a balancing effect of 1.5 percentage points, something similar to

what we saw in Table 2. The balancing effect is relatively large in column 2, which adds a

lagged a dependent variable, but is statistically insignificant. This is the same pattern we saw for

state assemblies.

Columns 3 and 4 report results in which we include state-level partisanship and ideology

variables. The results are consistent with previous analyses: the balancing effect is in the 2 to 3

percentage point range and statistically significant. The partisanship and ideology variables are

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insignificant, again implying they do not explain legislative results over and above the other

controls already in the model.

Columns 5 and 6 of Table 4 report models in which redistricting variables are included.

Including these variables drops the sample size considerably. Column 5 reports a balancing

effect of 2 percentage points, but it is not significant at conventional levels (p= 0.116); losing

about half the data seems the most reasonable explanation here as the results are similar if we run

the model on this subset of the data and do not include the redistricting data. In column 6, we

include a lagged dependent variable and the balancing result completely disappears.

Finally, we explore conditions under which balancing may be stronger or weaker. One

possibility is that balancing at the state level may be stronger in non-presidential years as the

intensity of a presidential election may so occupy voters that they pay less attention to potential

benefits of balancing. Therefore, Table 5 presents results where the sample is limited to non-

presidential years. The first three columns present results for assemblies. The results are indeed

stronger. Column 1 presents a model without lagged dependent variables (it is equivalent to

column 3 of Table 1). The balancing effect is 7.2 percentage points (compared to 1.7 percentage

points when both presidential and non-presidential years are included). Given the strong effect

gubernatorial approval had on results earlier, we add approval data to the model in Column 2; the

balancing effect shoots up to 13 percentage points. Column 3 includes a lagged dependent

variable (making is comparable to column 4 of Table 1) and the effect falls, as it has generally

done when adding lagged dependent variables, but the effect remains very large (9.4 percentage

points) and is highly significant.

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We run the same specifications for state senates in columns 4 through 6, running a series of

models based only on non-presidential election years. The balancing effect of 3.5 percentage

points in column 4 is large (but not as large as for assemblies) and statistically significant. The

effect skyrockets to 13.9 percentage points in column 5 and is highly significant. The effect falls

back to earth a bit in column 6, reaching 6.4 percentage points, but is nonetheless on the high end

of our findings. The results are not statistically significant, but some consideration must be made

for the fact that we have less than 1/3 of the data as in our original state senate models from

Table 2.

Another possible wrinkle in the estimation is that the South could somehow be distinctive. In

particular, the realignment pattern of the region over the last 30 years could have led to different

patterns of balancing. Therefore, we run models excluding the eleven former Confederate states.

Our results, however, are largely the same. For example, re-running the specifications in Table 1

without the South leads to an estimated balancing effect of between 1.6 and 2.5 percentage

points, with p-values ranging from 0.008 to 0.032. These are highly significant even though the

sample size is cut considerably. Re-running the tests in Table 2 without the South leads to an

estimated effect of between 2.0 and 2.5 percentage points, with p-values spanning from 0.005 to

0.027. Again, these are highly significant despite the reduced number of cases.

Conclusions

The debate over balancing remains unsettled after nearly two decades of attention. The ticket

splitting literature is divided, although there is perhaps more weight against balancing. The

survey-based tests of balancing are also divided, though scholars are more likely to find evidence

consistent with balancing.

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We seek in this work to provide new evidence on the question. Our work is premised on the

notion that the balancing hypothesis is essentially about voters and therefore it makes sense to

look at voting behavior at the state level to see if there are signs that voters act in the manner

predicted. In conducting our tests, we were able to call upon the relative abundance of state data

over thirty years.

How should one interpret our findings? Balancing skeptics could point to some insignificant

results and a number of other results in which the effect is around one percent. Hence even if

there is balancing, one percent (equivalent to four seats in the U.S. House or one seat in the U.S.

Senate) is not going to dominate election outcomes. On the other hand, balancing proponents

may reply that the balancing hypothesis survived even in the face of demanding models that

included fixed effects and lagged dependent variables, elements that are often suspected of

soaking up the effects of meaningful independent variables. In addition, in some quite reasonable

models the balancing effect is large, often near or above three percent and as high as 14 percent.

A three-percent balancing effect is equivalent to 13 U.S. House seats and four U.S. Senate seats,

or (in an average-sized U.S. state legislature) three assembly seats and one senate seat. A 14-

percent effect equates to 61 U.S. House seats and 14 U.S. Senate seats, or 15 state assembly seats

and six state senate seats. In Pennsylvania, a larger state, the effect would be 28 assembly seats

and seven senate seats.

We come down somewhere in the middle. We believe that the evidence suggests balancing

effects at the state level are real. The results are significant across a broad array of specifications,

including many which are quite demanding of a relatively small data set. And there seem to be

two factors that make the results particularly strong. First, balancing effects are clearer during

presidential midterm elections when the intensity of national presidential elections does not

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dominate the political environment. Second, balancing effects are clearer when we account for

the popularity of the governor, as popular governors mitigate balancing and unpopular governors

intensify it.

This last point also opens interesting directions for future research on balancing. Suppose our

conclusion is correct and voters do indeed balance. What should rational politicians do? One

possibility is that incumbents would temper their policy goals in the run-up to a midterm

election, especially if legislative control of a chamber is in question. To the extent that moderate

voters respond to such tempering of policy, this could have several interesting implications. First,

it could cause the observed effects to be smaller; for example, Democrats may moderate in

anticipation of balancing and thereby do better than they would have done with a more

aggressive agenda. Second, such anticipatory policy behavior could in and of itself be an

interesting and important topic for further analysis.

Finally, the micro-level determinants of voter balancing could be further probed. Who is it

that balances? Is it sophisticated voters making rational calculations? Or is it unsophisticated

voters going with the mood of the times? Our results here imply that this line of research merits

continued attention.

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References

Abramowitz, Alan I. 1985. Economic Conditions, Presidential Popularity, and Voting

Behavior in Midterm Congressional Elections. Journal of Politics 47 (February): 31-43.

Achen, Chris. 2001. Why Lagged Dependent Variables Can Suppress Explanatory Power of

Other Independent Variables. Presented at American Political Science Association.

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26

Table 1: Predicting Change in Democratic Proportion of State Assemblies

(1) (2) (3) (4)

Balancing

Dem. gov. x Gov. midterm -0.016* -0.003 0.001 -0.001

(2.18) (0.44) (0.07) (0.21)

Rep. gov. x Gov. midterm 0.01 0.016* 0.018* 0.013+

(1.31) (2.19) (2.51) (1.88)

Dem. gov. x Not gov. midterm -0.004 0.005 0.005 0.006

(0.58) (0.72) (0.76) (0.99)

Surge and Decline

Lagged Dem presidential vote share 0.034 0.014 0.202**

(0.58) (0.22) (3.40)

Current Dem presidential vote share 0.105+ 0.094 0.270**

(1.77) (1.53) (4.55)

Economic

Unemployment change x Dem. gov. -0.005 -0.007 -0.007

(0.23) (0.30) (0.37)

Unemployment change x Rep. gov. -0.029 -0.028 -0.031

(1.12) (1.05) (1.25)

Other

Pre-election seat share -0.265**

(8.53)

Constant -0.003 0.025 -0.047 0.067

(0.68) (0.37) (1.52) (1.04)

Year fixed effects No Yes Yes Yes

State fixed effects No Yes Yes Yes

AR1 model No No Yes No

N 740 740 691 740

R2

0.02 0.32 0.31 0.39

Rep gov midterm - Dem gov midterm 0.026 0.019 0.017 0.014

F-test: Rep gov midterm = Dem gov midterm 11.4 7.4 6.0 4.7

(p=0.001) (p=0.007) (p=0.014) (p=0.031)

Note: Dependent variable is change in Democratic proportion of state assembly

t-statistics are in parentheses

+ p 0.10; * p 0.05; ** p 0.01 (all two-tailed)

27

Table 2: Predicting Change in Democratic Proportion of State Senates

(1) (2) (3) (4)

Balancing

Dem. gov. x Gov. midterm -0.023** -0.015+ -0.017+ -0.012

(3.02) (1.70) (1.95) (1.56)

Rep. gov. x Gov. midterm 0.001 0.005 0.005 0.001

(0.12) (0.64) (0.57) (0.10)

Dem. gov. x Not gov. midterm -0.013+ -0.003 -0.006 -0.001

(1.77) (0.40) (0.83) (0.15)

Surge and Decline

Lagged Dem presidential vote share 0.005 0.008 0.05

(0.14) (0.22) (1.64)

Current Dem presidential vote share 0.093* 0.103* 0.169**

(1.98) (2.07) (3.79)

Economic

Unemployment change x Dem. gov. -0.016 -0.016 -0.013

(0.54) (0.50) (0.47)

Unemployment change x Rep. gov. -0.02 -0.018 -0.01

(0.65) (0.57) (0.34)

Other

Pre-election seat share -0.274**

(9.78)

Constant 0.001 0.095 -0.034 0.165*

(0.24) (1.12) (0.41) (2.10)

Year fixed effects No Yes Yes Yes

State fixed effects No Yes Yes Yes

AR1 model No No Yes No

N 689 689 640 689

R2

0.02 0.18 0.18 0.29

Rep gov midterm - Dem gov midterm 0.024 0.020 0.022 0.013

F-test: Rep gov midterm = Dem gov midterm 9.4 6.2 7.0 3.1

(p=0.002) (p=0.013) (p=0.008) (p=0.079)

Note: Dependent variable is change in Democratic proportion of state senate.



28

Table 3: Predicting Change in Democratic Proportion of State Assemblies

(1) (2) (3) (4) (5) (6)

Balancing

Dem. gov. x Gov. midterm -0.035 -0.016 0.004 0.001 0.000 -0.006

(1.58) (0.85) (0.55) (0.14) (0.02) (0.58)

Rep. gov. x Gov. midterm 0.012 0.011 0.019* 0.015* 0.030** 0.019+

(1.19) (1.19) (2.52) (2.20) (2.95) (1.74)

Dem. gov. x Not gov. midterm -0.022 -0.006 0.004 0.005 0.009 0.01

(1.01) (0.33) (0.52) (0.80) (0.84) (1.06)

Surge and Decline

Lagged Dem presidential vote share -0.051 0.017 -0.053 0.185** 0.053 0.264**

(0.46) (0.18) (0.79) (3.12) (0.55) (3.11)

Current Dem presidential vote share -0.024 0.101 0.042 0.247** 0.129 0.314**

(0.23) (1.07) (0.61) (3.94) (1.26) (3.52)

Economic

Unemployment change x Dem. gov. -0.017 -0.029 -0.053+ -0.027 -0.017* -0.02

(1.22) (0.77) (1.78) (1.03) (2.12) (0.74)

Unemployment change x Rep. gov. -0.032 -0.029 -0.050+ -0.042 -0.024 -0.025

(0.85) (0.80) (1.73) (1.64) (0.64) (0.68)

Other

Pre-election seat share -0.181** -0.374** -0.268**

(3.84) (10.59) (5.96)

Dem. Gov approval 0.018* 0.01

(2.41) (0.93)

Rep. Gov approval -0.034 -0.015

(0.92) (0.49)

Change in net Democrats 0.00 0.00

(0.05) (1.28)

Change in net liberals -0.001+ -0.001+

(1.81) (1.81)

Dem. control redistricting 0.011 0.020+

(0.72) (1.90)

Rep. control redistricting 0.004 -0.016

(0.24) (1.31)

Constant -0.161 -0.059 0.037 0.108+ -0.161 -1.158

(1.34) (0.28) (0.49) (1.83) (1.11) (0.73)

Year fixed effects Yes Yes Yes Yes Yes Yes

State fixed effects Yes Yes Yes Yes Yes Yes

AR1 model Yes No Yes No Yes No

N 384 433 528 575 358 407

R2

0.34 0.40 0.35 0.46 0.32 0.39

Rep gov midterm - Dem gov midterm 0.047 0.027 0.015 0.014 0.030 0.025

F-test: Rep gov midterm = Dem gov midterm 4.3 2.1 3.3 4.1 7.1 5.6

(p=0.039) (p=0.151) (p=0.071) (p=0.042) (p=0.008) (p=0.019)

Note: Dependent variable is change in Democratic proportion of state assembly



29

Table 4: Predicting Change in Democratic Proportion of State Senates

(1) (2) (3) (4) (5) (6)

Balancing

Dem. gov. x Gov. midterm -0.063+ -0.043 -0.023* -0.018* -0.013 -0.012

(1.85) (1.62) (2.11) (1.97) (1.05) (1.16)

Rep. gov. x Gov. midterm -0.004 -0.004 0.002 0.001 0.007 -0.006

(0.35) (0.35) (0.16) (0.08) (0.61) (0.54)

Dem. gov. x Not gov. midterm -0.051 -0.029 -0.009 -0.005 -0.001 0.005

(1.49) (1.11) (0.97) (0.62) (0.11) (0.53)

Surge and Decline

Lagged Dem presidential vote share -0.013 0.022 0.007 0.045 0.057 0.115**

(0.26) (0.58) (0.17) (1.32) (0.97) (2.86)

Current Dem presidential vote share 0.158+ 0.228** 0.086 0.116* 0.144+ 0.231**

(1.95) (3.42) (1.39) (2.18) (1.84) (3.81)

Economic

Unemployment change x Dem. gov. -0.015 -0.006 -0.052 -0.038 -0.035 -0.034

(0.27) (0.14) (1.30) (1.16) (0.83) (0.97)

Unemployment change x Rep. gov. 0.008 0.011 -0.025 -0.002 -0.022 -0.024

(0.17) (0.27) (0.63) (0.07) (0.49) (0.63)

Other

Pre-election seat share -0.370** -0.364** -0.299**

(8.86) (10.38) (7.74)

Dem. gov. approval 0.035 0.023

(0.79) (0.66)

Rep. gov. approval -0.046 -0.031

(1.07) (0.97)

Change in net Democrats 0.00 0.00

(0.73) (0.10)

Change in net liberals 0.00 0.00

(0.53) (0.04)

Dem. control redistricting 0.017 0.042**

(1.06) (3.89)

Rep. control redistricting 0.003 -0.011

(0.16) (0.90)

Constant -0.154 0.229+ 0.032 0.212** -0.093 0.049

(1.45) (1.89) (0.30) (2.61) (0.60) (0.73)



AR1 model Yes No Yes No Yes No

N 350 399 488 535 328 377

R2

0.31 0.42 0.18 0.33 0.14 0.30



(p=0.090) (p=0.138) (p=0.023) (p=0.035) (p=0.116) (p=0.559)

Note: Dependent variable is change in Democratic proportion of state senate.



30

Table 5: Predicting Level Differences in Democratic Proportion of State Legislatures

in Presidential Midterms

1 2 3 1 2 3

Balancing

Dem. gov. x Gov. midterm 0.001 -0.184 -0.11 0.013 -0.203+ -0.132

(0.01) (1.52) (1.34) (0.19) (1.80) (1.57)

Rep. gov. x Gov. midterm 0.073 -0.055 -0.016 0.048 -0.064 -0.068

(0.65) (0.49) (0.21) (0.67) (0.66) (0.95)

Dem. gov. x Not gov. midterm 0.000 -0.06 -0.046 -0.004 -0.057 -0.023

(0.03) (1.43) (1.44) (0.33) (1.01) (0.59)

Surge and Decline

Lagged Dem presidential vote share -0.125 -0.364 -0.13 -0.154 -0.105 0.039

(0.72) (1.39) (0.95) (1.45) (0.63) (0.40)

Economic

Unemployment change x Dem. gov. 0.125** -0.117 -0.099 -0.065 -0.164 -0.032

(3.38) (1.40) (1.62) (1.16) (1.59) (0.47)

Unemployment change x Rep. gov. 0.072 0.042 0.005 0.036 0.089 0.094

(1.52) (0.57) (0.08) (0.70) (1.07) (1.61)

Other

Pre-election seat share -0.043 -0.412**

(0.55) (6.03)

Dem. gov. approval 0.081** 0.027+ 0.036 0.014

(4.25) (1.80) (0.48) (0.28)

Rep. gov. approval -0.038 -0.069 -0.069 -0.036

(0.55) (1.31) (0.96) (0.68)

Constant -0.513** -0.319** -0.758 0.121 0.023 0.310**

(5.18) (4.88) (1.39) (0.57) (0.12) (4.28)



AR1 model Yes Yes No Yes Yes No

N 353 192 241 312 166 217

R2

0.32 0.48 0.46 0.24 0.39 0.51



(p=0.002) (p=0.008) (p=0.016) (p=0.096) (p=0.027) (p=0.162)

Note: Dependent variable is change in Democratic proportion of state legislative body.



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