Previous Evidence of Macropartisan Change

MES were among the first to apply time-series methods to aggregate partisanship survey data, leveraging the fact that the Gallup Poll had conducted at least quarterly measurements of party identification dating back to the beginning of the Eisenhower administration. Operationalizing macropartisanship as the ratio of self-identified Democrats over the sum of Democrats and Republicans, they pointed out that aggregate partisanship moved faster and further than would be expected if population replacement were the sole factor perturbing lifelong party attachments.

Drawing on the extensive literature on aggregate pocketbook voting (Kramer Reference Kramer1971; Tufte Reference Tufte1978), MES sought to test whether changing economic evaluations affected macropartisanship—in other words, whether macropartisanship is buffeted by changing performance evaluations. Specifically, they predicted that a strong economy should burnish the image of the party in control of the presidency; conversely, poor economic conditions should reduce the share of the public that identifies with the president’s party. A second hypothesis is that presidential approval, which reflects both economic and noneconomic performance evaluations, influences macropartisanship. All else being equal, the share of Democrats should rise when a Democratic president becomes more popular or when a Republican president becomes unpopular.Footnote ² Their analysis showed support for both of these claims using data from 1953 through 1987.

What Has Changed?

To our knowledge, there has been no comprehensive assessment of whether the original findings—that the aggregate distribution of partisanship in the country depends on economic evaluations and presidential approval more generally—still hold true today. There are reasons to suspect that macropartisanship may have become less responsive to short-term forces.

First, affective polarization has increased in the American public, particularly post-2000 (Iyengar, Sood, and Lelkes Reference Iyengar, Sood and Lelkes2012; Iyengar and Westwood Reference Iyengar and Westwood2015). Specifically, partisans are increasingly viewing the opposition party more negatively than their own, and political behavior has become increasingly driven by negative attitudes toward the opposition (Abramowitz and Webster Reference Abramowitz and Webster2016). At the same time, election outcomes have become nationalized, such that citizens tend to vote for candidates of one party across all elective offices (Hopkins Reference Hopkins2018; Jacobson Reference Jacobson2015). As a consequence, there is much less ticket splitting, and more party loyalty, than ever before. For macropartisanship, the rise of distaste for the other party may decrease the likelihood that partisans switch sides in response to economic or political events.

Second, the increasing availability of partisan media may limit some citizens’ exposure to objective information about the opposition party or the state of the country (Prior Reference Prior2007; Stroud Reference Stroud2011). For instance, recent work finds that exposure to CNN as opposed to Fox News during the Trump administration changed viewers’ perceptions of facts and also their evaluations of elected officials (Broockman and Kalla Reference Broockman and Kalla2022). While perceptions of the state of the economy have surely always been influenced by party identification (Bartels Reference Bartels2002; Campbell et al. Reference Campbell, Converse, Miller and Stokes1960; Gerber and Huber Reference Gerber and Huber2009), the opportunities for selective exposure in today’s information environment may dampen the connection between economic swings and macropartisan attachments. We note too that scholars have made progress in identifying when these changes have occurred—in other words, when political attitudes and behavior became so tethered to party. Some of this work identifies the rise of Newt Gingrich and the Republican takeover of the House in 1994, coupled with the introduction of Fox News, as an inflection point when elite polarization and conflict accelerated (e.g., Lee Reference Lee2016; Theriault Reference Theriault2013), and in turn changed the behavior of the electorate. For example, Bonica and Cox (Reference Bonica and Cox2018) show that since 1994, voters no longer punish candidates for ideological extremity and rather vote exclusively on the basis of national party preferences. Likewise, Hamel and Miller (Reference Hamel and Miller2019) show that after 1994 politicians engulfed in scandal have experienced a much smaller decline in their vote share than they did previously. They also show that partisan campaign donors give more to scandal-tainted politicians today, suggesting that partisan donors today defend rather than punish bad behavior. Our goal in this essay is to extend previous work and reassess how consumer sentiment in particular shapes macropartisanship. That is, what has changed (and when) since the ink dried on MES’s analysis? We thus not only update the findings of a canonical study, but build on newer work on the malleability of partisanship in the modern era.

Data

The three key time-series variables in MES’s model are macropartisanship, consumer sentiment, and presidential approval. Each series raises some subtle measurement issues. Macropartisanship has provoked a certain amount of controversy because of the manner in which the party-identification question has been asked by the Gallup Poll. Unlike the American National Election Study, which asks “In general when it comes to politics, do you think of yourself as a Republican, Democrat, Independent, or what?” Gallup begins its version of the party-identification question with the phrase “In politics as of today.” By calling respondents’ attention to the present moment, the Gallup wording arguably accentuates the effects of recent events and short-term forces, a point demonstrated by Abramson and Ostrom (Reference Abramson and Ostrom1991). Another complication is the change in survey mode over time, as Gallup moved from face-to-face interviews to phone interviews in the 1980s.Footnote ³

Approval presents a challenge insofar as it is measured using the same Gallup surveys used to measure macropartisanship. The repurposing of the same polls to create both a left-hand side and right-hand side measure runs the risk of exaggerating the apparent effect of approval, since a poll that draws an extra dollop of Democrats will also draw higher approval of a Democratic president. One way around this issue is to use approval measured by a different poll, such as CBS/New York Times (Green, Palmquist, and Schickler Reference Green, Palmquist and Schickler1998), but this approach will not work here because no other poll runs concurrently with Gallup from 1953 through 1987, or from 1988 to 2021.Footnote ⁴

We therefore follow MES in employing quarterly data from the Gallup time series for both our measures of macropartisanship and presidential approval, recognizing that this approach may tend to overstate the causal influence of approval. We begin with data from Smidt (Reference Smidt2018), extending that series from its end point of 2012 forward to 2021 using all available Gallup polls conducted in those subsequent years, placing them within quarters, and calculating quarterly averages for our variables of interest. We adjust for Gallup’s shift from face-to-face polling to phone polling by using concurrent surveys that employ both modes to calibrate the effects of this mode change.Footnote ⁵

To measure economic perceptions, MES use the Michigan Index of Consumer Sentiment (ICS), which conveniently spans the entire time period from 1953 onward. This measure combines survey responses to questions about the financial condition of respondents and their perceptions about current and future economic conditions. Enns, Kellstedt, and McAvoy (Reference Enns, Kellstedt and McAvoy2012) show that this index tracks closely with subjective evaluations of the economy as measured by Gallup, but the latter measures are only available from 1985 onward.

Although a case could be made for augmenting or substituting measures, for present purposes our priority is to compare time-series relationships over time, which is best done with a consistent set of measures spanning the longest possible time period. Thus, our analysis leaves the basic variables used by MES largely intact (apart from the necessary mode adjustment to macropartisanship) and simply extends them forward in time. Descriptive statistics can be seen in table 1.Footnote ⁶

Table 1

Descriptive Statistics

Note: “party-adjusted” variables are multiplied by -1 for quarters during Republican administrations.

Visualizing Over-Time Patterns

Before delving into statistical analyses of these time series, we begin with a graphical depiction of the macropartisanship time series covering the period from Eisenhower’s inauguration to Biden’s. Figure 1 displays the full series, with a vertical line demarcating the dates covered by the original MES analysis, which runs through the fourth quarter of 1987. To the right of the vertical line is the “Modern” period (from the first quarter of 1988 through the first quarter of 2021).

Figure 1

Macropartisanship, 1953–2021

Note: macropartisanship is the proportion of party identifiers who are Democrats, based on data from Gallup. This quarterly time series has been adjusted to account for Gallup’s shift from in-person to phone surveys.

With the exception of the marked shift in favor of Republicans during the 1980s, the two periods are broadly similar in terms of amplitude. Although we see short-term fluctuations in both the 1953–87 and 1988–2021 periods, they are relatively small in magnitude. The median quarterly change (the absolute value of current minus lagged macropartisanship) is 1.07 percentage points in the MES period and 1.08 points in the Modern period. The median yearly change (the absolute value of current minus four-lagged macropartisanship) is 1.87 points for the MES period and 1.71 points for the Modern period.

Figure 2 arrays all three time series for each presidency from Eisenhower through Trump. As expected, approval and consumer sentiment move reliably together for most presidents. For example, the decline in consumer sentiment during the first three years of the Carter administration coincided with erosion of Carter’s approval ratings. By contrast, the connection between short-term forces and macropartisanship is subtle. Declines in the popularity of Democratic presidents, such as Johnson or Carter, diminished the share of Democrats. Conversely, declines in the popularity of Republican presidents, such as George H. W. Bush and George W. Bush, coincided with an uptick in Democratic identifiers. On the other hand, the pattern of improving economic conditions under Clinton and Obama seems to have contributed little to the share of Democratic identifiers. The same holds for deteriorating economic conditions under Ford or Trump. The next section attempts to quantify the extent to which short-term forces had different effects on macropartisanship during the two periods.

Figure 2

Macropartisanship, ICS, and Presidential Approval: Eisenhower to Trump

Note: macropartisanship (the proportion of Democrats among respondents who identify with a major party) and presidential approval are measured by the Gallup Poll. Consumer sentiment is measured by the Michigan Index of Consumer Sentiment, divided by 100.

Statistical Model

Our workhorse model will be a minor variant on the model originally presented by MES.Footnote ⁷ Using the subscript t to denote each quarterly observation, the regression of macropartisanship (M_t) includes a lagged dependent variable (M _t−1), a party indicator (P_t), an interaction between the Index of Consumer Sentiment (I_t) and the party indicator, and a disturbance term:Footnote ⁸

(1) $$ {M}_t={\alpha}_0+{\alpha}_1{M}_{t-1}+{\alpha}_2{I}_t{P}_t+{\alpha}_3{P}_t+{u}_t $$

The party indicator is scored 1 if the president is a Democrat and -1 otherwise. This indicator is interacted with consumer sentiment so that good economic times under Democratic presidents increase macropartisanship, while good economic times under Republican presidents decrease it.

The basic structure of the regression model remains the same when we instead regress macropartisanship on presidential approval (A_t):

(2) $$ {M}_t={\beta}_0+{\beta}_1{M}_{t-1}+{\beta}_2{A}_t{P}_t+{\beta}_3{P}_t{+}{u}_t^{\prime } $$

Again, the party indicator is interacted with presidential approval so that popularity of Democratic presidents increases macropartisanship, while popularity of Republican presidents decreases it.Footnote ⁹

Estimating Structural Breaks

The previous section looked for signs of structural change by comparing the MES period to the Modern period. An alternative approach is to pool the full time series and use statistical tools to detect changes in model fit at any point in time. The strucchange package in R (Zeileis et al. Reference Zeileis, Leisch, Hornik and Kleiber2002) supports this approach. We test for structural breaks with equation 1 over the entire time series using a moving window with a bandwidth that examines 20% of the data at a time. We set the number of breaks to investigate at four—the maximum number the software allows us to search for. This approach allows us to see search results had we looked for one, two, three, or four structural breaks.

We cross-validate this search with the framework developed by Bai and Perron (Reference Bai and Perron1998; Reference Bai and Perron2003) for detecting unknown structural breaks and then forming 95% confidence intervals around when they occur. This approach, which is implemented in the Stata package xtbreak, outputs the apparent number of breaks, when they are said to occur, and the confidence intervals around these estimated occurrences.Footnote ¹⁰ Xtbreak allows up to five breaks. Typically, the more breaks one investigates, the more uncertainty there will be about their timing. Recognizing that identifying the timing of multiple structural breaks may overtax the available data, we focus primarily on whether the two approaches tell a similar story about structural change.

The results from these methods are summarized in table 3. Applying the xtbreak framework to equation 1 (consumer sentiment) detects five structural breaks over the entire time series: 1963, quarter 2 (prior to Johnson’s legislative juggernaut); 1974, quarter 2 (Watergate); 1984, quarter 2 (as Reagan expanded the Republican Party’s voter base en route to a landslide reelection victory); 1996, quarter 1 (a period of conflict between Clinton and House Republicans); and 2006, quarter 1 (George W. Bush’s slide in popularity). As we search for fewer breaks—moving from five to one—we see the breaks from the early 1960s, mid-1990s, early 2000s, and early 1970s disappear in turn.

Table 3

Location of Structural Breaks

Note: “xtb” denotes results from the xtbreak package in Stata. “sc” denotes results from the strucchange package in R. Strucchange does not support searches for more than four breaks.

The strucchange package supports a search for up to four breaks. As table 3 indicates, the strucchange search reveals a substantial structural break in late 1966, around the time Johnson had secured major legislative victories, including the Civil Rights Act of 1964, the Medicare and Medicaid Act of 1965, and the Voting Rights Act of 1965. Regardless of the number of breaks we search for (including one), the strucchange package finds a structural break occurring in either the third or fourth quarter of that year. If we search for two breaks, we find one in 1966 and another in 1995, quarter 4 (the Clinton–Republican conflict). A search for three breaks finds them in 1966, quarter 3; the third quarter of 1983 (the Reagan realignment); and the fourth quarter of 2005, when George W. Bush’s popularity began to slide. Finally, a search for four breaks finds them in 1966, 1980, 1994, and 2007.

Both the xtbreak and strucchange packages reveal a similar pattern when we use presidential approval as a predictor of macropartisanship instead of consumer sentiment. Indeed, in all cases but one the break points identified for presidential approval are within two years of those for consumer sentiment, and in most cases they are exactly the same. The one exception is the strucchange search for one break: the strucchange package finds that break in 1974 for presidential approval, and in 1966 for consumer sentiment.

The two methods for detecting structural breaks do not agree on the precise timing of post-MES changes, but they both point to the first Clinton administration and the second Bush administration as turning points. The two statistical packages also turn up ample evidence of structural change during the period that MES studied. Both approaches find evidence of structural change during the early to mid-1960s, and both suggest that change was afoot sometime between 1980 and 1984. The two approaches diverge mainly on the question of whether a structural break occurred during Watergate. Interestingly, neither method detects a structural break during Trump’s rise to the presidency or during his term in office.