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23-03-2019 | Original Paper

Partisanship, Political Awareness, and Retrospective Evaluations, 1956–2016

Author: Philip Edward Jones

Published in: Political Behavior | Issue 4/2020

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Abstract

A long line of research shows that voters frequently evaluate objective conditions through a perceptual screen, seeing a stronger economy and more peaceful world when their party is in power. We know less about how and why these partisan perceptual differences have changed over recent history, however. This paper combines ANES measures of retrospective evaluations from 1956 to 2016 and shows that partisan differences (1) have increased significantly over the past few decades across all types of assessments; (2) are greatest, and have changed the most, amongst the most politically aware; and (3) closely track changes in elite polarization over this time period. The extent of partisan disagreement in retrospective evaluations is thus not constant, but rather contingent on attributes of the voter and the political context. Greater political awareness and more polarized politicians result in larger partisan perceptual differences, as the most engaged citizens are the most likely to receive and internalize cues about the state of the world from their party’s elites.

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I follow previous research (e.g., Bisgaard and Slothuus 2018) and use “partisan perceptual differences” to refer to differences between in- and out-partisans’ retrospective evaluations. Unlike the term “partisan bias”, it is purely descriptive, and agnostic on the exact mechanism producing such differences (see Gerber and Huber (2010) for more).

I use the term “political awareness” in the sense of Zaller (1992): “the extent to which an individual pays attention to politics and understands what he or she has encountered...Political awareness denotes intellectual or cognitive engagement with public affairs” (p. 21, italics in original). As Zaller notes, this is sometimes labelled sophistication, engagement, attention, and so on. Here, I understand these terms to essentially be synonymous.

A related question is how economic conditions influence voters’ assessments of the economy. Lewis-Beck et al. (2013), using similar data to this study, find that as the economy grows, evaluations become more positive. My interest here is in a different question, how economic conditions influence the magnitude of partisan differences in perceptions of the economy. I return to this distinction in the conclusion.

The ANES did not run a Time Series study in the midterm years of 2006, 2010, or 2014. Questions about retrospective evaluations first appear in the 1956 survey, and so I do not use the 1948 or 1952 studies.

Question wording and details about which year each item was asked in are included in Table A1 in the online appendix.

In some but not all years, the ANES followed up with questions about how much better or worse conditions had become. In order to maintain comparability across survey years, the results shown here do not utilize this follow-up question. Preliminary analysis using the full responses on five-point scales reached the same substantive conclusions as here.

The measures of income and education are treated as continuous variables ranging from 1–5 to 1–7 respectively. This assumes that the numerical distances between each category code represent equal increases in each underlying variable. This is a strong assumption; since these variables are used as controls here I adopt this approach for simplicity.

ANES measurement of race and ethnicity has changed significantly over the years. As such, I rely on the White indicator with all other races/ethnicities as the omitted category. This is obviously not ideal, and sacrifices a more accurate measure of respondents’ racial/ethnic identities in favor of comparability across years.

This timeframe was chosen for three reasons. First, the survey questions almost all ask for assessments of change over the prior year (see Table A1 in the online appendix). Second, previous studies use this measure (e.g., Lewis-Beck et al. 2013; Lavine et al. 2012), allowing for more comparability with the findings here. Finally, research suggests voters weigh the preceding year most heavily in their assessments (Healy and Lenz 2013).

Alternative measures of economic conditions used by Lewis-Beck et al. (2013)—the change in the Consumer Price Index, the value of the S&P 500 index, and the unemployment rate—produced substantively similar results as those reported here. Since Lewis-Beck et al. (2013, p. 526) report that change in GDP has the most robust effects on perceptions of the economy, I present those estimates here.

First differences between partisans and Independents could also be calculated, although the focus of this paper is on differences between identifiers of different parties. I leave it to future work to explore how Independents construct retrospective evaluations, but note that the analysis in the online appendix suggests Independents’ responses frequently resemble those of out-partisans.

The local weighted regression uses a span of 0.75, meaning that the nearest 3/4 of the data points are used to calculate the average for each year, with greater weight given to closer data points. See Cleveland et al. (1991).

The average awareness score is .56 but these minimum and maximum values are not particularly unusual: 6% of all respondents score a 0, and 9% a 1. Section A4 of the online appendix provides more descriptive information about the measure.

This is consistent across types of evaluations: among the least aware, there were significant partisan perceptual differences on 14% of pocketbook evaluations, 19% of sociotropic evaluations, and 8% of foreign affairs evaluations. Among the most aware, those percentages are 56%, 89%, and 88%, respectively.

This is not the full range in the dataset; polarization reached a high of 0.84 in 2016. However, the highest levels of polarization have occurred solely during divided government. Simulating for values of both terms simultaneously results in highly imprecise estimates at higher levels of polarization. As such, I limit the simulations to values of polarization of between 0.50 and 0.75, which is roughly equivalent to the increase in polarization between the 1950s and 2008.

First differences simulated from model 3 that assess how changes in economic context influence the least and most aware respondents separately can be found in Section A6 of the online appendix. They do not suggest that the conclusions about the economy’s lack of impact change when breaking the results down by awareness.

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Title: Partisanship, Political Awareness, and Retrospective Evaluations, 1956–2016
Author: Philip Edward Jones
Publication date: 23-03-2019
Publisher: Springer US
Published in: Political Behavior / Issue 4/2020
Print ISSN: 0190-9320
Electronic ISSN: 1573-6687
DOI: https://doi.org/10.1007/s11109-019-09543-y

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