Reversing the Causal Arrow: The Political. Conditioning of Economic Perceptions in the. 2000--2004 U.S. Presidential Election Cycle. Geoffrey Evans University ...
Reversing the Causal Arrow: The Political Conditioning of Economic Perceptions in the 2000--2004 U.S. Presidential Election Cycle Geoffrey Evans University of Oxford Mark Pickup Simon Fraser University Many economic voting models assume that individual voters’ reactions to incumbents are strongly conditioned by their perceptions of the performance of the macroeconomy. However, the direction of causality between economic perceptions and political preferences is unclear: economic perceptions can be a consequence of incumbent support rather than an influence on it. We develop the latter thesis by examining the dynamic relationship between retrospective economic perceptions and several measures of political preferences—approval, partisanship, and vote—in the 2000–2004 U.S. presidential election cycle using the ANES 2000-2002-2004 panel study to estimate structural equation model extensions of the Anderson and Hsiao estimator for panel data. Our findings confirm that the conventional wisdom misrepresents the relationship between retrospective economic perceptions and incumbent partisanship: economic perceptions are consistently and robustly conditioned by political preferences. Individuals’ economic perceptions are influenced by their political preferences rather than vice versa.
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any political scientists assume that subjective indicators, such as perceptions of economic performance, can be treated as causes of presidential or party choices. Most interpretations of this causality rely heavily on assumptions about the causal direction of any observed relationship between responses to survey questions usually asked a few minutes apart. Following the application of some statistical controls, the observed net coefficient is taken as an estimate of the causal impact in one direction. Unsurprisingly, concerns about the endogeneity of such subjective responses are at least part of the reason for the growth of field, and other, experiments in recent years. However, evidence on the direction of the relationship between perceptions and political choices can also be ascertained through the analysis of panel surveys using appropriate econometric estimation procedures. This paper uses these techniques to examine the case of the effect of economic perceptions on U.S. presidential support. The United States is the primary testing ground for much research into the effects of economic perceptions, with aggregate appraisals of the macro economy (usually operationalized as the Index of Consumer
Sentiment) driving Presidential election outcomes (Erikson, MacKuen and Stimson 2002), and subjective perceptions of the economy motivating the individual voting decisions underlying those outcomes (Fiorina 1981; Kinder, Adams, and Gronke 1989; Kinder and Kiewiet 1979; Lewis-Beck, Nadeau, and Elias 2008). It is thus an important test case. We focus on economic perceptions because of the central role attributed to economic performance in providing a mechanism of political accountability in the United States and elsewhere (Mutz 1998). For the accountability function of incumbent economic performance to be effective a complex chain of assumptions needs to be met (Anderson 2007). When economic perceptions influence responses to incumbent performance they facilitate the process of electoral accountability. However, to the degree that economic perceptions are derived from rather than influence political preferences the role of the economy in accountability is weakened. The causal interpretation of the relationship is therefore central. The conventional view of the causal link between economic perceptions and political choices is that voters punish or reward incumbents for their economic
The Journal of Politics, Vol. 72, No. 4, October 2010, Pp. 1236–1251 Southern Political Science Association, 2010
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doi:10.1017/S0022381610000654 ISSN 0022-3816
reversing the causal arrow performance. Its proponents have recently arrayed in the region of 400 studies to bolster this pronouncement (Lewis-Beck and Stegmaier 2007). Although many of these studies are of aggregate trends, these are premised on the notion that it is the individual vote decision that is influenced by the performance of the economy: ‘‘The powerful relationship between the economy and the electorate in democracies the world over comes from the economic responsiveness of the electors, the individual voters’’ (Lewis-Beck and Stegmaier 2000, 211), and the key to understanding the choices these individuals make lies in their perceptions of economic performance: ‘‘ . . . the search for the preferred macroeconomic indicators, and their lagged effects pattern, has been largely abandoned... models now contain aggregate perceptual evaluations of general economic performance instead of hard data on unemployment, inflation, income, or growth’’ (Lewis-Beck and Stegmaier 2000, 186). The plausibility of aggregate models of economic voting relies therefore on assumptions about individuals’ perceptions of economic performance and how these influence political preferences. Even within the economic voting paradigm there has been skepticism with respect to this assumption: Critiques of individual level analyses of economic voting argue that cross-sectional variations in perceptions of the economy are variations in perceptions of a constant that are best treated as perceptual errors correlated with reported vote. To assume the direction of association is one way biases estimates of economic effects on vote upwards (Erikson 2004; Kramer 1983). However, treating variation in perceptions of the economy as error around a constant has been shown to be problematic by studies demonstrating heterogeneity in economic perceptions that can be traced to differences in vulnerability to economic events. For example, income, employment status, and social class, are all likely to condition variation in economic perceptions and associated political responses (e.g., Bartels 1996; Duch, Palmer, and Anderson 2000; Haller and Norpoth 1997; Hetherington 1996; Krause 1997; Weatherford 1978), so that: ‘‘aggregate deviations of individual-level economic evaluations from objective conditions are not idiosyncratic but reflect the systematic effects of respondent characteristics. This, in turn, compromises the connection between actual economic conditions and voters’ evaluations of those conditions’’ (Anderson 2007, 280). Recently there has been a reaction against the use of subjective indicators of economic performance and a renewal of interest in objective measures. Van der Brug, van der Eijk, and Franklin’s (2007) use of
1237 exogenous measures of the economy produces a far smaller impact from the economy to political choices than is found using retrospective economic perceptions (cf. Duch and Stevenson 2008)—a pattern also observed in studies that try to establish linkages between objective indicators of economic performance and economic perceptions, such as media reporting (Sanders and Gavin 2004). Wilcox and Wlezien (1993) provide evidence of the endogeneity of economic perceptions through a survey experiment, while Wlezien, Franklin, and Twiggs’ (1997) 2SLS estimation finds that preelection vote intention predicts perceptions of economic performance, a finding echoed in Van der Eijk et al.’s (2007) EU study using SEM. Anderson et al. (2004) examine economic perceptions in both preand postelection surveys and find that evaluations of the economy’s past and future performance are adjusted retrospectively to make them consistent with vote choice so that they ‘‘cease being exogenous . . . thus calling into question the extent to which the economy truly moves voting behavior’’ (Anderson et al. 2004, 684). Using a similar before-and-after election design, Ladner and Wlezien (2007) demonstrate that economic expectations are conditional on estimates of election outcomes in the United States as well as the United Kingdom: economic expectations prior to elections reflect both the political present and expected future. These studies provide grounds for believing that short-term economic expectations are strongly influenced by political factors. A longer term influence has also been identified by Evans and Andersen (2006) who find that retrospective macro-economic perceptions are strongly conditioned by prior opinions of the incumbent party and, once this association is controlled, have little or no effect on incumbent popularity over a five-year period (see also Johnston et al. 2005). None of these ‘‘revisionist’’ (Lewis-Beck, Nadeau, and Elias 2008, 846) analyses have so far focused on the primary source of information on economic voting in U.S. elections, the ANES full electoral cycle panel study (most use British data which is more plentiful), but all point to the possibility that estimates of the effects of economic perceptions on voting in U.S. presidential elections could be erroneous and that in many circumstances partisanship is the key mover of economic perceptions rather than vice versa. This should not perhaps surprise us: From Berelson, Lazarsfeld, and McPhee (1954, 220) onwards, whether as expressions of partisan loyalty, the preservation of consistency, or the role of the party as a source of trusted information, U.S. voters’ perceptions have demonstrated ‘‘distortion in harmony with political
1238 predispositions," reflecting ‘‘the role of enduring partisan commitments in shaping attitudes toward political objects’’ (Campbell et al. 1960, 135), as ‘‘observers with different preconceptions interpret the same piece of evidence in ways that conform to their initial views’’ (Gerber and Green 1999, 197; see also Bartels 2002; Green, Palmquist, and Schickler 2002; Zaller 1992). The significance of such partisan influence for economic voting models is apparent: if economic perceptions are influenced by the type of phenomena they are assumed to influence their impact will be mis-specified by models that fail to control effectively for this reciprocal relationship. This implication has unsurprisingly been disputed by adherents of economic voting theories. Lewis-Beck (2006; Lewis-Beck, Nadeau, and Elias 2008) has replied to revisionist accounts by focusing on the specification and estimation of models, the selection of variables used to operationalize key concepts, and by presenting analyses of their own which demur from the revisionist view. We address these concerns and extend the analysis of the political conditioning of economic perceptions to the 2004 U.S. presidential election using long-term, 2000-2002-2004, panel data to untangle the relations between the key measures in this debate.1 We focus on the 2004 Presidential Elections and the electoral cycle preceding it not only because appropriate panel data are available but also because there are good reasons to believe that the economy was not a major factor influencing vote choice, or at least, that other important considerations—the war on terror, the invasion of Iraq, religion and the debate over abortion—were likely to be more salient. To find powerful economic voting effects—as LewisBeck, Nadeau, and Elias (2008) do using these same data—indicates there may be a misleading upward
1
An online appendix for this article containing formal proofs and supplemental analyses is available at www.cambridge.org/cjo/ EvansPickupWAppendix. Data used in this analysis are taken from The National Election Studies (www.umich.edu/~nes). THE NATIONAL ELECTION STUDY 2000-2002-2004 FULL PANEL FILE (2004.M2000). Ann Arbor: University of Michigan, Center for Political Studies. The Anderson and Hsiao estimation was performed in STATA using the ivregress command. The structural equation models were run in R using the sem and polycor packages. Multiple imputation was also performed in R using Amelia. Supporting materials necessary to reproduce the numerical results in the paper are available at www.nuffield.ox.ac.uk/users/evansG/Web Appendix.pdf.
geoffrey evans and mark pickup bias in current estimates of the effects of economic perceptions.2 To preview our findings, we show that in crosssectional multivariate models the macro economy does appear to be the only robust explanatory variable across a range of dependent presidential support measures. We then demonstrate how appropriate dynamic estimation disposes completely of this misleading impression. For this purpose we introduce techniques developed in econometrics which allow us to estimate panel relationships more effectively than hitherto. Using this structural equation model extension of the Anderson and Hsiao 2SLS estimator, we show that typically used measures of economic perceptions have no discernable impact on vote, presidential approval and party identification when the effects of political preferences on economic perceptions are taken into account. In the 2004 U.S. presidential context political preferences have far stronger effects on economic perceptions than vice versa.
The Current Focus of Dispute The only revisionist analysis that addresses the endogeneity of economic perceptions using extended panel analysis over a complete electoral cycle is that by Evans and Andersen (2003; 2006), who examined the British Election Panel Study across the 1992–97 British electoral cycle. They treated economic perceptions and party support as part of a dynamic process of mutual influence and demonstrated that effects derived from cross-sectional models estimating the impact of contemporaneous economic perceptions were not only substantially overestimated but also the effects of party support (or vote) on economic perceptions were far stronger. This study has been criticized by Lewis-Beck (2006). His critique rests in part on the argument (Achen 2000) that residual autocorrelation biases the coefficients for explanatory variables downward even when lagging the dependent variable is theoretically appropriate.3 Using panel data from the 2
We do not examine personal economic (or ‘‘pocket-book’’) or prospective models of economic voting. Retrospective macroeconomic perceptions have formed the basis of most influential models of subjective economic voting from Fiorina (1981) onwards. As Kramer (1983) observed, personal economic circumstances are likely to be more idiosyncratic and less attributable to political agency than is the national economy (see Evans and Andersen 2006). 3
The reader is referred to Wooldridge (2006, 415–16) for a useful discussion on when this will and will not be a problem. Keele and Kelly (2005) have provided a Monte Carlo simulation study of this problem for relatively large T time series.
reversing the causal arrow 2000-02-04 ANES as well as replication analyses from studies of earlier US, British and Canadian elections Lewis-Beck, Nadeau, and Elias (2008) claim to find evidence that economic effects are robust: ‘‘economics does really matter for the vote choice, even more so than previously thought’’ (2008, 94). However, their analysis has various problematic features. Firstly the choice of instrumental variables: as is well-known, an instrument is a variable, conditional on the other covariates, that is not itself a predictor of the dependent variable—in this case, vote. To instrument economic perceptions, Lewis-Beck, Nadeau, and Elias use race, age, gender, education, income, interest in politics, class, unemployment status, union membership, and personal retrospective economic evaluations. It would not seem permissible to leave any of these out as a predictor in the explanatory equation. Whether any of them are left out of the explanatory equation is not clear. The relevant table contains no indication of any instruments being excluded from the explanatory equation. Nor is there any indication that this information is available anywhere else. Lewis-Beck, Nadeau, and Elias also include lagged party identification in a simple cross-sectional model to demonstrate that it does not eliminate the effect of economic perceptions. However, to control for the endogeneity of economic perceptions they need a lag of the dependent variable— in this case vote.4 Only if party identification is the dependent variable is it appropriate to use a lag of party identification as the control. Lewis-Beck, Nadeau, and Elias indicate in a footnote that they ran this model with a lag of vote instead of party identity, but they do not indicate the statistical significance of the results.5 We are therefore left with uncertainty with respect to conclusions that might be drawn about the extent and direction of causality between economic perceptions and political preference. This paper addresses this issue by analyzing panel data about political preferences and economic perceptions through the same four-year Presidential electoral cycle. This enables us 4 The party identity variable they use is coded as: +1 if a partisan of the incumbent, 0 for an independent and 21 for a partisan of the challenger. It is unusual to use party identity in this way, rather than as two dummy variables. Somewhat more surprising, given that the dependent variable is (0, 1) binary, is the fact that they claim to use an ordered probit: ‘‘Since both variables have the same metric (three ordered categories), the magnitude of the coefficients can be directly compared in an ordered probit analysis’’ (Lewis-Beck, Nadeau, and Elias (2008, 86–87).
1239 to address concerns about bias and demonstrate the dominance of partisan conditioning even in a context where extensive evidence supporting the economic voting model has been garnered. We deal with the concern about residual autocorrelation identified by Lewis-Beck and other important issues by applying techniques for solving problems in small T dynamic model estimation identified by econometricians since the 1950s. Hurwicz (1950) first identified problems for small T time series, and Nickell (1981) analytically described similar problems for dynamic panel models. The general conclusion is that estimation bias for dynamic models (including one or more lag of the dependent variable) using OLS is a significant problem when T is small. What is needed is a procedure that deals with all concerns regarding bias, correlated errors and measurement and context. For this we adopt a simple structural equation model extension of the estimator proposed by Anderson and Hsiao (1981, 1982). Following Evans and Andersen (2006), we hypothesize that once previous Presidential support is taken into account through appropriate estimation procedures, economic perceptions will have little or no impact on Presidential support, whereas economic perceptions will be significantly affected by Presidential support. In other words, the balance of influence will be disproportionately from politics to economics rather than vice versa.
Analysis Table 1 presents the results of a series of models estimating the impact of economic assessments on vote intention and approval. Approval is a 5-point scale measure of how President Bush is handling his presidency ranging from ‘‘Disapprove strongly’’ to ‘‘Approve strongly.’’ Approval is also often reduced to a binary variable indicating approval of President Bush’s handling of his presidency.6 Vote is estimated using a logistic model; approval as a five-point scale is estimated using a linear model; and approval as a binary variable is estimated using both. The models each include a set of control variables typically included in such models. Some of these identify particularly relevant issues in the campaign—the war on terror,
5
Lewis-Beck also criticized Evans and Andersen for using measures of party support rather than vote in their analyses. This decision was taken to minimize concerns about the validity of reported vote intention in nonelection years. Evans and Andersen (2003) include vote in their panel analyses and confirm the findings obtained with party support.
6 This is often referred to as popularity, as it is this measure that is typically aggregated to produce a popularity measure for U.S. presidents.
1240 T ABLE 1
geoffrey evans and mark pickup Economic Voting, Approval and Approval Models Approval: binary variable
Vote
PID Ideology Econ P.finance Abortion Welfare Terror Iraq Gender Age Black Income Education Intercept N Variable PID Ideology Econ P.finance
Abortion
Welfare Terror
Iraq Gender Age Black Income
Education Vote Approval: 5-point scale Approval : binary
Approval: five-point scale
Logit Coef
SE
Logit Coef
SE
OLS Coef
SE
OLS Coef
SE
0.803** 20.034 0.966** 20.337 20.585** 20.257 0.202 2.034** 20.419 0.004 22.066 0.267** 0.746 1.315 707
0.124 0.114 0.135 0.266 0.213 0.302 0.241 0.425 0.399 0.013 1.202 0.106 0.454 1.798 707
0.126 0.044 0.486** 0.374** 20.081 0.229 0.019 0.269 0.231 0.005 21.294 0.068 20.821** 0.896 707
0.074 0.075 0.098 0.151 0.108 0.172 0.129 0.282 0.205 0.007 0.802 0.057 0.258 1.055 707
0.054** 20.024 0.243** 0.202** 20.005 0.022 0.022 20.072 0.120** 0.002 20.333** 0.006 20.243** 1.812
0.021 0.019 0.028 0.042 0.032 0.050 0.037 0.087 0.061 0.002 0.124 0.016 0.072 0.306
0.019 0.010 0.065** 0.051** 20.014 0.029 0.004 0.056 0.031 0.001 20.055 0.010 20.116** 0.599
0.010 0.009 0.013 0.020 0.016 0.024 0.018 0.042 0.029 0.001 0.060 0.008 0.035 0.148
Description Seven-point scale ranging from strong Democrat to strong Republican Seven-point scale ranging from extremely liberal to extremely conservative Five-point scale indicating perceived change in the economy over the past year, ranging from 1. Much worse to 5. Much better. Five-point scale indicating how the respondent felt they were getting along financially compared to a year ago, ranging from 1. Much worse to 5. Much better. Four-point scale indicating position on abortion , ranging from ‘‘by law, abortion should never be permitted’’ to ‘‘by law, a woman should always be able to obtain an abortion as a matter of personal choice’’. Variable indicating if the respondent felt federal spending on welfare programs should be: 1. Increased; 2. Kept about the same ; 3. Decreased. Five-point scale indicating respondents approval of the President’s handling of the war on terror, ranging from disapprove strongly to approve strongly Dummy variable indicating if respondent felt the war in Iraq was worth the cost. Dummy variable indicating if the respondent is male Age of respondent in years Dummy variable indicating if the respondent is black Family income coded as: 01. $0–$14,999; 02. $15,000–$34,999; 03. $35,000–$49,999; 04. Just about $50,000; 05. $50,000–$64,999; 06. $65,000–$84,999; 07. More than $84,999 Binary variable indicating if the respondent attended an undergraduate college or university Binary variable indicating a vote intention for the President Five-point scale of approval of the President’s handling of his job ranging from 1. Disapprove strongly to 5. Approve strongly Binary variable indicating approval of the President’s handling of his job, neutral point coded as not approving
**Significant at the 95% confidence level Data source: the 2004 component of the 2000-2002-2004 National Election Study dataset, except ideology which comes from the 2002 component.
reversing the causal arrow Iraq, abortion—while others are standard predictors— personal finances, gender, age, race, income, education (see Table 1) as suggested by previous research (e.g., Conover, Feldman, and Knight 1987; Duch, Palmer, and Anderson 2000). The controls should ostensibly serve to prevent spurious inferences about the effects of economic perceptions on presidential support. The only attitude and perception variables that are consistently significant in all three models are party identification and macro-economic perceptions. Identifying as a Republican increases the probability of supporting President Bush, as do positive assessments of Bush’s handling of the economy. Other significant influences on Vote but not on Approval, are abortion and Iraq. Personal finances and several demographic indicators have partial effects but do not influence all presidential support measures. So far, therefore, we find estimates of economic effects that are impressively robust to the inclusion of other important influences on Vote and Approval, outperforming apparently hot button issues such as terrorism, abortion, and the Iraq war. These initial estimates of these apparently powerful cross-sectional concurrent effects of economic perceptions provide the benchmark against which we now compare the magnitude of effects estimated in models that endogenize economic perceptions utilizing the panel element of the ANES.
1241 This is the standard zero conditional mean assumption, required when T is small. It asserts that the expected value of the error ei,t, given the explanatory variables for all time periods, is zero.8 This means that the error at time t is independent of all possible leads and lags of each explanatory variable (Wooldridge 2006). In the cross-sectional setup of the typical voting model, data for only one time point exists and t 5 1). The concern is that APPROVEi,t is a function of APPROVEi,t21—current values of approval for a given individual are related to past values of approval for that same individual. If this relationship is not modeled then past values of approval are contained within ei,t. If past values of approval condition current assessments of the economy for a given individual then E(ei,t|ECONi,s) 6¼ 0 for s 5 t + 1 and the zero conditional mean assumption is violated. This is a source of endogeneity and will bias the estimation of b1 upwards. The coefficient b1 will essentially capture both the effect of the economy on current government approval and the effect of past approval on current approval. Therefore to control for this possibility, the economic voting model that we wish to estimate is: APPROVEi;t 5 g 0 þ g1 APPROVEi;t1 þ g2 ECONi;t þ zi;t ; where:
Specification
APPROVEi,t21 is the government approval rating or vote intention for individual i at time t21.
In our analysis, we consider models of vote, approval, and—for completeness—party identification (PID). This section starts by describing the model in terms of approval. The exact same considerations apply to the models of vote and PID. The traditional economic voting model (stripped of covariates included as control variables) is:
As this is a model for panel data, the error term will have two components, zi;t 5 hi þ y i;t ;
APPROVEi;t 5 b0 þ b1 ECONi;t þ ei;t where:
where: hi is an unobserved individual-specific time-invariant effect; yit is the time varying disturbances for individual i at time t;
APPROVEi,t is the government approval rating or vote intention for individual i at time t; ECONi,t is the economic assessment of individual i at time t; and it is assumed that E(ei,t|ECONi,s) 5 0, " s.7 7 The important part of this assumption is not that the conditional expectation is equal to zero but rather that it is a constant. If it is a constant, it can be defined as 0 by the appropriate choice of intercept. The other Gauss-Markov assumptions required for OLS to be unbiased are also assumed.
Eðhi Þ 5 Eðyi;t Þ 5 Eðhi y i;t Þ 5 0 ; and yi;t 5 ry i;t1 þ ei;t E ei;t 5 0 and 0 # jrj , 1 :
8
This assumption can be relaxed somewhat as T approaches infinity. But this is never the case for economic voting models.
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The effect hi represents the heterogeneity in the means of the APPROVEi,t series across individuals. It represents time-invariant, unobserved characteristics that make an individual more or less likely, on average, to approve for the government. It is also an obvious cause of violation of the zero conditional mean assumption: as E(hi|APPROVEi,t21) 6¼ 0, the assumption that E(hi|APPROVEi,s) 5 0 for all s does not hold. Therefore, the presence of hi will bias the estimate of g1 (Greene 2003). This, in turn, may bias the estimate of g 2. Since the late 70s (Ashenfelter 1978; Hausman 1978) statisticians and econometricians have been developing methods to account for such bias when estimating dynamic panel models with a limited T. It has been found that the optimal method depends on the exact model being estimated and the data available. For our purposes, the Anderson and Hsiao estimator (1981, 1982) is the approach of choice. It has the advantages of being very simple to implement, well understood, and can be found in essentially any modern econometric panel-data textbook (e.g., Arellano 2003; Baltagi 2005; Greene 2003; Hsiao 2003). The online appendix describes how the Anderson and Hsiao estimator resolves the issue of endogeneity. In short, by estimating: DAPPROVEi;t 5 g1;1 ðDAPPROVEi;t1 Þ þ g 1;2 ðDECONi;t Þ þ Dy i;t DECONi;t 5 g 2;1 ðDECONi;t1 Þ þ g2;2 ðDAPPROVEi;t Þ þ Dy i;t
ð1Þ ð2Þ
using some sufficiently large lag of APPROVEi as an instrumental variable for DAPPROVEi,t21 in (1) and some sufficiently large lag of ECONi as an instrumental variable for DECONi,t21 in (2), we can obtain unbiased estimates of the coefficients from the models we are really interested in: APPROVEi;t 5 g 1;0 þ g1;1 APPROVEi;t1 þ g 1;2 ECONi;t þ zi;t ECONi;t 5 g 2;0 þ g 2;1 ECONi;t1 þ g 2;2 APPROVEi;t þ zi;t
ð1:1Þ ð2:1Þ
Note that the coefficients in equations (1) and (2) are the same as those in equations (1.1) and (2.1). This is standard econometric practice. However, it poses two limitations: (1) it assumes a linear function for government approval; and (2) it requires estimating equations (1) and (2) separately. Limitation (2) does not allow for the direction of the contemporaneous relationship between approval and economic perceptions
to be tested but there is nothing statistically wrong with estimating equations (1) and (2) separately. Also (1) is an appropriate approximation. Therefore, as a first step, we use the method as described using the binary approval variable and for equation (2), the 5-point scale economic variable. This is done using the second lags of APPROVEi,t and ECONi,t as instruments. This, of course, makes the assumption that the second lags are appropriate instruments. To control for the possibility that the contemporaneous arrow of causation between approval and economic perceptions may run in both directions, the lag of economic perceptions is used as an instrument for the economic perceptions variable in the approval equation (1). Similarly, the lag of approval is used as an instrument for the approval variable in the economic perceptions equation (2). As a second step, we employ a simultaneous equation modeling extension of the Anderson and Hsiao estimator in order to overcome the two limitations of that method and to test the validity of using second lags as instruments. In moving to the SEM setup, the original five-point approval measure rather than the binary approval measure is used as the measure of presidential support. Doing so produces an intuitively appealing symmetry between the measures of presidential approval and economic assessments. It also demonstrates that our findings are robust to the different measures of presidential approval used. We shall also, shortly, discuss the extension of this analysis to other presidential support measures—PID and vote. The SEM set up combines equations (1) and (2), while also allowing DAPPROVEi,t and DECONi,t to be contemporaneously related in a nonrecursive manner (see Figure 1). This estimates the effect of current assessments of the economy on current approval controlling not only for past approval but also the possible nonrecursive relationship between current approval and current assessments of the economy. At the same time, it estimates the effect of current approval on current assessments of the economy controlling not only for past assessments of the economy but also the possible nonrecursive relationship between current assessments of the economy and current approval.9 In specifying the SEM, correlations are allowed between
9
There are two important identifying restrictions: (1) past economic evaluations do not cause current approval, once we control for the effect of current economic evaluations on current approval; and (2) past approval does not cause current economic evaluations, once we control for the effect of current approval on current economic evaluations.
reversing the causal arrow
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F IGURE 1 Structural Equation Model
the errors as would be expected from the previous description—specifically, (y i,t 2 yi,t21) is correlated t 5 2004, with (y i,t21 2 yi,t22).10 [Note: t21 5 2002, t22 5 2000. yi;t yi;t1 y i;t yi;t1 is represented eecon0402 in the as eaprv0402 eaprv0402 and eecon0402 SEM model; and y i;t1 y i;t2 yi;t1 yi;t2 is represented as eaprv0200 eaprv0200 and eecon0200 eecon0200 in the SEM model.] The SEM depicted in Figure 1, relies on the fact that the second lags (t22) of APPROVEi,t and ECONi,t are exogenous and, therefore, appropriate instruments. The online appendix describes the test we used to determine that this is in fact the case. Only once APPROVEi,t22 and ECONi,t22 were shown to be exogenous did we proceed. This is the SEM extension of the Anderson and Hsiao estimator. It is this SEM that we use to draw our conclusions regarding the dynamics of approval and economic perceptions. This SEM setup overcomes the limitation of estimating equations (1) and (2) separately.11 10
The expected correlation would be negative.
11
In order to overcome the problem of having a categorical dependent variable with a linear function, the SEM is estimated using polychoric correlations and bootstrapped standard errors (Fox 2006). Estimating an SEM using maximum-likelihood fitting criterion and Polychoric correlations—estimated by a two-step procedure (Drasgow 1986) produces consistent estimates of the model parameters. However, the standard errors are likely incorrect. This can be rectified by computing bootstrapped standard errors.
Up to now, we have focused on a model for approval. The effect of economic perceptions on presidential approval is important in its own right, and approval is often treated as equivalent to vote and is also combined in the ‘‘VP-function’’ (Vote and Popularity, see Lewis-Beck and Paldam 2000). Nonetheless, economic models are ultimately about explaining vote itself. It is important therefore to evaluate the robustness of the inferences taken from the approval analysis, even though the use of vote as the dependent variable is problematic as the presidential candidates from whom the electorate may choose is only made clear every four years, and so respondents are only asked whom they would vote for in presidential election years. As is described below, the data used in our analysis come from individuals interviewed in 2000, 2002, and 2004. A vote intention question was asked in 2000 and 2004 but not 2002. There are a number of potential solutions to this problem, each of which has its strengths and weaknesses. The first solution is to simply note that approval is a strong proxy for vote.12 Previous research has demonstrated that to the extent that real economic conditions have an effect on vote, they primarily do so by affecting approval which may then affect vote (Pickup 2010). Certainly, vote and approval are The regression of vote intention on approval produces an R2 of 0.51. 12
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geoffrey evans and mark pickup
different but the difference is such that approval is much more readily moved by factors such as the economy, compared to vote which is more rigid. This means that using approval, rather than vote, increases the chances of estimating an effect for economic perceptions. This in itself provides a strong justification for using approval as a proxy for vote. A second solution is to consider an alternative proxy for vote—party identification. Like approval, PID is a strong predictor of vote.13 Lewis-Beck, Nadeau, and Elias (2008) follow this approach. While we do not believe this is a satisfactory solution, we do estimate the SEM using PID rather than approval to demonstrate that results are robust to the dependent variable used. A third solution is to impute a value for vote in 2002 using both approval and PID. This allows us to use the vote data that we do have for 2000 and 2004 and to use the information we have regarding the strength of approval and PID as predictors of vote from these two years.14 Using the actual vote intention data from 2000 and 2004 has its obvious advantages. The imputed value for 2002 is simply a weighted combination of the approval and PID data, where the weights are based on information from 2000 and 2004 and are designed to maximize the utility of approval and PID as predictors of vote. We follow this approach using multiple imputations for the 2002 vote in order to incorporate the uncertainty in using imputed values into the confidence intervals for the coefficients estimated in the SEM.15
Data and Estimation The data are from the 2000-2002-2004 National Election Study. Respondents were first questioned by the National Election Study in the fall of 2000 and then again in the fall of 2002. The sample was interviewed one last time, at the climax of the 2004 presidential campaign. The approval variable at lags of 0, 1, and 2 was operationalized using responses to the questions: ‘‘Do you APPROVE or DISAPPROVE of the way George W. Bush (Bill Clinton) is HANDLING HIS JOB AS PRESIDENT?’’ and as appropriate ‘‘Do you [approve/ disapprove] STRONGLY or NOT STRONGLY?’’ This
produced a 5-point scale of approval from: ‘‘Disapprove strongly’’ to ‘‘Approve strongly.’’16 As there was a change in the presidency in 2000 from a Democrat to a Republican, there is a negative relationship between approval in 2000 and in the subsequent 2002 time point. This is intuitive as it suggests that on average those that were more likely to approve of Clinton in 2000 were less likely to approve of Bush in 2002. In order to account for the change in President, the 2000 approval variable was reverse coded. The resulting 2000 approval measure predicts 2002 approval about as well as the 2002 approval measure predicts 2004 approval.17 The ECONi,t variable at lags of 0, 1, and 2 was created using responses to the questions: ‘‘Now thinking about the economy in the country as a whole, would you say that OVER THE PAST YEAR the nation’s economy has gotten better, stayed about the same, or gotten worse?’’ and as appropriate ‘‘Much better or somewhat better?’’ or ‘‘Much worse or somewhat worse?’’ This produces a five-point scale from: ‘‘Much worse’’ to ‘‘Much better.’’ Each lag represents a time point two years previous. The vote variable for 2000 and 2004 is based on the usual question asking respondents for whom they intend on voting, while the 2002 values are imputed. As for the PID variable, it was created using responses to the questions: ‘‘Generally speaking, do you think of yourself as a Republican, a Democrat, an Independent, or what?’’ and as appropriate: ‘‘Would you call yourself a strong Democrat/Republican or a not very strong Democrat/Republican?’’ Like the approval variable, PID is measured on a 5-point scale and includes the categories strong Democrat, weak Democrat, Independent, weak Republican, and strong Republican.
Results The results of estimating equations (1) and (2) for approval using the Anderson and Hsiao estimator are presented in Tables 2 and 3. The tables also present the results of: (1) estimating the effect of economic assessments on approval without controlling for past 16
13
The regression of vote intention on PID produces an R2 of 0.50.
17
14
The regression of vote intention on PID and approval produces an R2 of 0.62. 15
Approval is operationalized as a 5-point scale where those that respond with ‘‘don’t know’’ to the approval question are coded as the middle neutral value (3).
Five datasets were imputed as is common practice.
The regression of 2002 approval on 2000 approval produces an estimated coefficient of 0.37 and an R2 of 0.15. The regression of 2004 approval on 2002 approval produces an estimated coefficient of 0.42 and an R2 of 0.17.
reversing the causal arrow
1245
values of approval and (2) the effect of approval on economic assessments without controlling for past values of economic assessments. Without controlling for past approval, the effect of economic assessments on presidential approval is a significant 0.27. This suggests that a one unit change in the 5-point economic assessment scale produces a 0.27 change in the probability of approving of the President—a large effect. The estimated effect of approval on economic assessments is a significant 1.15. If endogeneity from unobserved individual-specific time-invariant effects (hi) is an issue then the estimated effects will be biased. There are also strong theoretical reasons to believe that current presidential approval is in part a function of past presidential approval and that past presidential approval may also influence current economic assessments. It is for this reason that the lag of approval is required. For similar reasons, the lag of economic assessments is required in the economic perceptions model. The results of the Anderson and Hsiao estimate of these models are presented in the last columns of Tables 2 and 3. Once the lag of presidential approval is included in the approval model, the estimated effect of economic assessments on approval becomes clearly nonsignificant. Controlling for past presidential approval eliminates the effect of current economic assessments on current presidential approval. As for the effect of current presidential approval on current economic assessments, the results show it remains significant even after controlling for past economic assessments. The estimated effect of current presidential approval on current economic assessments, controlling for past economic assessments, is a statistically significant 0.40. The results also revealed that past economic assessments have no effect on current economic assessments once we control for the partisan conditioning of economic perceptions. It should also be noted that the effect of past approval on current approval is signifiT ABLE 2
Approval as a Function of Economic Perceptions No control For Approvalt21 Coefficient
Econ t21 Approval Intercept N
0.266** – 1.363 827
Robust SE
Control for Approvalt21 A&H Coefficient
0.010 – 0.036
**Significant at the 95% confidence level
0.006 20.188** 20.114** 789
Robust SE 0.012 0.040 0.020
T ABLE 3
Economic Perceptions as a Function of Approval No control for Econt21 Coefficient
Econt21 Approval Intercept N
– 1.145** 3.804 827
Robust SE
Control for Econt21 A&H Coefficient
– 0.060 0.044
0.023 0.400** 20.701** 799
Robust SE 0.031 0.121 0.070
**Significant at the 95% confidence level
cant and negative. When interpreting this coefficient, it must be kept in mind that this is the effect of past approval once individual, time-invariant differences have been removed. In absolute terms, an individual’s approval from one period is positively correlated with their approval in the next. However, once we partial out all of the time-invariant characteristics that produce this positive over-time correlation, we are left with a moderately small and negative relationship. This relationship indicates that when moved away from their equilibrium level of approval (determined by the time-invariant characteristics), individuals will return to their equilibrium but in doing so will overcompensate slightly. For example, an individual that is moved above their equilibrium level of approval (maybe due to a policy announcement) will eventually return to their equilibrium after first overcompensating by expressing a slightly below equilibrium level of approval. The picture that emerges is one of economic assessments being formed (or at least influenced) by presidential approval. These economic assessments, in turn, have no independent effect on presidential approval or further economic assessments. However, as this analysis has been run as two separate models, the effect of economic assessments on presidential approval controlling for the effect of both past and current approval on current economic assessments have not been estimated. Neither have the effects of presidential approval on economic assessments controlling for the effect of both past and current economic assessments. This is equivalent to saying that the direction of the relationship between current economic assessments and current approval remains unknown. This is rectified by estimating the SEMs depicted in Figure 1.18. 18 We might also like to include the reciprocal relationship between economic perceptions and approval at t-2 in the SEM. This would require the panel data to have additional waves.
1246 T ABLE 4
geoffrey evans and mark pickup Structural Equation Model of Approval and Economic Perceptions – year 2000 Instruments SEM
D D D D
Econ04-02 / D Approve 04-02 Approve 04-02 / D Econ04-02 Econ02-00 / D Econ04-02 Approve02-00 / D Approve 04-02
e(D Approve02-00 )/ D Approve04-02) e(D Econ 02-00 )/ D Econ04-02) N
Coefficient
Bootstrapped SE
20.029 0.178 ** 0.020 20.246** Error Correlation 20.148 20.272**
0.070 0.063 0 .044 0.058 Bootstrapped SE 0.042 0.024 800
SEM Diagnostics Model Chisquare 5 367.16 Df 5 4 Pr(.Chisq) 5 0 Chisquare (null model) 5 1923.5 Df 5 15 Goodness-of-fit index 5 0.88615 Adjusted goodness-of-fit index 5 0.60151 Bentler CFI 5 0.85775 BIC 5 327.41 **Significant at the 95% confidence level Note: D Econ04-02 5 ECON04 – ECON02; D Econ02-00 5 ECON02 – ECON00; D Approve 04-02 5 APRV04 - APRV02; and D Approve 02-00 5 APRV02 - APRV00
The findings are presented in Table 4, along with model diagnostics.19 The interpretation of these results is straight forward. Economic assessments have no contemporaneous effects on presidential approval, once contemporaneous effects of approval and past effects of economic assessments on current economic assessments are controlled. However, presidential approval does have a contemporaneous effect on economic assessments, even after controlling for the potential contemporaneous effects of economic assessments and past effects of approval on current approval. In summary, the direction of causation is clearly from presidential approval to economic assessments. The magnitude of the effect of approval on economic perceptions is 0.18 where both are measured on 5-point scales. The results also reveal that once presidential approval is controlled, past economic assessments no longer have an independent effect on current economic assessments. Economic assessments are so determined by partisan sentiments that changes in assessments are purely a function of changes in presidential approval. The same is not true for the effect of past approval on current approval, which is clearly significant. These results are consistent with those from the separately 19 The RMSEA index is not reported as its calculated confidence intervals will likely be incorrect for polychoric correlations. Overall, the diagnostics suggest a moderate fit to the data.
estimated models of approval and economic assessments and provide strong collaborative evidence that economic assessments and changes in economic assessments are largely a function of presidential approval. Table 5 presents the results from the SEM estimation using PID in place of approval. The results corroborate those obtained using approval. Economic perceptions have no effect on PID, while PID does have an effect on economic perceptions. The magnitude of the effect of PID on economic perceptions is 0.14, where both are measured on 5-point scales—compare this with the estimated effect of 0.18 using approval. Finally, Table 6 presents the results from the SEM using vote as the measure of presidential support. For 2000 and 2004, the values used are those directly measured by the vote intention question. For 2002, the values of vote are imputed from 2002 levels of approval and PID. The imputation is based on the relationship between these variables and vote determined by the data in 2000 and 2004. Multiple 2002 values of vote were imputed and the SEM was run for each imputation. The confidence intervals for the estimated coefficients presented in Table 6 incorporate the variance across these imputations. The results are again very consistent with those from the SEMs using approval and PID. Economic perceptions have no effect on vote intention but vote intention does have an effect on economic perceptions. The magnitude of this effect is 0.16, where economic perceptions
reversing the causal arrow T ABLE 5
1247
Structural Equation Model of PID and Economic Perceptions – year 2000 Instruments SEM Coefficient
D Econ04-02 / D PID 04-02 D PID 04-02 / D Econ04-02 D Econ02-00 / D Econ04-02 D PID 02-00 / D PID 04-02
e(D PID 02-00 )/ D PID 04-02) e(D Econ 02-00 )/ D Econ04-02) N
Bootstrapped SE 0.076
20.117 0.141 **
0.069
0.025
0 .047
2 0.432 **
0.047
Error Correlation 0.036
Bootstrapped SE 0.115
20.278**
0.023 756
SEM Diagnostics Model Chisquare 5 211.9 Df 5 6 Pr(.Chisq) 5 0 Chisquare (null model) 5 1648.6 Df 5 15 Goodness-of-fit index 5 0.93076 Adjusted goodness-of-fit index 5 0.75767 Bentler CFI 5 0.87396 BIC 5 171.5 **Significant at the 95% confidence level Note: D Econ04-02 5 ECON04 – ECON02; D Econ02-00 5 ECON02 – ECON00; D PID 04-02 5 PID04 - PID02; and D PID 02-00 5 PID02 - PID00
are measured on a 5-point scale and vote is a binary variable (vote for Bush 5 1, otherwise 5 0). This suggests a somewhat smaller effect for vote on economic perceptions compared to approval or PID but overall the results are highly consistent and robust. Vote intention, presidential approval and partisan identification all have an effect on individual economic perceptions, while none of these are influenced by economic perceptions.
Discussion and Conclusions These findings support the argument that individuals’ perceptions of the macro economy do not explain their political preferences, in fact the direction of causality is reversed: economic perceptions are derived from political preferences. In the U.S. case, as with British data, the effects of presidential approval
(lagged or contemporaneous) on macro-economic perceptions are consistently stronger than the reciprocal effects of concurrent economic perceptions on incumbent support. Sociotropic, or macro-economic, perceptions should not therefore be treated as exogenous variables in individual level models of approval and vote. We also find that although all our measures of political preferences—vote, party identity, and presidential approval—behave similarly, approval influences economic perceptions more strongly and is less closely related to issues such as Iraq and the abortion question. This is consistent with the findings of van der Eijk et al.’s (2007) cross-sectional, individual-level analysis of EU surveys which finds that the economy does matter at the level of individual voters and that it does affect vote choice. It does so, however, not through its impact on subjective assessments of the economy but through its effect on approval. Thus of our three political preference indicators approval would appear to have the most central role, but in the opposite causal direction to that envisaged in conventional accounts of economic voting. The analysis therefore challenges the individuallevel assumptions of the dominant economic voting model and points to the role of political factors as explanations of perceptions and behavior even in the U.S. context, where much economic voting research supporting the conventional view has been undertaken. Nevertheless, there are, arguably, limitations on the robustness of the findings which we consider in turn. First, it could be argued that the lag-structure of economic perceptions is such that their effects cannot be observed concurrently. The economy’s consequences for political preferences take time to coalesce. But this would seem to be inconsistent with work which examines both lagged and contemporaneous economic effects and shows the former to be substantially weaker than the latter (Evans and Andersen 2006).20 Second, as we have argued elsewhere (Evans 1999; Evans and Andersen 2006), it seems likely that in a relatively stable and healthy economic situation, the economy may matter less for political choices than other factors. The U.S. case during 2000–2004 would appear to fall into this category of moderately positive economic situations. Moreover, given the shock of the 9/11 attacks and the massive rally effect in presidential approval, any economic effect may have been even further weakened. If that situation should change for the worse, as it has since done, concern about the economy could reemerge as an 20
Analyses available from the authors replicate these findings.
1248 T ABLE 6
geoffrey evans and mark pickup Structural Equation Model of Vote and Economic Perceptions – year 2000 Instruments SEM
D D D D
Econ04-02 / D Vote04-02 Vote 04-02 / D Econ04-02 Econ02-00 / D Econ04-02 Vote 02-00 / D Vote 04-02
e(D Vote 02-00 )/ D Vote 04-02) e(D Econ 02-00 )/ D Econ04-02) N
Coefficient
Bootstrapped 95% CI*
0.012 0.162** 0.032 20.630** Error Correlation 20.184** 20.278**
20.038, 0.062 0.085, 0.243 20.061, 0.127 20.725, 20.522 Bootstrapped SE 20.253, 20.130 20.334, 20.226 629
SEM Diagnostics Model Chisquare 5 204.26 Df 5 6 Pr(.Chisq) 5 0 Chisquare (null model) 5 2176.1 Df 5 15 Goodness-of-fit index 5 0.91448 Adjusted goodness-of-fit index 5 0.7007 Bentler CFI 5 0.90826 BIC 5 165.60 *The results are bootstrapped across the five imputed datasets, so that the 95% confidence intervals incorporate the uncertainty inherent in imputing the 2002 values of vote intention. **Significant at the 95% confidence level Note: D Econ04-02 5 ECON04 – ECON02; D Econ02-00 5 ECON02 – ECON00; D Vote 04-02 5 VOTE04 - VOTE02; and D Vote 02-00 5 VOTE02 - VOTE00
exogenous basis of voters’ political choices. More research is required into the conditions under which the exogeneity of economic perceptions is more or less pronounced, much as researchers have identified institutional influences on the strength of association between economic performance and incumbent support. However, given the growth patterns of most economies in established democracies we are probably safe in concluding that in most circumstances cross-sectional studies using measures of sociotropic economic perceptions overestimate the effect of the economy on voting. Strikingly, as we saw in Table 1, the use of simple models of economic effects that fail to take into account adequately the political conditioning of economic perceptions would have lead us to believe, counterintuitively, that even in 2000–2004 the economy was the strongest factor influencing vote choice. Third, our measures of retrospective economic perceptions do not encompass recent variants of economic theories such as those pertaining to economic management competence (Duch and Stevenson 2008; Sanders 1996). This is not particularly restrictive however as our measures do link directly to the theoretical literature on retrospective economic voting as it has been most typically conceived and operationalized. Moreover, the use of items that explicitly evaluate incumbent performance on the economy
arguably raise the question of direction of causality even more clearly than do the more commonly used and less direct formulations examined here. What do our cautionary findings imply for researchers who seek to understand the nature of the relations between voters’ understanding of the economy and their political preferences? One response is to examine the processes that underpin the observed aggregate-level linkages between economic perceptions and government approval. A key candidate in this respect is the media, where an increasing number of studies point to its role in linking factors such as individual economic well-being, objective national economic indicators and perceptions of the national economy (eg. Funk and Garcı´a-Monet 1997; Hetherington 1996; Mutz 1998; Sanders and Gavin 2004; see Anderson 2007 for an extensive review). In addition to establishing the presence of concurrent linkages of these sorts, however, researchers would also need to specify the conditions under which the endogeneity of economic perceptions is more or less pronounced, and where economic perceptions influence political preferences as well as vice versa. As of yet, such an approach is not well-developed. An alternative response has been to follow Kramer’s (1983) lead with respect to the problems of crosssectional survey estimates and to retreat from an
reversing the causal arrow individual to a purely macro-level of analysis (Erikson 2004). While we have demonstrated that individual level, cross-sectional differences in economic perceptions do not explain individual differences in government support, it remains possible that aggregate, overtime movements in economic evaluations may affect aggregate government support. However, DeBoef and Kellstedt (2004) and Freeman et al. (1998) indicate that macro-level indicators such as the Index of Consumer Sentiment are themselves endogenous to government approval. A more promising strategy could instead be to examine more carefully how different sectors of the electorate, and different electorates, are responsive to differing economic information. There is some evidence of this—Conover, Feldman, and Knight (1986, 1987) find that while respondents are generally uninformed about economic facts, they do pick up on aspects of economic change such as unemployment. Other studies argue that the types of information that influence economic voting differ across societies—so that in the United Kingdom, for example, ‘‘interest rates’’ on household mortgages provide a particularly strong signal for the electorate (Sanders 2000; Sanders and Gavin 2004). If the appropriateness of commonly used measures of highly generalized economic perceptions is questionable, a similar concern accompanies the operationalization of retrospective economic perceptions as a single item. In related areas of political perception/ attitude measurement, such as the study of political values (Ansolabehere, Rodden, and Snyder 2008; Evans, Heath, and Lalljee 1996), there has been extensive development of reliable and valid multiple indicator measures that give some protection against spuriousness. Similar attempts with respect to the measurement of economic perceptions—Conover, Feldman, and Knight (1986, 1987)—have not yet been incorporated into general usage. For the time being therefore, as Erikson conjectured: ‘‘Micro-level evidence regarding economic voting must be regarded with some suspicion, because of the dubious assumption that evaluations of the economy are exogenous to vote choice. To the extent that the presumptive dependent variable— vote choice—also influences survey responses about the state of the economy, the evidence for economic voting is biased and exaggerated’’ (2004, 2).
Acknowledgments We would like to thank the editors and anonymous reviewers for their helpful comments and suggestions.
1249 Mark would also like to thank the Centre for Research Methods in the Social Sciences for providing the necessary resources for him to work on this project, while at the University of Oxford. Manuscript submitted 1 January 2009 Manuscript accepted for publication 2 March 2010
References Achen, Christopher H. 2000. ‘‘Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables.’’ Presented at the annual meeting of Political Methodology, Los Angeles. Anderson, Christopher J. 2000. ‘‘Economic Voting and Political Context: A Comparative Perspective.’’ Electoral Studies 19 (2/3): 151–70. Anderson, Christopher J. 2007. ‘‘The End of Economic Voting? Contingency Dilemmas and the Limits of Democratic Accountability.’’ Annual Review of Political Science 10: 271–96 Anderson, Christopher J., Silvia M. Mendes, Yuliya V. Tverdova, and Haklin Kim. 2004. ‘‘Endogenous Economic Voting: Evidence from the 1997 British Election.’’ Electoral Studies 23 (4): 683–708. Anderson, T. W., and Cheng Hsiao. 1981. ‘‘Estimation of Dynamic Models with Error Components.’’ Journal of the American Statistical Association 76: 598–606. Anderson, T. W., and Cheng Hsiao. 1982. ‘‘Formulation and Estimation of Dynamic Models Using Panel Data.’’ Journal of Econometrics 18: 47–82. Arellano, Manuel. 2003. Panel Data Econometrics. Oxford: Oxford University Press. Ashenfelter, O.. 1978. ‘‘Estimating the Effect of Training Programs on Earnings.’’ Review of Economics and Statistics 60: 47–57. Ansolabehere, Stephen, Jonathan Rodden, and James M. Snyder. 2008. ‘‘The Strength of Issues: Using Multiple Measures to Gauge Preference Stability, Ideological Constraint, and Issue Voting.’’ American Political Science Review 102: 215–32. Baltagi, Badi H. 2005. Econometric Analysis of Panel Data. 3rd ed. Chichester, West Sussex, UK: John Wiley & Sons Ltd. Bartels, Larry M. 1996. ‘‘Uninformed Votes: Information Effects in Presidential Elections.’’ American Journal of Political Science 40 (2): 194–230 Bartels, Larry M. 2002. ‘‘Beyond the Running Tally: Partisan Bias in Political Perceptions.’’ Political Behavior, 24 (1): 117–50. Berelson, R, P. F. Lazarsfeld, and W. N. McPhee. 1954. Voting: A Study of Opinion Formation in a Presidential Campaign. Chicago: University of Chicago Press. van der Brug, Wouter, Cees van der Eijk, and Mark N. Franklin 2007. The Economy and the Vote: Economic Conditions and Elections in Fifteen Countries. Cambridge: University Press. Campbell, Angus, Philip Converse, Warren Miller, and Donald Stokes. 1960. The American Voter. New York: Wiley and Sons. Conover, Pamela S., Stanley Feldman, and Kathleen Knight. 1986. ‘‘Judging Inflation and Unemployment: The Origins of Retrospective Evaluations.’’ Journal of Politics 48 (3): 565–68. Conover, Pamela J., Stanley Feldman, and Kathleen Knight. 1987. ‘‘The Personal and Political Underpinnings of Economic
1250 Forecasts.’’ American Journal of Political Science 31 (3): 559–83. DeBoef, Suzanna, and Paul M. Kellstedt. 2004. ‘‘The Political (and Economic) Origins of Consumer Confidence.’’ American Journal of Political Science 48 (4): 633–49. Drasgow, F. 1986. ‘‘Polychoric and Polyserial Correlations.’’ In The Encyclopedia of Statistics, Vol. 7, eds. S. Kotz and N. Johnson. Boston: Wiley, 68–74. Duch, Raymond M., Harvey D. Palmer, and Christopher J. Anderson. 2000. ‘‘Heterogeneity in Perceptions of National Economic Conditions.’’ American Journal of Political Science 44 (4): 635–52. Duch, Raymond M., and Randy Stevenson, 2008. Voting in Context: How Political and Economic Institutions Condition Election Results. Cambridge: Cambridge University Press. van der Eijk, Cees, Mark N. Franklin, Froukje Demant, and Wouter van der Brug. 2007. ‘‘The Endogenous Economy: ‘Real’ Economic Conditions, Subjective Economic Evaluations and Government Support.’’ Acta Politica 42 (1): 1–22. Erikson, Robert S. 2004. ‘‘Macro vs. Micro-Level Perspectives on Economic Voting: Is the Micro-Level Evidence Endogenously Induced?’’ Presented at the 2004 Political Methodology Meetings, Stanford University. Erikson, Robert S, Michael B. MacKuen, and James A. Stimson. 2002. The Macro Polity. New York: Cambridge University Press. Evans, Geoffrey. 1999. ‘‘Economic and Politics Revisited: Exploring the Decline in Conservative Support, 1992--95.’’ Political Studies 47 (1): 139–51. Evans, Geoffrey, and Robert Andersen. 2003. ‘‘Endogenizing the Economy: Political Preferences and Economic Perceptions across the Electoral Cycle.’’ Revue de la Maison Francaise 1: 117–44. Evans, Geoffrey, and Robert Andersen. 2006. ‘‘The Political Conditioning of Economic Perceptions.’’ Journal of Politics 68 (1): 194–207. Evans, G., A. F. Heath, and M. G. Lalljee. 1996. ‘‘Measuring LeftRight and Libertarian-Authoritarian Values in the British Electorate.’’ British Journal of Sociology 47 (1): 93–112. Fiorina, Morris. 1981. Retrospective Voting in American National Elections. New Haven: Yale University Press. Fox, John. 2006. ‘‘Structural-Equation Modeling with the sem Package in R.’’ Structural Equation Modeling 13: 465–86. Freeman, John, Daniel Houser, Paul Kellstedt, and John T. Williams. 1998. ‘‘Long Memoried Processes, Unit Roots, and Causal Inference in Political Science.’’ American Journal of Political Science 42 (4): 1289–1327. Funk, Carolyn L., and Patricia A. Garcı´a-Monet. 1997. ‘‘The Relationship between Personal and National Concerns in Public Perceptions about the Economy.’’ Political Research Quarterly 50 (2): 317–42. Gerber, Alan S., and Donald Green. 1999. ‘‘Misperceptions about Perceptual Bias.’’ Annual Review of Political Science 2: 189– 210. Green, Donald, Bradley Palmquist, and Eric Schickler. 2002. Partisan Hearts and Minds. New Haven, CT: Yale University Press. Greene, William H. 2003. Econometric Analysis. 5th ed. Upper Saddle River, NJ: Prentice Hall. Haller, H. Brandon, and Helmut Norpoth. 1997. ‘‘Reality Bites: News Exposure and Economic Opinion.’’ Public Opinion Quarterly 61 (4): 555–75.
geoffrey evans and mark pickup Hausman, J. A. 1978. ‘‘Specification Tests in Econometrics.’’ Econometrica 46: 1251–73. Hetherington, Marc J. 1996. ‘‘The Media’s Role in Forming Voters’ National Economic Evaluations in 1992.’’ American Journal of Political Science 40 (2): 372–95. Hsiao, Cheng. 2003. Analysis of Panel Data. Cambridge: Cambridge University Press. Hurwicz, Leonid. 1950. ‘‘Least-Squares Bias in Time Series.’’ In Statistical Inference in Dynamic Economic Models, ed. Tjalling C. Koopman. New York: John Wiley & Sons, 365–83. Johnston, Ron, R. Sarker, K. Jones, A. Bolster, C. Propper, and S. M. Burgess. 2005. ‘‘Egocentric Economic Voting and Changes in Party Choice: Great Britain, 1992--2001.’’ Journal of Elections, Political Opinion and Parties 1: 129–44. Keele, Luke, and Nathan J. Kelly. 2006. ‘‘Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables.’’ Political Analysis, 14 (2): 186–205. Kinder, Donald R., and D. Roderick Kiewiet. 1979. ‘‘Economic Discontent and Political Behavior: The Role of Personal Grievances and Collective Economic Judgments in Congressional Voting.’’ American Journal of Political Science 23 (3): 495–527. Kinder, Donald, Gordon S. Adams, and Paul W. Gronke. 1989. ‘‘Economics and Politics in the 1984 American Presidential Election.’’ American Journal of Political Science 33: 491– 515. Kramer, Gerald. 1983. ‘‘The Ecological Fallacy Revisited: Aggregate vs. Individual-Level Findings on Economics and Elections, and Sociotropic Voting.’’ American Political Science Review 77 (1): 92–111. Krause, George. 1997. ‘‘Voters, Information Heterogeneity, and the Dynamics of Aggregate Economic Expectations.’’ American Journal of Political Science 41 (3): 1170–1200. Ladner, Matthew, and Christopher Wlezien 2007. ‘‘Partisan Preferences, Electoral Prospects, and Economic Expectations.’’ Comparative Political Studies 40: 571–96. Lewis-Beck, Michael S. 2006. ‘‘Does Economics Still Matter? Econometrics and the Vote.’’ Journal of Politics 68 (1): 208–12. Lewis-Beck, Michael S., and Martin Paldam. 2000. ‘‘Economic voting: An introduction’’. Electoral Studies 19 (2):113–22 Lewis-Beck, Michael S., and Mary Stegmaier. 2000. ‘‘Economic Determinants of Electoral Outcomes.’’ Annual Review of Political Science 3: 183–219. Lewis-Beck, Michael S., and Mary Stegmaier. 2007. ‘‘Economic Models of the Vote.’’ In The Oxford Handbook of Political Behavior, ed. Russell Dalton and Hans-Dieter Klingemann. Oxford: Oxford University Press, 518–37. Lewis-Beck, Michael, S. Richard Nadeau, and Angelo Elias, 2008. ‘‘Economics, Party, and the Vote: Causality Issues and Panel Data.’’ American Journal of Political Science 52 (1): 84–95. Mutz, Diana C. 1998. Impersonal Influence: How Perceptions of Mass Collectives Affect Political Attitudes. Cambridge: Cambridge University Press. Nadeau, Richard, and Michael S. Lewis-Beck. 2001. ‘‘National Economic Voting in U.S. Presidential Elections.’’ Journal of Politics 63 (1): 159–81. Nickell, Stephen. 1981. ‘‘Biases in Dynamic Models with Fixed Effects.’’ Econometrica 49, 1417–26. Pickup, Mark. 2010. ‘‘Better Know Your Dependent Variable: A Multination Analysis of Government Support Measures in Economic Popularity Models.’’ British Journal of Political Science 40: 449–68.
reversing the causal arrow Sanders, David. 1996. ‘‘Economic Performance, Management Competence, and the Outcome of the Next General Election.’’ Political Studies 44 (1): 203–31. Sanders, David. 2000. ‘‘The Real Economy and the Perceived Economy in Popularity Functions: How Much Do Voters Need to Know? A Study of British Data, 1974--97.’’ Electoral Studies 19 (2–3): 275–94. Sanders, David, and Neil T. Gavin. 2004. ‘‘Television News, Economic Perceptions and Political Preferences in Britain, 1997--2001.’’ Journal of Politics 66 (4): 1245–66. Weatherford, M. Stephen. 1978. ‘‘Economic Conditions and Electoral Outcomes: Class Differences in the Political Response to Recession.’’ American Journal of Political Science 22 (4): 917–38. Wilcox, Nathaniel T., and Christopher Wlezien. 1993. ‘‘The Contamination of Responses to Survey Items: Economic perceptions and Political Judgments.’’ Political Analysis 5 (1): 181–213.
1251 Wooldridge Jeffrey M. 2006. Introductory Econometrics: A Modern Approach. 3rd ed. Independence, KY: South-Western Cengage Learning. Wlezien, Christopher, Mark Franklin, and Daniel Twiggs. 1997. ‘‘Economic Perceptions and Vote Choice: Disentangling the Endogeneity.’’ Political Behavior 19 (1): 7–17. Zaller, John R. 1992. The Nature and Origins of Mass Opinion. New York: Cambridge. University Press.
Geoffrey Evans is Professor and Official Fellow, Nuffield College, Oxford, OX1 1NF, United Kingdom. Mark Pickup is an Assistant Professor of Political Science, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, V5A 1S6, Canada.
