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An alternative approach to explaining political popularity
Electoral&dies (1985). 4:3. 231-139
An Alternative Approach to Explaining Political Popularity K. HOLDEN* Department o/Economic and Business Studies, University of Liverpool, England
D. A. PEEL* Department of Economics, University College of Wales, Aberystwyth,
UK
Previous empirical work on the relationship between political popularity and economic events has either not attempted to model the alternative policies of the different parties, or has modelled them in a rather simplistic manner. It has also typically assumed that voters are backward-looking in contrast to recent work on expectations theory. An approach is outlined which is based on a forward-looking comparison of the parties and incorporates the effect of news. An alternative derivation relying on the evaluation of the stock of goodwill built up for each party is also suggested. Empirical evidence from the Gallup opinion poll provides some support for these hypotheses.
In recent years there has been a considerable amount of empirical work devoted to examining the relationship between economic events and political popularity. (See, for example, Downs, 1957; Borooah and van der Ploeg, 1982a, 1982b; Fair, 1978; Fiorina, 1981; Frey and Schneider, 1978a, 1978b; Kramer, 1971; Minford and Peel, 1982; Pissarides, 1980; Stigler, 1973; Whiteley, 1984.) It would appear from this work that the impact of economic variables on political popularity is unstable. In some studies the actual rate of inflation, unemployment or disposable real income (in levels or rates of change) appear with significant coefficients, but in other studies, for different time periods, these variables cease to be significant. This apparent instability of economic variables in the popularity functions could be a consequence of a number of factors. First, either little attempt has been made at modelling the alternative policies of different parties and the voters’ choice between these alternatives in previous work, or the opposition parties’ policies are modelled in a relatively simplistic manner by, for example, using a trend. (See, for example, Minford and Peel, 1982; Paldam and Schneider, 1980.) Thus typically, for example, the lead (or deficit) of the party in power over the main opposition (or the proportion voting for a particular party) is made a function of the economic variables, but not of the voters’ evaluation of the difference in policies. Second, it seems quite obvious, a priori, that there are many non-economic issues such as
*We are grateful to an anonymous referee and the Editor for helpful comments on an earlier draft. We are also grateful to the University of Liverpool Research Committee for financial support, and to Jane Edwards for research assistance. Any errors are the responsibility of the authors. 0261-3794/85/03/0231-091%03.00
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An Aiterndve
Approach
to Explaining
Political Popuiarity
defence, capital punishment and immigration which are of concern to voters. It is not clear that representing the effects of these issues by a simple random disturbance term (as in the studies previously cited) is an appropriate modelling procedure. Third, the popularity of a particular government is usually assumed to depend on the current or past values of economic variables rather than on anticipations or expectations of the future value of the variables. When expectations are explicitly included they are modelling with a simple backward looking function (for example. adaptive-see e.g., Nordhaus, 1975). To the extent that expectations are modelled, the assumed dependence of popularity on the current or past values of economic variables implies that espectations are formed in an adaptive manner-that is, as a mechanistic extrapolation of current or past values of variables. Recently various authors have incorporated rational expectations in a number of behavioural functions such as consumption, interest rates or wages (see, for example, Hall, 1978; Fama, 1975; Minford and Brech, 1981). The basic idea of the rational expectations hypothesis as outlined by Muth, 1961 is that expectations are informed predictions of future events and are essentially the same as the predictions of the correct economic theory. The rational expectations hypothesis has a number of implications for the ex post properties of expectations. First, expectations will be unbiased predictions or forecasts of the future value of a variable. Second, the forecasts will embody all relevant information available at the time the forecast is made. This implies that the forecast error will be uncorrelated or orthogonal to the information set. If this property does not hold then the forecast could have been improved by using this information. Third, the forecast errors will be serially uncorrelated after due allowance for the forecast horizon has been made. (See, e.g., Brown and hlaital, 1981.) All of these will only all hold if agents are assumed to form expectations as if they know the true model of the economy.’ Since there is no consensus amongst economists as to the correct model of the economy, as evidenced by competing macroeconomic models such as Cambridge or Liverpool, it is a priori unlikely that the rational expectations hypothesis can be literally true. Nevertheless the hypothesis does demand that we give explicit consideration to the information set that agents possess when expectations are formed. From this perspective it is clear that the great majority of agents within an economy are not expert econometricians who are able to produce their own rational forecasts. However. we should recognize that there is access through the media, at very low marginal cost, to a great variety of informed opinion such as the forecasts of bodies like the National Institute or the London Business School, and the views of specialist economic correspondents on the news and documentary programmes. Given these sources of information it becomes more reasonable to assume that expectations of voters may be based on a much richer information set than that given simply by the past history of a variable as is implied by the adaptive expectations hypothesis. Also, as mentioned above, a large number of authors have reported empirical results for different behavioural functions which appear to be consistent with the data generating process and in which expectations are modelled rationally. In general it seems a priori plausible to hypothesize that the expectations of the electorate of the future values of economic variables will influence voting intentions. Certainly the alternative to this would imply a degree of voter myopia which has connotations of irrationality and contradicts empirical evidence relating to, for example, saving behaviour. To the extent that these expectations do influence voting intentions modelling expectations implicitly in an adaptive manner, as in previous work, could be a potential source of misspecification. This point provides the major motivation for this study. The purpose in this paper is to present some further analysis of monthly popularity data
K. HOLDEN AND D. A. PEEL
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based on responses to the UK Gallup poll survey over the period 1956(4)-1982( 12). The major novelty of the paper is that we develop a model based on the assumption that respondents’ expectations to the survey can be assumed to be formed in a manner approximated by the rational expectations hypothesis.’ This a: ;umption enables us to incorporate the points raised about previous work noted above. Although the empirical results we report are not totally consistent with our proposed models we believe this could provide a useful basis for future work.
Theory Our approach is inspired by the work on the consumption function of Hall (1978), who shows that optimization on the part of consumers, coupled with the assumption of rational expectations, implies that consumption in life-cycle or permanent income models should change according to a random walk, and that no variable other than current consumption should help to predict future consumption. That is, the best predictor of future consumption expenditure is the current level of consumption expenditure. In the context of voting models the corresponding result is that the best predictor of government support is the current level of government support. We now develop two models which are based on Hall’s approach. In our first model we assume that voters express a preference or voting intention for that party which gives them the greater anticipated stream of future benefits from its policies. This hypothesis is in the spirit of the sentiments expressed by Tullock (1976), who writes Voters and customers are essentially the same people. Mr. Smith buys and votes: he is the same man in the supermarket and in the voting booth. There is no strong reason to believe his behaviour is radically different in the two environments. we assume that in both he will choose the product or candidate he thinks is the best bargain for him.
For simplicity we assume a two-party framework3 which is reasonable for our data period when the Conservative and Labour parties dominated the political arena in the UK. Hence, G = a(EYA - EYE) + U. . . where G = the lead or deficit of the party in power (A) over the other party (B). EYA, EYB = the expected benefit accruing if party A or party B is in power. U =a random error reflecting the impact of measurement error in the opinion polls, which is assumed to have the classical properties. a = a positive constant. Both EYA and EYB depend on non-economic as well as economic factors since decisions to: privatize an industry, increase the inflation rare, invade the Falklands or reduce unemployment benefits, may all have implications for the voter. If we now difference (1) we get: AG=aA(EYA-EYE)+
U-U_1...
(2)
Here (EYA -EYE) is the change in the evaluation of the stream of benefits from party A relative to party B. If the evaluations are formed in a rational or unbiased manner then following Hall (1978) changes occur because of new information so that: A(EYA - EYB) =
E .
where E is a random variable reflecting news. Clearly, ‘news’ is unpredictable
(3) otherwise it
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An Alternatiz~e Approdcb
to Elxplaining Political Popularity
could have been exploited previously in order to reassess EY” and EYE. Combining (3) gives: AC = a& + U-U-,...
(2) and
(4)
The residual here is a combination of two white noise error processes. Equation (4) informs us that if the joint hypotheses are correct then changes in the lead of the government is a function only of a combination of two errors: one reflecting measurement error (U-U1) and one reflecting new information or news (QE). Ignoring the measurement error term for the moment, the basic hypothesis embodied in (4) is that agents only change their decision to vote for a particular party between any two periods because of new information which has accrued between the periods. Because this information is unpredictable ex ante any lagged economic (or non-economic) variables which are part of the information set of agents at the time expectations are formed should not have any predictive power when added to equation (4). Following Harvey (1981, p. 43) it is easily shown that the combined error in (4) follows a first order moving average process. Notice that while (4) might suggest a moving average parameter of unity, the combined process has a parameter which depends on a and the variances of E and U, and so this will be different from one providing a f 0 and the variance of E is not zero. Moreover, the moving average parameter will have a negative coefficient.i The theoretical prediction of a negative moving average parameter provides an additional empirical test of the hypothesis. In the more general framework of autoregressive integrated moving average models this is an ARIMA (O,l, 1) process (see Whiteley, 1984 for further discussion) and can be written AG, = W, + 6W,_,
.
(5)
where W is a white noise error and 0 is the moving average parameter. This will be referred to as the ‘permanent benefits’ model. It can be interpreted that the best predictor of G, is G,_ , plus the error term. An alternative approach to modelling popularity, which remains very much within the spirit of our analysis, is to integrate the notion of loyalty to a party with rational expectations. We suppose that the voters’ support for a party is based on an evaluation of the relative ‘stock of goodwill’ which has accumulated for each party. Changes in the stock of goodwill only occur due to new information becoming available and due to depreciation (as a result of forgetfulness, deaths, diffusion of information, etc.). Assuming voters process information in an informed or rational manner. new information will be a random variable. This can be modelled as: G, = US, + U, and
As, = E, -
as,_,
.
(6)
..
(7)
where S is the relative stock of goodwill for the two parties. d is the depreciation rate. E and U are random errors with the classical properties. Combining (6) and (7) yields G, = (1-d)G,_,
+ as, + U, - (1-6)U,_,
.
.
(8)
As with (4), this equation has a residual which is a combination of the two error processes. Again. following Harvey (198 1, p. 43). (8) ISessentially an ARIMA (l,O,l) process with an additional white noise error which combine to give a different ARIMA (1 ,O, 1) process. The
K. HOLDEN AND
D. r\.
PEEL
235
autoregressive parameter of the combined process is (1 - 6) and the moving average parameter depends upon Q, d and the variances of E and U. As with the first model, the moving average parameter will have a negative coefficient if the hypothesis is correct. Thus (8) can be written G, = (l-6)G,-,
+ V, + yV,_,
..
(9)
where V, is a white noise error and v is the moving average parameter. Comparing the ‘permanent benefits’ approach. which gives (5), with the ‘stock of goodwill’ approach, which gives (9), the only formal difference is that due to the presence of 6 (the rate of depreciation of goodwill). In each case the level of government support, G,, is determined solely by its previous value, G,_ 1 , and the error term, and so no other variables effect G,.
Empirical
Results
We now present some empirical tests of the two models derived above. The data are collected monthly by the UK Gallup poll organization and, based on a quota sample of approximately 1,000 voters, include the percentage of respondents supporting the Conservative party and the Labour party. A preliminary examination of the data showed that the lead of the government over the major opposition was stationary. Next, we estimated univariate ARIMA (1 ,O, 1) and ARIMA (0,1,l) models for the government lead using monthly data over the period 1956(3)-1982(12). In order to check the stability of the estimated functions over time we also estimated the functions for the sub-periods 1956(j)-1968(8) and 1969(9)-1982(12). The time series results in Table 1 provide some support for both the models derived above. The constant terms are never significantly different from zero at the 5 per cent level of significance and the moving average parameter is negative as demanded by the theoretical models. The Box-Pierce statistics indicate that the residuals are random at the 5 per cent level of significance. The two models are not nested within each other and therefore we cannot discriminate between them on the basis of the test statistics reported in Table 1. An additional test of the postulated models is whether all new information is contained in the various random shocks which reflect news. If it is, and expectations are formed rationally, then any lagged variables, which are part of the information set conditioning expectations should not have a statistically significant systematic impact on government popularity when added to the time series models. We experimented with a large number of different economic and non-economic variables such as industrial production, interest rates, inflation, unemployment and ‘political’ variables (suggested by Miller and Mackie, 1973 and Frey and Schneider, 1981). Interest rates and industrial production were always insignificant. However, some significant coefficients were sometimes obtained for the other variables for the ‘goodwill’ model. We report a selection of our results in Table 2. Ordinary least squares is the method of estimation. The presence of a moving average error process implies that the least square estimates will be unbiased but the standard errors will be biased.5 However, Vinod (1976) has provided tables of critical values oft if least squares is utilized when a moving average error process is present. If the least squares estimates of the l-values lie outside the critical range, then the hypothesis tests can be conducted in the normal way. We observe from the first six results from Table 2 that for the ‘permanent benefits’ model (5) inflation, unemployment and the political variables are always insignificantly different for zero which supports this hypothesis: the change in popularity is purely random. In the ‘goodwill’ model
236
An Alternative
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TABLE 1. Time series properties
Political Popuiarity
of the Gallup data. &loving Average
Dependent Time
Period
1956(3)-82(
Variable 12)
1956(3)-69(8) 1969(9)-S2(
12)
G, -
G,-1
G, -
G,-I
G
G,-I
1956(3)-82(12)
G,
1956(3)-69(8)
G,
1969(9)-82(12)
G,
-
Constant
G,_
0.03 (0.20) -0.0s (0.26) 0.11 (0.30) -0.41 (0.25) -0.35 (0.29) -0.56 (0.39)
0.88 (0.03) 0.93 (0.03) 0.83 (0.06)
1
Parameter
R -’
BP10
-0.26 (0.05) -0.22 (0.07) -0.31 (0.08) -0.17 (0.06) -0.16 (0.08) -0.18 (0.10)
0.05
24.9
0.04
19.90
0.07
16.95
0.73
18.92
0.83
18.44
0.62
11.40
Numbers in parentheses are standard errors R -? is the corrected coefficient of determination BPis theBox and Pierce(l970)statistic with twenty degrees of freedom(5 percent critical value is 31.41) G, is the percentage lead of the government in power over the main opposition
(the last six results in Table 2), the rate of unemployment is significant in the latter period. Also the cyclical political variable (D 1) is significantly different from zero in all periods. Thus, while G, depends on G,_ I as that required by the hypothesis, the significance of D 1 contradicts the hypothesis.
Conclusions In this paper we have suggested that empirical work on the relationship between political popularity and economic events has perhaps given insufficient attention to modelling the alternative policies of different parties. Also typically it is assumed that voters’ expectations are backward looking and mechanistic in contrast to recent work on expectations theory. Two alternative approaches to modelling popularity have been proposed in which voters are assumed to process information efficiently as implied by the rational expectations hypothesis. One approach is based on a forward-looking comparison of the differences in permanent benefits generated by the parties and the other is based on an evaluation of the relative stocks of goodwill accumulated for each party. The two models imply rather similar time series representations for government popularity. The time series representations of popularity data over the period 1956(3)-1982(12) are consistent with the different approaches.6 One implication of our models is that no other lagged variables should have any systematic effect on popularity. This prediction, of course, contrasts dramatically with other ‘optimizing’ models which purport to explain popularity. These models (see, for example, Whiteley, 1984) imply that the aggregate relationship between economic conditions and government popularity should be reasonably stable and strong over time. We found that all the lagged economic and non-economic variables we experimented with had an insignificant impact on changes in popularity (the permanent benefits model) but that the cyclical political variable was always significant in the goodwill model. This finding suggests that the permanent benefits model is the more appropriate of the two ways of modelling popularity.
G, - cl-1
G, - G,-1
G, - G,-I
G, - Gt-I
G, - G/--I
G - G-1
Gf
G
Gf
Gt
Gf
G,
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)--1982(12)
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)-1982(12)
1956(3 j-1982(12)
1956(3)-1969(8)
1969(9)-1982(12)
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)--1982(12)
l
0.73 ( 18.99)a 0.74 (14.21)a 0.59 (9.3lY 0.76 (22.19)” 0.83 (20.9l)a 0.68 (11.82)’
0.91 (0.74)’ 0.73 (0.33) 0.91 (0.74)’ 0.55 (0.96)’ 0.84 (1.19)’ 0.26 (0.20)’ 2.41 (2.07) 5.92 (2.57) 1.19’ (0.61) 1.22 (2.26) 1.43 (2.11) 0.97 (1.16) l
G, _ 1
Constant
2.
-0.08 (1.24)’ -0.23 (0.96)’ 0.12 (1.08)’
0.03 (0.4 1)’ 0.15 (0.60)’ 0.03 (0.41)’
PC--1)
1)
0.24 (1.95) -1.92 (2.58) 0.50 (2.86)p
(0.09).
(0.09). -0.51 (0.70) -0.01
-0.01
w-
Dl
-0.06 (1.43)’ -0.01 (0.19)’ -0.16 (3.74Y -0.15 (2.91y’ -0.24 (3.2)a -0.15 (4.04)” -0.15 (3.33)” -0.15 (2.50)
-0.03 (0.65) -0.07 (1.36)’ -0.03 (0.65)’ -0.03 (1.06)’
l‘he influence ot other variables.
-0.05 (1.77) -0.02 (0.68)’ -0.06 (1.56)’
0.05 (0.18)’ 0.03 (0.98)’ 0.05 (0.18)’
02
-0.15 (0.62)’ 0.004 (0.13)’ -0.08 (1.97)
(0.09). -0.03 (1.24)’
-0.03 (-1.24)’ -0.003
D3
R2
2.21 2.11 2.22 2.27 2.14
0.70 0.80 0.50
2.48 2.19
2.41
2.45
2.48
2.46
2.48
DW
0.81 0.58
0 0.70
0
0
0
0
0
I values in parentheses. A high I value implies the variable effects the dependent variable. ’ indicates significantly different from zero or indicates insignificantly different from zero given the presence of a first order moving average parameter. DW = Durbin Watson Statistics. U( - 1) = Percentage rate of unemployment lagged one period. P( - l)= Rate of retail price inflation over preceding year lagged one period. D 1 =Cyclical variable taking value of 1. in period following election then 2, 3, up to N and declining to I the period before election. D2 = Declining trend, i.e. 60, 59, 58 from the month of the election. D3=Trend variable, taking value 1 after election and rising till next election, i.e. 1, 2, 3, 4, 5, etc.
Dependent Variable
Time Period
TABLE
g
9
2 P
02 6 L?
7;;
235
An Alternatit,e
Approach
to Explaining Political Popnhrity
In conclusion, on the basis of our empirical tests the permanent benefits model with rational expectations appears to be consistent with the data and suggests that the only significant predictor of government popularity, G,. is the lagged value, G, _ , (plus a moving average error process). This result provides a useful basis for further empirical work and can be employed as a benchmark for evaluating popularity functions based on alternative underpinnings. In other words, researchers in this area might find it desirable to compare their preferred models to those offered in the paper.
Notes 1. Notice that the property of unbiasedness. of expectations, that is that agents are right on average. does not imply per se that agents have the true model of the economy. There may be several models with the property that forecasts from them are in principle unbiased predictors. However, the error orthogonality property will not hold unless the forecast comes from the ‘true model’. 2. Notice that Fiorina’s work also lies close to the spirit of our model (though with a different expectations mechanism) in that it integrates a ‘rational’ choice model into the determinants of electoral behaviour. 3. A multiple-partv framework has been proposed bv Fair (1978) and Borooah and van der Ploeg (1982b) but their analyses are subject to the criticisms mentioned above. 4. Letting the moving average parameter be given by 0. it is obtained as a root of the equation +’ + (j@?v;*Lv)+
1 = 0
where V and Ware the variances of E and U respectively. 5. Joint estimation of the moving average error process and the other parameters
is not feasible for certain technical reasons involving composite moving average error terms (see MacDonald and Darroch, 1983). 6. It should also be noted that Hibbs. in a number of studies has employed time series methods in his analysis of popularity data, though with different behavioural underpinnings. (See, for example. Hibbs and Fassbender, 1981 for references.)
References V. Borooah and F. van der Ploeg. (1982a), ‘British Government Popularity and Economic Performance: A Comment’, Economic JorrmaL, 92366. 1982, pp. 405-10. V. Borooah and F. van der Ploeg. (1982b). ‘The Changing Criteria of Economic Success: Performance and Popularity in British Politics’, The Manchester School, l:l, 1982, pp. 61-78. in AutoregressiveG. E. P. Box and D. A. Pierce, ‘Distribution of Residual Autocorrelations Integrated Moving Average Time Series IModels‘, Journal ofthe American Statistical Association, 65: 1970, pp. 1509-26. B. Brown and S. Maital. ‘What do economists know? An Empirical Study of Expert Expectations’. Econometrica. 49:2. 1981, pp. 491-504. A. Downs, ‘An Economic Theory of Democracy’, (New York: Harper Br Row. 1957). R. C. Fair, ‘The Effect of Economic Events on Votes for President’. The Retmiew o/Economics and Statistics, 60:2. 1978, pp. 159-73. E. F. Fama, ‘Short Term Interest Rates as Predictors of Inflation’. American Economic Revieus. 67: 1975, pp. 269-82. M. Fiorina, ‘Short and Long-term effects of economic conditions on individual voting decisions’ in: D. A. Hibbs and H. Fassbender (editors), Contemporary PoliticalEconomy. (Amsterdam: North Holland, 198 1). B. S. Frey and F. Schneider, (1978a), ‘A Politico-economic IModel of the United Kingdom’, Economic Jonma/, 88~350, 1978, pp. 242-53. B. S. Frey and F. Schneider, (1978b). ‘An Empirical Study of Politico-economic Interaction in the U.S.‘, Review of Economics and Statistics. 60:2, 1978. pp. 174-83. B. S. Frev and F. Schneider, ‘A Politico-Economic Model of the U.K.---New Estimates and Predictions’. Tl,e Economic Journal, 91: 1981, pp. 737-40.
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HOLDEN ASD
D. A. PEEL
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R. E. Hall. ‘Stochastic Implications of the LifeCycle Permanent Income Hypothesis: Theory and Evidence’, Journal ofPolitical Economy. 86:6. 1978. pp. 971-87. A. C. Harvey, Time Series Modeis. (Deddington: Philip Xllan. 1981). D. A. Hibbs and H. Fassbender (editors), Contemporary Political Economy. (Amsterdam: North Holland. 198 1). G. H. Kramer, ‘Short-term Fluctuations in U.S. Voting Behaviour 1896-1964’. American Political Science Review. 65:1, 1971. pp. 131-43. J. MacDonald and J. Darroch. ‘Consistent Estimation of equations with composite moving average disturbance terms’, Journal ofEconometrics, 23: 1983. pp. 253-67. W. L. Miller and RI. Mackie. ‘The Electoral Cycle and the Asymmetry of Government and Opposition Popularity’, Political Studies. 21: 1973. pp. 263-79. A. P. L. Minford and D. A. Peel, ‘The Political Theory of the Business Cycle’. European Economic Review, 17:2, 1982, pp. 253-70. A. P. L. Minford and M. Brech, ‘The Wage Equation and Rational Expectations’ in D: Currie, A. R. Nobay and D. A. Peel (editors), Macroeconomic Anabsis, Proceedings of X.U.T.E. Conference. (London: Croom Helm, 198 1). J. F. Muth. ‘Rational Expectations and the Theory of Price Movements’. Econometrica. 29: 1961. pp. 315-35. W. D. Nordhaus. ‘The Political Business Cycle’, Review o/Economic Studies, 42: 1975. pp. 16990. M. Paldam and F. Schneider, ‘The Macro Economic Aspects of Government and Opposition Popularity in Denmark, 1957-1978’. National okonomisk Tidsskrift, No. 2, 1980. pp. 14970. C. A. Pissarides, ‘British Government Popularity and Economic Performance’. Economx Journa/. 90:359, 1980. pp. 569-81. G. J. Stigler, The Vote Motive. Hobart Paper No. 9. (London: Institute of Economic Affairs. 1973). G. Tullock. The Vote Motive. Hobart Paper No. 9, (London: Institute of Economic Affairs. 1976). H. D. Vinod. ‘Effects of ARIhlX Errors on the Significance Tests for Regression Coefficients’. Jorrrnal o/the American Statistical Association. 71: 1976. pp. 929-33. P. Whiteley. ‘Inflation, Unemployment and Government Popularity-Dynamic hlodels for the United States. Britain and W’est Germany’. Electoral Studies, 3: 1. 1984. pp. 3-24.
An Alternative Approach to Explaining Political Popularity K. HOLDEN* Department o/Economic and Business Studies, University of Liverpool, England
D. A. PEEL* Department of Economics, University College of Wales, Aberystwyth,
UK
Previous empirical work on the relationship between political popularity and economic events has either not attempted to model the alternative policies of the different parties, or has modelled them in a rather simplistic manner. It has also typically assumed that voters are backward-looking in contrast to recent work on expectations theory. An approach is outlined which is based on a forward-looking comparison of the parties and incorporates the effect of news. An alternative derivation relying on the evaluation of the stock of goodwill built up for each party is also suggested. Empirical evidence from the Gallup opinion poll provides some support for these hypotheses.
In recent years there has been a considerable amount of empirical work devoted to examining the relationship between economic events and political popularity. (See, for example, Downs, 1957; Borooah and van der Ploeg, 1982a, 1982b; Fair, 1978; Fiorina, 1981; Frey and Schneider, 1978a, 1978b; Kramer, 1971; Minford and Peel, 1982; Pissarides, 1980; Stigler, 1973; Whiteley, 1984.) It would appear from this work that the impact of economic variables on political popularity is unstable. In some studies the actual rate of inflation, unemployment or disposable real income (in levels or rates of change) appear with significant coefficients, but in other studies, for different time periods, these variables cease to be significant. This apparent instability of economic variables in the popularity functions could be a consequence of a number of factors. First, either little attempt has been made at modelling the alternative policies of different parties and the voters’ choice between these alternatives in previous work, or the opposition parties’ policies are modelled in a relatively simplistic manner by, for example, using a trend. (See, for example, Minford and Peel, 1982; Paldam and Schneider, 1980.) Thus typically, for example, the lead (or deficit) of the party in power over the main opposition (or the proportion voting for a particular party) is made a function of the economic variables, but not of the voters’ evaluation of the difference in policies. Second, it seems quite obvious, a priori, that there are many non-economic issues such as
*We are grateful to an anonymous referee and the Editor for helpful comments on an earlier draft. We are also grateful to the University of Liverpool Research Committee for financial support, and to Jane Edwards for research assistance. Any errors are the responsibility of the authors. 0261-3794/85/03/0231-091%03.00
0
1985 Butterworth
& Co (Publishers) Ltd
232
An Aiterndve
Approach
to Explaining
Political Popuiarity
defence, capital punishment and immigration which are of concern to voters. It is not clear that representing the effects of these issues by a simple random disturbance term (as in the studies previously cited) is an appropriate modelling procedure. Third, the popularity of a particular government is usually assumed to depend on the current or past values of economic variables rather than on anticipations or expectations of the future value of the variables. When expectations are explicitly included they are modelling with a simple backward looking function (for example. adaptive-see e.g., Nordhaus, 1975). To the extent that expectations are modelled, the assumed dependence of popularity on the current or past values of economic variables implies that espectations are formed in an adaptive manner-that is, as a mechanistic extrapolation of current or past values of variables. Recently various authors have incorporated rational expectations in a number of behavioural functions such as consumption, interest rates or wages (see, for example, Hall, 1978; Fama, 1975; Minford and Brech, 1981). The basic idea of the rational expectations hypothesis as outlined by Muth, 1961 is that expectations are informed predictions of future events and are essentially the same as the predictions of the correct economic theory. The rational expectations hypothesis has a number of implications for the ex post properties of expectations. First, expectations will be unbiased predictions or forecasts of the future value of a variable. Second, the forecasts will embody all relevant information available at the time the forecast is made. This implies that the forecast error will be uncorrelated or orthogonal to the information set. If this property does not hold then the forecast could have been improved by using this information. Third, the forecast errors will be serially uncorrelated after due allowance for the forecast horizon has been made. (See, e.g., Brown and hlaital, 1981.) All of these will only all hold if agents are assumed to form expectations as if they know the true model of the economy.’ Since there is no consensus amongst economists as to the correct model of the economy, as evidenced by competing macroeconomic models such as Cambridge or Liverpool, it is a priori unlikely that the rational expectations hypothesis can be literally true. Nevertheless the hypothesis does demand that we give explicit consideration to the information set that agents possess when expectations are formed. From this perspective it is clear that the great majority of agents within an economy are not expert econometricians who are able to produce their own rational forecasts. However. we should recognize that there is access through the media, at very low marginal cost, to a great variety of informed opinion such as the forecasts of bodies like the National Institute or the London Business School, and the views of specialist economic correspondents on the news and documentary programmes. Given these sources of information it becomes more reasonable to assume that expectations of voters may be based on a much richer information set than that given simply by the past history of a variable as is implied by the adaptive expectations hypothesis. Also, as mentioned above, a large number of authors have reported empirical results for different behavioural functions which appear to be consistent with the data generating process and in which expectations are modelled rationally. In general it seems a priori plausible to hypothesize that the expectations of the electorate of the future values of economic variables will influence voting intentions. Certainly the alternative to this would imply a degree of voter myopia which has connotations of irrationality and contradicts empirical evidence relating to, for example, saving behaviour. To the extent that these expectations do influence voting intentions modelling expectations implicitly in an adaptive manner, as in previous work, could be a potential source of misspecification. This point provides the major motivation for this study. The purpose in this paper is to present some further analysis of monthly popularity data
K. HOLDEN AND D. A. PEEL
233
based on responses to the UK Gallup poll survey over the period 1956(4)-1982( 12). The major novelty of the paper is that we develop a model based on the assumption that respondents’ expectations to the survey can be assumed to be formed in a manner approximated by the rational expectations hypothesis.’ This a: ;umption enables us to incorporate the points raised about previous work noted above. Although the empirical results we report are not totally consistent with our proposed models we believe this could provide a useful basis for future work.
Theory Our approach is inspired by the work on the consumption function of Hall (1978), who shows that optimization on the part of consumers, coupled with the assumption of rational expectations, implies that consumption in life-cycle or permanent income models should change according to a random walk, and that no variable other than current consumption should help to predict future consumption. That is, the best predictor of future consumption expenditure is the current level of consumption expenditure. In the context of voting models the corresponding result is that the best predictor of government support is the current level of government support. We now develop two models which are based on Hall’s approach. In our first model we assume that voters express a preference or voting intention for that party which gives them the greater anticipated stream of future benefits from its policies. This hypothesis is in the spirit of the sentiments expressed by Tullock (1976), who writes Voters and customers are essentially the same people. Mr. Smith buys and votes: he is the same man in the supermarket and in the voting booth. There is no strong reason to believe his behaviour is radically different in the two environments. we assume that in both he will choose the product or candidate he thinks is the best bargain for him.
For simplicity we assume a two-party framework3 which is reasonable for our data period when the Conservative and Labour parties dominated the political arena in the UK. Hence, G = a(EYA - EYE) + U. . . where G = the lead or deficit of the party in power (A) over the other party (B). EYA, EYB = the expected benefit accruing if party A or party B is in power. U =a random error reflecting the impact of measurement error in the opinion polls, which is assumed to have the classical properties. a = a positive constant. Both EYA and EYB depend on non-economic as well as economic factors since decisions to: privatize an industry, increase the inflation rare, invade the Falklands or reduce unemployment benefits, may all have implications for the voter. If we now difference (1) we get: AG=aA(EYA-EYE)+
U-U_1...
(2)
Here (EYA -EYE) is the change in the evaluation of the stream of benefits from party A relative to party B. If the evaluations are formed in a rational or unbiased manner then following Hall (1978) changes occur because of new information so that: A(EYA - EYB) =
E .
where E is a random variable reflecting news. Clearly, ‘news’ is unpredictable
(3) otherwise it
233
An Alternatiz~e Approdcb
to Elxplaining Political Popularity
could have been exploited previously in order to reassess EY” and EYE. Combining (3) gives: AC = a& + U-U-,...
(2) and
(4)
The residual here is a combination of two white noise error processes. Equation (4) informs us that if the joint hypotheses are correct then changes in the lead of the government is a function only of a combination of two errors: one reflecting measurement error (U-U1) and one reflecting new information or news (QE). Ignoring the measurement error term for the moment, the basic hypothesis embodied in (4) is that agents only change their decision to vote for a particular party between any two periods because of new information which has accrued between the periods. Because this information is unpredictable ex ante any lagged economic (or non-economic) variables which are part of the information set of agents at the time expectations are formed should not have any predictive power when added to equation (4). Following Harvey (1981, p. 43) it is easily shown that the combined error in (4) follows a first order moving average process. Notice that while (4) might suggest a moving average parameter of unity, the combined process has a parameter which depends on a and the variances of E and U, and so this will be different from one providing a f 0 and the variance of E is not zero. Moreover, the moving average parameter will have a negative coefficient.i The theoretical prediction of a negative moving average parameter provides an additional empirical test of the hypothesis. In the more general framework of autoregressive integrated moving average models this is an ARIMA (O,l, 1) process (see Whiteley, 1984 for further discussion) and can be written AG, = W, + 6W,_,
.
(5)
where W is a white noise error and 0 is the moving average parameter. This will be referred to as the ‘permanent benefits’ model. It can be interpreted that the best predictor of G, is G,_ , plus the error term. An alternative approach to modelling popularity, which remains very much within the spirit of our analysis, is to integrate the notion of loyalty to a party with rational expectations. We suppose that the voters’ support for a party is based on an evaluation of the relative ‘stock of goodwill’ which has accumulated for each party. Changes in the stock of goodwill only occur due to new information becoming available and due to depreciation (as a result of forgetfulness, deaths, diffusion of information, etc.). Assuming voters process information in an informed or rational manner. new information will be a random variable. This can be modelled as: G, = US, + U, and
As, = E, -
as,_,
.
(6)
..
(7)
where S is the relative stock of goodwill for the two parties. d is the depreciation rate. E and U are random errors with the classical properties. Combining (6) and (7) yields G, = (1-d)G,_,
+ as, + U, - (1-6)U,_,
.
.
(8)
As with (4), this equation has a residual which is a combination of the two error processes. Again. following Harvey (198 1, p. 43). (8) ISessentially an ARIMA (l,O,l) process with an additional white noise error which combine to give a different ARIMA (1 ,O, 1) process. The
K. HOLDEN AND
D. r\.
PEEL
235
autoregressive parameter of the combined process is (1 - 6) and the moving average parameter depends upon Q, d and the variances of E and U. As with the first model, the moving average parameter will have a negative coefficient if the hypothesis is correct. Thus (8) can be written G, = (l-6)G,-,
+ V, + yV,_,
..
(9)
where V, is a white noise error and v is the moving average parameter. Comparing the ‘permanent benefits’ approach. which gives (5), with the ‘stock of goodwill’ approach, which gives (9), the only formal difference is that due to the presence of 6 (the rate of depreciation of goodwill). In each case the level of government support, G,, is determined solely by its previous value, G,_ 1 , and the error term, and so no other variables effect G,.
Empirical
Results
We now present some empirical tests of the two models derived above. The data are collected monthly by the UK Gallup poll organization and, based on a quota sample of approximately 1,000 voters, include the percentage of respondents supporting the Conservative party and the Labour party. A preliminary examination of the data showed that the lead of the government over the major opposition was stationary. Next, we estimated univariate ARIMA (1 ,O, 1) and ARIMA (0,1,l) models for the government lead using monthly data over the period 1956(3)-1982(12). In order to check the stability of the estimated functions over time we also estimated the functions for the sub-periods 1956(j)-1968(8) and 1969(9)-1982(12). The time series results in Table 1 provide some support for both the models derived above. The constant terms are never significantly different from zero at the 5 per cent level of significance and the moving average parameter is negative as demanded by the theoretical models. The Box-Pierce statistics indicate that the residuals are random at the 5 per cent level of significance. The two models are not nested within each other and therefore we cannot discriminate between them on the basis of the test statistics reported in Table 1. An additional test of the postulated models is whether all new information is contained in the various random shocks which reflect news. If it is, and expectations are formed rationally, then any lagged variables, which are part of the information set conditioning expectations should not have a statistically significant systematic impact on government popularity when added to the time series models. We experimented with a large number of different economic and non-economic variables such as industrial production, interest rates, inflation, unemployment and ‘political’ variables (suggested by Miller and Mackie, 1973 and Frey and Schneider, 1981). Interest rates and industrial production were always insignificant. However, some significant coefficients were sometimes obtained for the other variables for the ‘goodwill’ model. We report a selection of our results in Table 2. Ordinary least squares is the method of estimation. The presence of a moving average error process implies that the least square estimates will be unbiased but the standard errors will be biased.5 However, Vinod (1976) has provided tables of critical values oft if least squares is utilized when a moving average error process is present. If the least squares estimates of the l-values lie outside the critical range, then the hypothesis tests can be conducted in the normal way. We observe from the first six results from Table 2 that for the ‘permanent benefits’ model (5) inflation, unemployment and the political variables are always insignificantly different for zero which supports this hypothesis: the change in popularity is purely random. In the ‘goodwill’ model
236
An Alternative
Approach
to Erpiaining
TABLE 1. Time series properties
Political Popuiarity
of the Gallup data. &loving Average
Dependent Time
Period
1956(3)-82(
Variable 12)
1956(3)-69(8) 1969(9)-S2(
12)
G, -
G,-1
G, -
G,-I
G
G,-I
1956(3)-82(12)
G,
1956(3)-69(8)
G,
1969(9)-82(12)
G,
-
Constant
G,_
0.03 (0.20) -0.0s (0.26) 0.11 (0.30) -0.41 (0.25) -0.35 (0.29) -0.56 (0.39)
0.88 (0.03) 0.93 (0.03) 0.83 (0.06)
1
Parameter
R -’
BP10
-0.26 (0.05) -0.22 (0.07) -0.31 (0.08) -0.17 (0.06) -0.16 (0.08) -0.18 (0.10)
0.05
24.9
0.04
19.90
0.07
16.95
0.73
18.92
0.83
18.44
0.62
11.40
Numbers in parentheses are standard errors R -? is the corrected coefficient of determination BPis theBox and Pierce(l970)statistic with twenty degrees of freedom(5 percent critical value is 31.41) G, is the percentage lead of the government in power over the main opposition
(the last six results in Table 2), the rate of unemployment is significant in the latter period. Also the cyclical political variable (D 1) is significantly different from zero in all periods. Thus, while G, depends on G,_ I as that required by the hypothesis, the significance of D 1 contradicts the hypothesis.
Conclusions In this paper we have suggested that empirical work on the relationship between political popularity and economic events has perhaps given insufficient attention to modelling the alternative policies of different parties. Also typically it is assumed that voters’ expectations are backward looking and mechanistic in contrast to recent work on expectations theory. Two alternative approaches to modelling popularity have been proposed in which voters are assumed to process information efficiently as implied by the rational expectations hypothesis. One approach is based on a forward-looking comparison of the differences in permanent benefits generated by the parties and the other is based on an evaluation of the relative stocks of goodwill accumulated for each party. The two models imply rather similar time series representations for government popularity. The time series representations of popularity data over the period 1956(3)-1982(12) are consistent with the different approaches.6 One implication of our models is that no other lagged variables should have any systematic effect on popularity. This prediction, of course, contrasts dramatically with other ‘optimizing’ models which purport to explain popularity. These models (see, for example, Whiteley, 1984) imply that the aggregate relationship between economic conditions and government popularity should be reasonably stable and strong over time. We found that all the lagged economic and non-economic variables we experimented with had an insignificant impact on changes in popularity (the permanent benefits model) but that the cyclical political variable was always significant in the goodwill model. This finding suggests that the permanent benefits model is the more appropriate of the two ways of modelling popularity.
G, - cl-1
G, - G,-1
G, - G,-I
G, - Gt-I
G, - G/--I
G - G-1
Gf
G
Gf
Gt
Gf
G,
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)--1982(12)
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)-1982(12)
1956(3 j-1982(12)
1956(3)-1969(8)
1969(9)-1982(12)
1956(3)-1982(12)
1956(3)-1969(8)
1969(9)--1982(12)
l
0.73 ( 18.99)a 0.74 (14.21)a 0.59 (9.3lY 0.76 (22.19)” 0.83 (20.9l)a 0.68 (11.82)’
0.91 (0.74)’ 0.73 (0.33) 0.91 (0.74)’ 0.55 (0.96)’ 0.84 (1.19)’ 0.26 (0.20)’ 2.41 (2.07) 5.92 (2.57) 1.19’ (0.61) 1.22 (2.26) 1.43 (2.11) 0.97 (1.16) l
G, _ 1
Constant
2.
-0.08 (1.24)’ -0.23 (0.96)’ 0.12 (1.08)’
0.03 (0.4 1)’ 0.15 (0.60)’ 0.03 (0.41)’
PC--1)
1)
0.24 (1.95) -1.92 (2.58) 0.50 (2.86)p
(0.09).
(0.09). -0.51 (0.70) -0.01
-0.01
w-
Dl
-0.06 (1.43)’ -0.01 (0.19)’ -0.16 (3.74Y -0.15 (2.91y’ -0.24 (3.2)a -0.15 (4.04)” -0.15 (3.33)” -0.15 (2.50)
-0.03 (0.65) -0.07 (1.36)’ -0.03 (0.65)’ -0.03 (1.06)’
l‘he influence ot other variables.
-0.05 (1.77) -0.02 (0.68)’ -0.06 (1.56)’
0.05 (0.18)’ 0.03 (0.98)’ 0.05 (0.18)’
02
-0.15 (0.62)’ 0.004 (0.13)’ -0.08 (1.97)
(0.09). -0.03 (1.24)’
-0.03 (-1.24)’ -0.003
D3
R2
2.21 2.11 2.22 2.27 2.14
0.70 0.80 0.50
2.48 2.19
2.41
2.45
2.48
2.46
2.48
DW
0.81 0.58
0 0.70
0
0
0
0
0
I values in parentheses. A high I value implies the variable effects the dependent variable. ’ indicates significantly different from zero or indicates insignificantly different from zero given the presence of a first order moving average parameter. DW = Durbin Watson Statistics. U( - 1) = Percentage rate of unemployment lagged one period. P( - l)= Rate of retail price inflation over preceding year lagged one period. D 1 =Cyclical variable taking value of 1. in period following election then 2, 3, up to N and declining to I the period before election. D2 = Declining trend, i.e. 60, 59, 58 from the month of the election. D3=Trend variable, taking value 1 after election and rising till next election, i.e. 1, 2, 3, 4, 5, etc.
Dependent Variable
Time Period
TABLE
g
9
2 P
02 6 L?
7;;
235
An Alternatit,e
Approach
to Explaining Political Popnhrity
In conclusion, on the basis of our empirical tests the permanent benefits model with rational expectations appears to be consistent with the data and suggests that the only significant predictor of government popularity, G,. is the lagged value, G, _ , (plus a moving average error process). This result provides a useful basis for further empirical work and can be employed as a benchmark for evaluating popularity functions based on alternative underpinnings. In other words, researchers in this area might find it desirable to compare their preferred models to those offered in the paper.
Notes 1. Notice that the property of unbiasedness. of expectations, that is that agents are right on average. does not imply per se that agents have the true model of the economy. There may be several models with the property that forecasts from them are in principle unbiased predictors. However, the error orthogonality property will not hold unless the forecast comes from the ‘true model’. 2. Notice that Fiorina’s work also lies close to the spirit of our model (though with a different expectations mechanism) in that it integrates a ‘rational’ choice model into the determinants of electoral behaviour. 3. A multiple-partv framework has been proposed bv Fair (1978) and Borooah and van der Ploeg (1982b) but their analyses are subject to the criticisms mentioned above. 4. Letting the moving average parameter be given by 0. it is obtained as a root of the equation +’ + (j@?v;*Lv)+
1 = 0
where V and Ware the variances of E and U respectively. 5. Joint estimation of the moving average error process and the other parameters
is not feasible for certain technical reasons involving composite moving average error terms (see MacDonald and Darroch, 1983). 6. It should also be noted that Hibbs. in a number of studies has employed time series methods in his analysis of popularity data, though with different behavioural underpinnings. (See, for example. Hibbs and Fassbender, 1981 for references.)
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R. E. Hall. ‘Stochastic Implications of the LifeCycle Permanent Income Hypothesis: Theory and Evidence’, Journal ofPolitical Economy. 86:6. 1978. pp. 971-87. A. C. Harvey, Time Series Modeis. (Deddington: Philip Xllan. 1981). D. A. Hibbs and H. Fassbender (editors), Contemporary Political Economy. (Amsterdam: North Holland. 198 1). G. H. Kramer, ‘Short-term Fluctuations in U.S. Voting Behaviour 1896-1964’. American Political Science Review. 65:1, 1971. pp. 131-43. J. MacDonald and J. Darroch. ‘Consistent Estimation of equations with composite moving average disturbance terms’, Journal ofEconometrics, 23: 1983. pp. 253-67. W. L. Miller and RI. Mackie. ‘The Electoral Cycle and the Asymmetry of Government and Opposition Popularity’, Political Studies. 21: 1973. pp. 263-79. A. P. L. Minford and D. A. Peel, ‘The Political Theory of the Business Cycle’. European Economic Review, 17:2, 1982, pp. 253-70. A. P. L. Minford and M. Brech, ‘The Wage Equation and Rational Expectations’ in D: Currie, A. R. Nobay and D. A. Peel (editors), Macroeconomic Anabsis, Proceedings of X.U.T.E. Conference. (London: Croom Helm, 198 1). J. F. Muth. ‘Rational Expectations and the Theory of Price Movements’. Econometrica. 29: 1961. pp. 315-35. W. D. Nordhaus. ‘The Political Business Cycle’, Review o/Economic Studies, 42: 1975. pp. 16990. M. Paldam and F. Schneider, ‘The Macro Economic Aspects of Government and Opposition Popularity in Denmark, 1957-1978’. National okonomisk Tidsskrift, No. 2, 1980. pp. 14970. C. A. Pissarides, ‘British Government Popularity and Economic Performance’. Economx Journa/. 90:359, 1980. pp. 569-81. G. J. Stigler, The Vote Motive. Hobart Paper No. 9. (London: Institute of Economic Affairs. 1973). G. Tullock. The Vote Motive. Hobart Paper No. 9, (London: Institute of Economic Affairs. 1976). H. D. Vinod. ‘Effects of ARIhlX Errors on the Significance Tests for Regression Coefficients’. Jorrrnal o/the American Statistical Association. 71: 1976. pp. 929-33. P. Whiteley. ‘Inflation, Unemployment and Government Popularity-Dynamic hlodels for the United States. Britain and W’est Germany’. Electoral Studies, 3: 1. 1984. pp. 3-24.