Public Choice 91: 57–88, 1997. 57c 1997 Kluwer Academic Publishers. Printed in the Netherlands.

Econometric estimations of popularity functions: A case study forAustria�

REINHARD NECK1 and SOHBET KARBUZ21Department of Economics, University of Osnabruck, D-49069 Osnabruck, Germany;2UTESAV, TR-80310 Istanbul, Turkey

Abstract. In this paper, we investigate the effects of changes in economic conditions on thepopularity of political parties in Austria. After a brief description of the Austrian politicalsystem, we estimate single equations and simultaneous systems of popularity functions fordifferent parties, based on traditional theoretical foundations. Results show that some effectsof economic variables on popularity exist, although they are different between different pol-icy regimes. Traditional popularity functions nevertheless outperform models based on theassumption of voters’ rational expectations, which claim that only unexpected changes ineconomic conditions affect political popularity.

1. Introduction

Over the past 25 years, the influence of economic conditions on voters’ behav-ior has been one of the central topics of empirical research in public choice,as can be seen from surveys of Schneider and Frey (1988) and Nannestadand Paldam (1994). For many countries, it has been shown that voters takeinto account past and present government performance when evaluating thepolitical parties competing for votes. This is reflected by significant effectsof macroeconomic policy objective variables, such as the rate of unemploy-ment, the rate of inflation, and the rate of growth of real disposable income,on voters’ approval of parties in government as measured by opinion polls.

On the other hand, already Stigler (1973) has criticized the view that votersshould take into account economic conditions when evaluating government

� Earlier versions of this paper have been presented at the 39th International Atlantic Eco-nomic Conference, Vienna, March 1995, and at the Econometric Research Seminar of the Insti-tute for Advanced Studies, Vienna, June 1995. We are grateful to A. Kirschhofer-Bozenhardt(IMAS Linz) for providing us with the Austrian popularity data and to the participants of theabove meetings, especially to M. Deistler and G. Kirchgassner, as well as to B.S. Frey, J.Jaenicke and two anonymous referees for very valuable comments. Financial support from theLudwig Boltzmann-Institut zur Analyse wirtschaftspolitischer Aktivitaten, Vienna, is grate-fully acknowledged. Karbuz acknowledges support from the Institute for Advanced Studies,Vienna. The usual disclaimer applies.

58

performance, because he considered the differences between parties’ plat-forms on macroeconomic issues to be too small. Later on, new classicalmacroeconomics raised doubts about government’s ability to influence realeconomic variables, such as unemployment and output, in a systematic man-ner. If voters have rational expectations, they should not hold the governmentresponsible for the development of these variables. Moreover, results of stud-ies using time series methods indicate that many economic variables seem tofollow random walk processes, so even well-established structural relations,such as the consumption function, might be based on spurious correlations(Nelson and Plosser, 1982; Hall, 1978). This might be even more true of thepopularity function which has been found to be a rather unstable relationshipin many earlier studies.

The question whether the traditional popularity function is valid in spite ofthese criticisms is primarily an empirical one, and answers might be differentfor different countries. For instance, studies for Germany using time seriesmethods and test procedures based on the hypothesis of rational expectationshave found significant effects of unemployment and inflation on the popularityof parties in government (Kirchgassner, 1985). In the present paper, we tryto answer this question for Austria. So far, only Neck (1988) and Hofreither(1988) have dealt with the Austrian case, using data up to 1986. Here, weextend these studies to include more recent developments. Moreover, wecontrast results obtained from estimating popularity functions based on thetraditional theory to those obtained from rational-expectations models. Usingvarious econometric approaches, we arrive at the conclusion that traditionalmodels of voters’ behavior, though (as expected) not stable across politicalregimes, still provide estimates which are superior to those obtained frommodels with rational expectations.

2. The Austrian political system

Austria regained independence from German occupation at the end of WorldWar II. Since 1945, the two biggest political parties are the conservativePeople’s Party (Osterreichische Volkspartei, OVP) and the social-democraticSocialist Party (Sozialistische, now Sozialdemokratische Partei Osterreichs,SPO). The Communist party (Kommunistische Partei Osterreichs, KPO),although strongly backed by Soviet forces occupying parts of Austria until1955, never had a significant share of the votes and was gradually marginal-ized as a political movement well ahead of the demise of the Eastern Bloc.In 1949, another party emerged which later became the Freedom Party (Frei-heitliche Partei Osterreich, FPO). Its voters were to a large extent people withstrong German nationalist background; however, there were also some people

59

committed to classical liberalism (in the European sense) among supportersand activists of the FPO. For about 35 years, the Austrian political systemcould be characterized as a two-and-a-half party system (for details, see, e.g.,Haerpfer, 1991). Starting with the early eighties, a deconcentration of thepolitical party system took place, first with the emergence of ‘green’ parties(environmentalists), one of which was elected to Parliament first in 1986, andthen with the splitting off of the Liberales Forum (a liberal party) from theFPO, elected to Parliament first in 1994. A short summary of the Austrianpolitical system is given in Table 1.

These developments of the political party system in Austria have beensubjects of research by sociologists and political scientists in Austria (see, e.g.,Traar and Birk, 1988). These authors investigate the influence of structuraldeterminants of voting behavior, such as changes in the occupational structureor changes of values and attitudes within the electorate. Apart from these long-run determinants, voting behavior is also influenced by short- and medium-run developments, which can be decisive for the turnover of votes. Public-choice analysis usually concentrates on these determinants of marginal voters’decisions, especially on economic ones. Popularity functions such as thoseestimated here rely on these economic explanations of voting behavior andshould, therefore, be regarded as complementary to sociological empiricalstudies. This implies that we must not expect stable popularity functions overan extended time period characterized by structural changes in the societyand in the political system, such as those having occurred in Austria overthe last twenty years, unless economic and other determinants of electoralbehavior are completely independent of each other, which cannot reasonablybe presumed. Although of obvious importance, the joint consideration oflong-run and economic (and other) factors influencing voting behavior seemsto be precluded in Austria by problems of data availability at present.

3. Theoretical foundations of traditional popularity functions

We start from the public-choice theory of voting behavior developed byDowns (1957), Davis, Hinich, and Ordeshook (1970) and Kirchgassner (1986),which is based upon individual rationality of voters in the sense of optimiz-ing behavior, but does not assume rational expectations of voters (whichmay be justified by prohibitive costs for obtaining the informations requiredfor the rational-expectations hypothesis to hold). Accordingly, each voteri = 1; � � � ; k wants to maximize his (her) utility function Ui defined upon then-dimensional politico-economic space P � <

n. Each voter has an optimalposition x

�

i in P:

x�

i = arg maxx2P

Ui(x); i = 1; � � � ; k:

60

Tabl

e1.

Dev

elop

men

tof

the

Aus

tria

npo

litic

alsy

stem

Peri

odPa

rtie

sin

Form

ofD

ate

of%

ofvo

tes

for

%of

seat

sin

Num

ber

of

gove

rnm

ent

gove

rnm

ent

elec

tion

part

ies

inPa

rlia

men

tfor

part

ies

in

gove

rnm

ent

part

ies

inPa

rlia

men

t

gove

rnm

ent

1945

–194

7O

VP,

SPO

,KPO

allp

artie

s25

Nov

.194

599

.810

0.0

3

1947

–194

9O

VP,

SPO

gran

dco

aliti

on94

.497

.6

1949

–195

3O

VP,

SPO

gran

dco

aliti

on9

Oct

.194

982

.787

.34

1953

–195

6O

VP,

SPO

gran

dco

aliti

on22

Feb.

1953

83.4

89.1

4

1956

–195

9O

VP,

SPO

gran

dco

aliti

on13

May

1956

89.0

94.5

4

1959

–196

3O

VP,

SPO

gran

dco

aliti

on10

May

1959

89.0

95.2

3

1963

–196

6O

VP,

SPO

gran

dco

aliti

on18

Nov

.196

289

.495

.23

1966

–197

0O

VP

one

part

y6

Mar

.196

648

.351

.53

1970

–197

1SP

Oon

epa

rty,

min

ority

1M

ar.–

4O

ct.1

970

48.4

49.1

3

1971

–197

5SP

Oon

epa

rty

10O

ct.1

971

50.0

50.8

3

1975

–197

9SP

Oon

epa

rty

5O

ct.1

975

50.4

50.8

3

1979

–198

3SP

Oon

epa

rty

6M

ay19

7951

.051

.93

1983

–198

7SP

O,F

POsm

allc

oalit

ion

24A

pr.1

983

52.6

55.7

3

1987

–199

0SP

O,O

VP

gran

dco

aliti

on23

Nov

.198

684

.485

.84

1990

–199

4SP

O,O

VP

gran

dco

aliti

on7

Oct

.199

074

.976

.54

1994

–199

6SP

O,O

VP

gran

dco

aliti

on9

Oct

.199

462

.663

.95

61

Each of the m political parties takes a position in the politico-economicspace, xp

j 2 P; 1; � � � ;m. Voters estimate the positions of the parties, usingtheir available informations:

xij = fi(xpj ); i = 1; � � � ; k; j = 1; � � � ;m:

Each voter i evaluates these estimated positions for all parties and determinesexpected utility losses Lij arising from the possibility of position xij beingrealized by party j if in power:

Lij = Ui(x�

i )� Ui(xij); i = 1; � � � ; k; j = 1; � � � ;m:

If voter i decides to participate in an election (which is a non-trivial require-ment, as is well known from the literature about the paradox of voting),then he (she) votes for that party j� which (according to his/her estimations)realizes the smallest utility loss for him (her):

j� = arg min Lij:

j 2 f1; � � � ; mg

In determining their expectations about the political positions of the parties,voters have two sources of information. For all parties, they know theirprograms and promises for the future. For the parties forming the government,voters have additional information resulting from the development of thepolitico-economic system in the recent past. A crucial hypothesis of the theoryof the popularity function states that macroeconomic variables are amongthese aspects of the politico-economic system relevant to the voters. If this isthe case, then the evaluation of the party in government (and indirectly thatof the other parties) depends upon those variables. In particular, voter i has anevaluation function Fi(x) for the party in power depending on the actual stateof the politico-economic system x, which includes macroeconomic variables.

These considerations can be extended to a dynamic setting. An electionperiod is defined as the time interval [0, T]. The state of the politico-economicsystem at each time t is given by x(t) 2 P 8t 2 [0; T]. The evaluationfunction of voter i estimating his (her) utility arising from x(t) at time t isgiven by Fi[x(t); t] : <n+1

7! <. The traditional theory of the popularityfunction assumes that voters are looking backwards in evaluating the party ingovernment; in contrast to the theory of rational expectations, voters do nottake into account anticipations of future policies. In addition, they discountpast developments of the politico-economic system by backward-lookingdiscount rates �i � 0, as introduced by Nordhaus (1975), i.e., they exhibitmyopic behavior. At the time of the election T, the utility of voter i fromdevelopments over the last election period is given by

62

JTi =

Z T

0Fi�x(t); t

�exp

��i(t� T)

�dt: (1)

The voting decision is assumed to be based on this utility function: voter ivotes for the party in government if JT

i is greater than some given benchmark.Aggregating over voters (which is again non-trivial) and defining for the

politico-economic system functions �; F and JT analogous to voter-specificfunctions �i; Fi and JT

i gives a collective voting function

G(T) = G(0)exp(��T) +Z T

0F�x(t); t

�exp

��(t� T)

�dt; (2)

with G(T) being the vote share of the party in government at an election attime T. For an empirical operationalization of this function, a discrete-timeformulation has to be used. Applying the Koyck transformation, we obtain

G(t) = �G(t� 1) + (1� �)F�x(t); t

�; (3)

where G(t); t = 0; 1; � � � ;T; is the vote share of the party in government ata fictitious election at time t, empirically measured by the results of opinionpolls, and � = exp(��) measures voters’ “memory” of past as compared tocurrent events, 0 � � � 1. In most cases, a linear approximation of F[x(t); t]is used to arrive at the regression model

G(t) = a0 + �G(t� 1) +nX

k=1

akxk(t) + u(t); (4)

where xk(t); k = 1; � � � ; n; are variables characterizing the state of thepolitico-economic system at time t, x(t) = [x1(t) � � � xn(t)]0; and u(t) is astochastic disturbance term. Both linearity and time-invariance of F[x(t); t]and normality of the disturbance term are only approximations; see Fair(1978) and Borooah and van der Ploeg (1983).

In a two-party system, it is sufficient to estimate a popularity function (4)for the party in government, because the opposition party gets automaticallyO(t) = 100 – G(t), if G(t) and O(t) are the popularity of the government and theopposition party, respectively, measured as percentage shares of those castingvalid votes (or expressing their preference for a party). However, Austriacannot be regarded as having a two-party system, at least not in the recentpast. With m � 2 parties, we get a system of popularity functions instead of(4), namely

Pj(t) = aj0 + �Pj(t� 1) +nX

k=1

ajkxk(t) + uj(t); (5)

63

where Pj(t) denotes the popularity of party j; j = 1; � � � ;m; for which therestrictions hold:

mXj=1

aj0 = 100(1� �); (6)

mXj=1

ajk = 0; k = l; � � � ; n; (7)

mXj=1

uj(t) = 0; t = 0; 1; � � � ;T: (8)

Here it is assumed that the parameter � is the same for each party; then therestrictions (6) to (8) are fulfilled automatically, and only m–1 equations haveto be estimated by seemingly unrelated regressions (SUR) – (see, e.g., Theil,1971).

When a popularity function like (4) or a system of popularity functions like(5) has to be estimated, the variables xk to be taken as regressors have to bedetermined. In most cases, only economic variables are included, althoughsome studies have added dummy variables to capture political developments,too. Among the economic variables, only those should be included whichaffect the voters, which are known to the voters, and which can reasonablybe assumed by the voters to be influenced by the government. Althoughsome international studies have tried various additional economic variables,most estimated popularity functions contain only three economic objectivevariables: the rate of unemployment, the rate of inflation, and an incomegrowth rate, usually that of real disposable income. As for the signs of thecoefficients of those variables, two hypotheses have been formulated in thepublic-choice literature:

(a) The responsibility hypothesis states that the electorate holds the govern-ment responsible for the development of the economic variables, irre-spective of the government’s ideological orientation. If this hypothesisholds true, then voters should withdraw support from the party in powerwhen unemployment or inflation rises and when income growth falls.Hence, the coefficients of the rate of unemployment and of the rate ofinflation in (4) and (5) should be negative and the coefficient of incomegrowth should be positive for a party in government, and have the oppo-site signs for an opposition party. This hypothesis is used in most studiesof popularity functions, and it has been supported by estimates for manycountries.

64

(b) Recently, an alternative hypothesis has been proposed by Swank (1993),which is based upon the partisan theory, originally due to Hibbs (1977).Here ideological differences between political parties are explicitly tak-en into account, as it is assumed that political parties pursue differentmacroeconomic policy objectives. Left-wing parties give higher weightsto employment and income growth than right-wing parties, which aremore concerned about inflation. This hypothesis, combined with a mod-el of optimal government behavior and with a modified model of voters’behavior, gives rise to the following implication: Left-wing parties losesupport if inflation rises, if unemployment falls, and if income growth ris-es, and vice versa for right-wing parties, irrespective of whether they arein office or not. Popularity functions based on the partisan model there-fore should exhibit positive coefficients for the rate of unemployment andnegative ones for the rate of inflation and the income growth for left-wingparties; the opposite signs are expected for right-wing parties. A morethorough empirical examination of this hypothesis would have to takeinto account also possible changes in the trade-offs between the macro-economic objective variables and additional assumptions about voters’expectations of government policies; for details, see Swank (1993).

In the following, we try to obtain empirical evidence for Austria basedon traditional popularity functions, where both of the above hypotheses willbe considered. As an alternative, we estimate popularity functions based onthe hypothesis of rational expectations which denies systematic influencesof past economic developments on voters’ evaluations of political parties.Throughout, we neglect possible non-economic determinants of the popularityof political parties.

4. Specification of the Austrian popularity function: OLS results

For this study, we use popularity data provided by the Institut fur Markt-und Sozialanalysen (IMAS) Linz. These are quarterly data, based on a quotasample representative for the Austrian population aged 16 years and older;in cases where more than one observation was available for a quarter, theirarithmetic mean has been used. Popularity and economic data are availablefor the period 1975.4 to 1993.4. For the political parties SPO, OVP, andFPO, data are available for the entire period. For other parties (Communists,Green Parties, Liberales Forum), data cover only some subsets of this period(the latter coming into existence only during this period), hence they wereaggregated to a fourth “party” called the remainder; cf. also Hofreither (1988)

65

for this procedure. The sum of the shares of these four “parties” is always100.

First, popularity functions were estimated separately for the four parties,with particular emphasis to those for the SPO, as this party was in governmentover the entire period for which popularity data are available. We denote theproportion of the population indicating their intention to vote for the SPO,the OVP, the FPO, and the “remainder party” if there were general electionsat the time of being asked (t) by SP(t), VP(t), FP(t), and R(t), respectively.For the independent variables, seasonally adjusted data for the rate of unem-ployment [UR(t)], the rate of inflation [IR(t)] (relative fourth difference of theconsumer price index), and the growth rate of real disposable income [GR(t)](relative fourth difference of real disposable income) were used; all data are ofquarterly frequency. To investigate the stationarity properties of the politicaland economic variables, augmented Dickey-Fuller tests for unit roots werecarried out. The results were ambiguous, depending on the number of laggedfirst differences taken into account and on the presence or absence of a deter-ministic trend. As non-stationarity could not be excluded, cointegration testsand attempts at estimating error-correction models have been undertaken.Due to the low power of the tests, again the results were not unambiguous,and error-correction estimations failed to yield acceptable results. Althoughthis question deserves further investigation, for the present study we followNannestad and Paldam (1994) in considering it reasonable to assume popu-larity and the economic variables to be trendless and cointegrated in the longrun.

We start from estimating equation (4) for SP(t) with the three independentvariables mentioned over the entire period 1976.1 to 1993.4. Results of anOLS regression show that only the lagged dependent variable and the rateof unemployment have a significant influence on the popularity of the SPO.The estimated coefficients of the rate of inflation and the growth rate of realdisposable income are insignificant; the coefficient of the inflation rate ispositive, which contradicts both the responsibility and the partisan theoryof the popularity function. A slight improvement over this specification interms of statistical goodness-of-fit measures could be obtained when laggedinstead of current values of economic variables were used as regressors. Thereason for this can be seen in the fact that data about economic conditionsare published with some time lag in Austria, hence they do not get knownto voters immediately. As this improvement remained true for most of thespecifications tried and also for popularity functions for the other parties, wedecided to retain this modification. The results of this estimation are given inequation (9) in Table 2. Now the growth rate is significant at the 90% level,the rate of unemployment is highly significant, both with the sign expected

66

under the responsibility hypothesis, but the rate of inflation has still a positive(though insignificant) coefficient.

One of the questions which deserve further investigations is that of thestability of the popularity function over the entire time period. It is possibleto argue that for the SPO, the function could be stable even in the presenceof the change from a one-party government to a “small coalition” and thento a “grand coalition” both under the responsibility hypothesis and the parti-san theory. At least for the OVP, this cannot be true under the responsibilityhypothesis, because this party was the major opposition party until the end of1986 and a party in office afterwards. This may also affect the popularity of theSPO and the other parties, hence stability of the popularity functions seemsto be a questionable presumption. Several tests were performed to check thisissue. For instance, we consider the three periods of the one-party governmentof the SPO (until 1983.2), the “small coalition” SPO-FPO (1983.3 to 1986.4)and the “grand coalition” SPO-OVP (since 1987.1) and estimate popularityfunctions separately for one or two of these subperiods. In this case, coef-ficients of economic variables show clearly different values and sometimeseven different signs between different subperiods. As an example, equations(10) and (11) in Table 2 show the results for the combined periods of the SPOone-party government and the SPO-FPO coalition and for the period of theSPO-OVP coalition, respectively. Here especially the positive coefficient ofthe unemployment rate for the period of the “grand coalition” is incompatiblewith the responsibility hypothesis; this and results from alternative specifi-cations seem to point towards a loss of a stable relation between economicvariables and the popularity of the SPO since 1987. Statistically, a Chow testrejects stability of the regression coefficients at the 0.98 significance level inthis and similar specifications.

As the stability hypothesis is rejected also for other specifications and forthe popularity functions of the other parties, in the following we always con-sider the two samples of 1976.1 to 1986.4 and 1987.1 to 1993.4 separately;it should be kept in mind, however, that the latter sample consists of only 28observations. Separate estimations for the period of the “small coalition” arenot possible due to deficient degrees of freedom. For both samples, laggedeconomic variables give a better fit than contemporaneous ones. Nevertheless,from the point of view of theoretical presumptions, both estimated popularityfunctions are not quite satisfactory. For the first sample, the positive coeffi-cient of the rate of inflation is annoying. In the second sample, the positivecoefficient of the rate of unemployment accords with the partisan theory,whereas the positive coefficient of the income growth rate is compatible withthe responsibility hypothesis. In both cases the coefficient of the inflationrate is insignificant. A fairly large number of additional estimations has been

67

Tabl

e2.

Pop

ular

ity

func

tion

sfo

rA

ustr

ia,O

LS

.Dep

ende

ntva

riab

le:S

P(t

)

Equ

atio

nT

ime

Exp

lana

tory

vari

able

sR

2 cSE

h

num

ber

peri

odC

onst

ant

SP(t

–1)

UR

(t–1

)IR

(t–1

)G

R(t

–1)

GN

(t-1

)

(9)

1976

.1–

16.5

448

0.66

476

–0.5

5427

0.16

851

0.10

687

0.88

967

1.22

06–1

.272

6

1993

.4(3

.745

0)(8

.028

4)(–

2.90

06)

(1.4

276)

(1.6

717)

(10)

1976

.1–

28.7

938

0.43

413

–0.7

8971

0.11

117

0.12

807

0.75

210

1.15

400.

6419

6

1986

.4(3

.898

2)(3

.035

7)(–

3.24

00)

(0.8

6701

)(1

.671

0)

(11)

1987

.1–

12.6

803

0.50

127

1.16

620.

0979

450.

4424

00.

4151

61.

0915

–2.5

595

1993

.4(1

.789

2)(3

.317

6)(2

.145

0)(0

.371

22)

(3.2

327)

(12)

1976

.1–

30.1

075

0.39

955

–0.7

9642

0.16

247

0.76

410

1.12

570.

5215

6

1986

.4(4

.161

8)(2

.783

7)(–

3.60

60)

(2.0

152)

(13)

1987

.1–

10.1

272

0.58

080

0.87

218

0.43

058

0.44

732

1.06

10–1

.841

2

1993

.4(1

.446

3)(4

.039

0)(1

.931

5)(3

.441

6)

Num

bers

inbr

acke

tsbe

low

estim

ated

coef

fici

ents

are

t-ra

tios.

R2 c

isth

eco

effi

cien

tof

dete

rmin

atio

nad

just

edfo

rth

ede

gree

sof

free

dom

.SE

isth

est

anda

rder

ror

ofth

ere

gres

sion

.his

Dur

bin’

sh-

stat

isti

cfo

rfi

rst-

orde

rse

rial

corr

elat

ion.

68

performed to obtain a more satisfactory popularity function. Most of theseattempts, such as introducing different functional forms, additional explana-tory variables or dummy and trend variables, did not yield better results.

One alternative specification, however, provided acceptable results for thesample 1976.1 to 1986.4. Here we omitted the insignificant rate of inflationand replaced the growth rate of real disposable income by that of nominaldisposable income [GN(t)]. This eliminates possible multicollinearity prob-lems between inflation and income growth. The results for the SPO are givenin equation (12) in Table 2. The goodness-of-fit measures are better than inspecification (10), and the signs of the coefficients are in accordance with theresponsibility hypothesis. Unfortunately, this does not remain true when thesame specification is estimated for the sample of the “grand coalition”, as isshown by equation (13) in Table 2. Nevertheless, we consider this specifica-tion to be satisfactory for the longer first sample. In terms of an economicinterpretation, it means that voters have some “money illusion” when hold-ing the party in government responsible for economic developments, becausethey do not discriminate between increases in disposable income due to priceincreases and those due to quantity (real income) increases. For the sam-ple 1976.1 to 1986.4, the responsibility hypothesis seems to outperform thehypothesis based on partisan theory. Apart from problems with the numberof the degrees of freedom, one reason for the failure of both hypotheses tobe confirmed for the sample 1987.1 to 1993.4 may be the apparent changein the economic constraints. A first regression and correlation analysis of thePhillips curve relation indicates that there is a trade-off between inflation andunemployment only in the first subperiod, whereas in the second subperiodthey show a positive relation. This means that the structure of the economicsystem in Austria has changed, too, and this may have induced voters to alterthe way in which they evaluate political parties.

The same specifications as for the popularity of the SPO have been esti-mated for the OVP, the FPO, and the “remainder party”. The results for thespecifications discussed in detail for the SPO are given in Tables 3 to 5 forthese parties, respectively. In all cases, a structural break at the end of 1986 isindicated by Chow tests and is also obvious from a look at the coefficients ofthe regressors in the two different samples. To summarize the results of alter-native specifications for the individual parties, it turns out that the popularityof the OVP is nearly unaffected by all economic variables tried in the period1976.1 to 1986.4. For the period where the OVP was in office, all traditionaleconomic variables exert a negative influence on its popularity; this remainseven if trend variables are included to account for the dramatic losses of theOVP since 1986. This result is consistent with both hypotheses for the rate ofunemployment, with the responsibility hypothesis for the rate of inflation, and

69

with none for the income growth rates. The traditional economic variableshave negative insignificant coefficients in popularity functions for the FPO inthe first period. Here the theoretical hypotheses are not very clear as this partywas to some extent supportive of the SPO government and was in office from1983 to 1986, but was formally an opposition party for most of the period until1986. Attempts at taking this into account by introducing dummy variablesdid not yield interpretable results. After 1986, the economic variables havepositive (mostly insignificant) coefficients in regressions for the popularity ofthe FPO, in accordance with the responsibility hypothesis for the unemploy-ment rate, with the partisan theory for the growth rates, and with both for theinflation rate. Again, introducing a trend variable (positive coefficient for thisperiod) does not change the signs of the coefficients. Finally, the results forthe “remainder party” are those expected for an opposition party under theresponsibility hypothesis for both subperiods: increases in the rate of inflationand the rate of unemployment increase support for these parties, increasesin the income growth rate decrease their vote share, ceteris paribus. Takingthese results for all parties altogether, the specification with the rate of unem-ployment and the growth rate of nominal disposable income gives results inaccordance with the responsibility hypothesis for all parties for the subperiod1976.1 to 1986.4, whereas no specification gives results unambiguously inaccordance with any of the two theories for the later subperiod.

5. Simultaneous estimations of Austrian popularity functions

Next, we estimate systems of popularity functions for all four parties simul-taneously by seemingly unrelated regressions, using the approach (5) withrestrictions (6) to (8). We start from the specification with the three (lagged)economic variables rate of unemployment, rate of inflation, and growth rateof real disposable income as regressors. First, the restrictions of equality ofthe coefficients of the lagged endogenous variables (�) are tested pairwise forthe OLS regressions. The restrictions are rejected at all conventional signif-icance levels for the entire period 1976.1 to 1993.4, which again shows theinadmissibility of estimating over the entire sample due to the presence ofthe structural break. For the subperiod 1976.1 to 1986.4, in most cases therestrictions cannot be rejected at the 90% significance level, although in onecase (coefficients in the OVP and the FPO equations) the significance levelis only 50%. In the subperiod 1987.1 to 1993.4, the lowest probability ofrejection is again about 50%, this time for the coefficients of the OVP and the“remainder party” equations. Therefore we decide to accept the restrictionson � as a working hypothesis and estimate this specification simultaneouslyfor the two subperiods 1976.1 to 1986.4 and 1987.1 to 1993.4. The results are

70

Tabl

e3.

Pop

ular

ity

func

tion

sfo

rA

ustr

ia,O

LS

.Dep

ende

ntva

riab

le:V

P(t

)

Equ

atio

nT

ime

Exp

lana

tory

vari

able

sR

2 cSE

h

num

ber

peri

odC

onst

ant

VP(

t–1)

UR

(t–1

)IR

(t–1

)G

R(t

–1)

GN

(t–1

)

(14)

1976

.1–

25.2

370

0.41

039

0.10

680

–0.0

3900

–0.0

1007

80.

1344

71.

1576

–

1986

.4(3

.993

3)(2

.767

7)(0

.632

17)

(–0.

3059

1)(–

0.14

243)

(15)

1987

.1–

46.3

674

0.35

744

–3.3

007

–1.8

228

–0.4

3960

0.87

049

1.47

330.

4342

2

1993

.4(4

.251

9)(2

.125

7)(–

3.76

86)

(–2.

9638

)(–

2.36

17)

(16)

1976

.1–

25.6

704

0.39

999

0.11

510

–0.0

2970

80.

1574

41.

1421

1.35

70

1986

.4(4

.046

0)(2

.738

1)(0

.780

93)

(–0.

4044

9)

(17)

1987

.1–

32.2

964

0.66

021

–3.2

081

–0.5

5163

0.86

597

1.49

87–0

.414

13

1993

.4(3

.944

4)(6

.731

4)(–

3.67

92)

(–3.

0336

)

Tabl

e4.

Pop

ular

ity

func

tion

sfo

rA

ustr

ia,O

LS

.Dep

ende

ntva

riab

le:F

P(t

)

Equ

atio

nT

ime

Exp

lana

tory

vari

able

sR

2 cSE

h

num

ber

peri

odC

onst

ant

FP(t

–1)

UR

(t–1

)IR

(t–1

)G

R(t

–1)

GN

(t–1

)

(18)

1976

.1–

3.63

320.

5885

7–0

.245

33–0

.060

109

–0.0

6112

10.

4787

01.

0112

–

1986

.4(2

.203

6)(3

.619

8)(–

1.36

14)

(–0.

5403

6)(–

0.91

245)

(19)

1987

.1–

0.57

252

0.49

574

0.68

448

1.10

120.

2539

50.

6766

01.

6844

–

1993

.4(0

.111

34)

(2.7

861)

(0.8

0369

)(1

.754

6)(1

.184

0)

(20)

1976

.1–

3.76

330.

5916

4–0

.254

83–0

.073

742

0.49

512

0.99

513

–

1986

.4(2

.388

3)(3

.833

8)(–

1.49

21)

(–1.

1055

)

(21)

1987

.1–

–2.3

629

0.67

776

1.05

640.

3108

90.

6738

41.

6916

0.47

911

1993

.4(–

0.48

180)

(5.8

136)

(1.2

607)

(1.5

060)

71

Tabl

e5.

Pop

ular

ity

func

tion

sfo

rA

ustr

ia,O

LS

.Dep

ende

ntva

riab

le:R

(t)

Equ

atio

nT

ime

Exp

lana

tory

vari

able

sR

2 cSE

h

num

ber

peri

odC

onst

ant

R(t

–1)

UR

(t–1

)IR

(t–1

)G

R(t

–1)

GN

(t–1

)

(22)

1976

.1–

–1.3

156

0.47

066

0.97

820

0.00

1469

6–0

.032

693

0.88

483

0.85

734

–0.5

3316

z198

6.4

(–1.

6093

)(3

.528

1)(3

.872

5)(0

.015

217)

(–0.

6272

6)

(23)

1987

.1–

–0.6

3938

0.70

485

0.71

181

0.06

1368

–0.2

6481

0.78

644

1.06

00–0

.397

52

1993

.4(–

0.19

530)

(3.6

439)

(1.1

621)

(0.2

1656

)(–

1.98

73)

(24)

1976

.1–

–1.1

244

0.45

647

0.98

022

–0.0

3009

10.

8873

30.

8479

7–0

.723

24

1986

.4(–

1.62

95)

(3.5

269)

(3.9

325)

(–0.

5529

8)

(25)

1987

.1–

–1.1

118

0.84

683

0.71

404

–0.2

2798

0.78

534

1.06

27–1

.367

2

1993

.4(–

0.34

145)

(5.0

457)

(1.1

397)

(–1.

8176

)

72

given in Tables 6 and 7, respectively. They show that for all coefficients thesame signs are obtained as in the OLS regressions. The parameter � is about0.5 for both samples, which is comparable to estimates obtained for othercountries and can be interpreted to imply a fairly modest amount of myopia.This specification seems acceptable except for the rate of inflation not beingsignificant in any equation in the first subperiod.

Therefore we estimate the system with the (lagged) rate of unemploymentand the (lagged) growth rate of nominal disposable income as an alternative.This embodies the assumption of “money illusion” for the voters. Again, forthe entire period the restrictions on � are clearly rejected, showing again thestructural break in the data. For the first subperiod, equality of coefficientsof lagged endogenous variables cannot be rejected for the SPO and the FPOequation only with probability 0.34; in all other cases, the hypothesis of equalcoefficients cannot be rejected at conventional significance levels. For thesecond subperiod, equality of coefficients is accepted with probability 0.19only for the SPO and the remainder party equation and with probabilitiesabove 0.5 for the other pairs of coefficients. In spite of this weak evidencefor equal �, especially for the period 1987.1 to 1993.4, we also estimate thissystem of equations for these two subperiods. Results are given in Tables 8and 9. Again, for both samples the signs of all coefficients are the same asin the OLS regressions, and some parameters are significantly different fromzero which are not so in the OLS regressions. For the period 1976.1 to 1986.4,these estimates are also satisfactory from the point of view of the underlyingtheory, as they confirm the responsibility hypothesis. For the period since1987.1, none of the two systems (and none of several alternatives which wetried) gives estimates whose signs are without exception in accordance withone of the two hypotheses of the traditional popularity function, hence thisperiod deserves further investigation, which might not become conclusivebefore the emergence of additional observations.

From the point of view of public choice theory, it is not quite unexpectedthat during a period of a “grand coalition” government supported by a two-thirds majority of the electorate voters do not behave according to predictionsof either of the two hypotheses examined. In such a situation, political compe-tition takes place between the parties in office as well as between governmentand opposition. The two parties in government make each other responsi-ble for adverse economic developments; their success with the electorate indoing so may depend also on their ideological positions. For instance, whenunemployment rises, voters may hold the OVP but not the SPO responsiblefor that and may change their votes between the coalition parties instead ofbetween government and opposition. This means that elements of both theresponsibility and the partisan hypothesis will have to be combined to obtain

73

Tabl

e6.

Sys

tem

ofpo

pula

rity

func

tion

s,S

UR

.3ex

plan

ator

yva

riab

les.

Tim

epe

riod

:197

6.1–

1986

.4

Dep

ende

ntE

xpla

nato

ryva

riab

les

R2

SE

vari

able

Con

stan

tP j

(t–1

)U

R(t

–1)

IR(t

–1)

GR

(t–1

)

SP(t

)26

.603

–0.7

3548

0.10

533

0.11

882

0.77

461.

0877

(6.4

608)

(–4.

0205

)(0

.878

29)

(1.7

413)

VP(

t)22

.439

0.08

8287

–0.0

4328

3–0

.006

7576

0.21

091.

0927

(6.5

348)

(0.5

6538

)(–

0.36

038)

(0.1

0156

)0.

4769

9

FP(t

)4.

5644

(6.1

059)

–0.3

2049

–0.0

6453

3–0

.079

384

0.52

150.

9577

3

(4.1

462)

(–2.

2043

)(–

0.61

328)

(–1.

3317

)

R(t

)–1

.306

40.

9676

80.

0024

873

–0.0

3267

90.

8955

0.80

718

(–1.

7275

)(5

.597

9)(0

.027

781)

(–0.

6659

7)

Tabl

e7.

Sys

tem

ofpo

pula

rity

func

tion

s,S

UR

.3ex

plan

ator

yva

riab

les.

Tim

epe

riod

:198

7.1–

1993

.4

Dep

ende

ntE

xpla

nato

ryva

riab

les

R2

SE

vari

able

Con

stan

tP j

(t–1

)U

R(t

–1)

IR(t

–1)

GR

(t–1

)

SP(t

)12

.062

1.16

140.

1041

40.

4427

10.

5016

0.98

944

(2.4

775)

(2.3

617)

(0.4

4224

)(3

.569

1)

VP(

t)36

.981

–2.8

451

–1.3

428

–0.4

1761

0.88

541.

3609

(5.3

977)

(–3.

9226

)(–

3.16

41)

(–2.

4412

)0.

5162

0

FP(t

)5.

2465

(5.5

909)

0.66

553

1.04

510.

2496

00.

7244

1.52

71

(0.1

1280

)(0

.872

95)

(2.3

818)

(1.2

972)

R(t

)–1

.188

01.

0181

0.19

349

–0.2

7471

0.81

060.

9803

5

(–0.

3966

6)(2

.000

4)(0

.809

17)

(–2.

2337

)

74

Table 8. System of popularity functions, SUR. 2 explanatory variables. Time period:1976.1–1986.4

Dependent Explanatory variables R2 SE

variable Constant Pj(t–1) UR(t–1) GN(t–1)

SP(t) 26.842 –0.71988 0.14570 0.7794 1.0761

(6.7702) (–4.3859) (2.0417)

VP(t) 22.882 0.10340 –0.025174 0.2124 1.0917

(6.6597) (0.74223) (–0.36104)0.46478

FP(t) 4.9047 (5.9933) –0.34929 –0.090868 0.5224 0.95680

(4.9050) (–2.5969) (–1.4698)

R(t) –1.1070 0.96578 –0.029661 0.8952 0.80855

(–1.7667) (5.7025) (–0.57431)

Table 9. System of popularity functions, SUR. 2 explanatory variables. Time period:1987.1–1993.4

Dependent Explanatory variables R2 SE

variable Constant Pj(t–1) UR(t–1) GN(t–1)

SP(t) 5.6056 0.89433 0.44270 0.4983 0.99276

(1.3555) (2.1205) (3.8077)

VP(t) 30.621 –3.0652 –0.53987 0.8806 1.3892

(4.8644) (–4.2406) (–3.2541)0.68371

FP(t) –2.3150 (9.9467) 1.0343 0.30784 0.7101 1.5662

(–0.51540) (1.4527) (1.6521)

R(t) –2.2831 1.1365 –0.21068 0.8017 1.0030

(–0.78803) (2.4622) (–1.7943)

an adequate theoretical model for a political system characterized by a “grandcoalition” whose predictions can be confirmed empirically. Developing sucha model is outside the scope of the present paper.

From an empirical point of view, one possibility to discriminate betweenthe two systems of equations reported in this section (and between them andother alternatives tried, which are not reported here) consists in examiningtheir predictive performance. For this purpose, within-sample forecasts forthese two systems were calculated; both static (one-step ahead) and dynamicforecasts were determined. We also tried out-of-sample forecasts for thesecond subperiod derived from the equations for the first subperiod, but, asexpected, they result in very high forecasting errors due to the structuralbreak. The results of some forecast statistics for the within-sample forecastsderived from the systems summarized in Tables 6 to 9 are given in Tables 10

75

to 13, respectively. They show that for the subperiod 1976.1 to 1986.4, the twosystems deliver comparable forecasting performances, with the popularity ofsome parties being better predicted by one system and that of some othersby the other system. Although we cannot discriminate empirically betweenthe two systems for this period, we prefer the specification with the nominalgrowth rate (Tables 8 and 12) to that with the inflation and the real growth rate(Tables 6 and 10) for reasons of conformity with theoretical presumptions. Forthe sample 1987.1 to 1993.4, the system with the three independent variables(Tables 7 and 11) performs better than the one with the two regressors (Tables9 and 13), which shows that the rate of inflation has indeed an independentinfluence upon the popularity of the political parties in this period. Whetherthis specification stands up to scrutiny when more data become available,remains to be seen.

6. Rational-expectations models of political popularity for Austria

So far, we have gathered evidence about the influence of economic condi-tions on voters’ evaluations of political parties in Austria which at least forthe period of the SPO one-party government and the “small coalition” isconsistent with the responsibility hypothesis. In the international literature,several critical points have been raised about these kinds of popularity func-tions. Here we concentrate on one particular aspect of popularity functionswhich has been criticized, namely the assumption of backward-looking evalu-ations of political parties by voters, which presumes voters’ myopia or at leastadaptive expectations. If voters have rational expectations, on the other hand,their expectations of future values of economic (and non-economic) variableswill affect political popularity. Two models of voters’ behavior incorporat-ing rational expectations have been proposed by Holden and Peel (1985); seealso Byers (1991). Both are based on an analogy with Hall’s (1978) consump-tion function, according to whom consumption follows a random walk; theyimply that the best predictor of future political popularity is the current levelof political popularity. The essential ideas of these models are also containedin Kirchgassner (1985).

In their “permanent benefits” model, Holden and Peel assume that votersprefer that party which gives them the highest expected stream of futureutilities by its policies. In this case, political popularity depends on expectedutilities derived from the party being in power. If expectations are formedin a rational manner, then changes in expected utility occur only when newinformation arises, and hence are white noise processes; in particular, they donot depend on past informations. If also a random walk disturbance reflecting

76

Tabl

e10

.Fo

reca

stst

atis

tics,

with

in-s

ampl

efo

reca

sts.

Sys

tem

ofTa

ble

6

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)1.

0876

70.

7844

71.

6267

30.

0112

641.

1846

00.

8785

51.

8204

80.

0122

73

VP(

t)1.

0926

60.

8352

61.

9315

70.

0126

961.

1796

80.

9036

92.

0860

60.

0137

12

FP(t

)0.

9577

30.

6281

910

.977

900.

0815

791.

0659

70.

8230

014

.986

710.

0903

73

R(t

)0.

8071

80.

5733

625

.553

020.

1044

90.

8589

60.

6265

628

.302

320.

1120

4

RM

SEis

the

root

-mea

n-sq

uare

erro

r,M

AE

isth

em

ean

abso

lute

erro

r,M

APE

isth

em

ean

abso

lute

perc

enta

geer

ror,

The

ilis

The

il’s

ineq

ualit

yco

effic

ient

.

Tabl

e11

.For

ecas

tsta

tistic

s,w

ithin

-sam

ple

fore

cast

s.S

yste

mof

Tabl

e7

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)0.

9894

50.

7876

61.

8759

50.

0117

860.

9356

70.

7463

41.

7868

10.

0111

48

VP(

t)1.

3609

01.

0894

83.

1944

40.

0201

801.

3819

71.

0698

13.

1005

40.

0205

05

FP(t

)1.

5270

81.

2517

28.

2265

20.

0466

501.

7211

51.

3753

58.

9483

60.

0527

04

R(t

)0.

9803

60.

8003

210

.259

440.

0565

181.

1850

10.

9346

511

.735

140.

0678

41

77

Tabl

e12

.Fo

reca

stst

atis

tics,

with

in-s

ampl

efo

reca

sts.

Sys

tem

ofTa

ble

8

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)1.

0760

60.

7830

81.

6247

10.

0111

441.

1459

20.

8515

31.

7661

20.

0118

72

VP(

t)1.

0916

60.

8304

81.

9202

10.

0126

841.

1800

60.

9122

22.

1056

50.

0137

16

FP(t

)0.

9568

00.

6255

110

.943

730.

0815

101.

0685

80.

8187

414

.916

030.

0906

25

R(t

)0.

8085

50.

5696

724

.960

970.

1046

70.

8615

40.

6166

027

.140

130.

1124

4

Tabl

e13

.For

ecas

tsta

tistic

s,w

ithin

-sam

ple

fore

cast

s.S

yste

mof

Tabl

e9

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)0.

9927

60.

7880

51.

8663

50.

0118

251.

0813

50.

7751

41.

8347

20.

0128

82

VP(

t)1.

3892

31.

1284

23.

2974

00.

0206

021.

5833

11.

2844

43.

7168

00.

0235

21

FP(t

)1.

5662

01.

3152

88.

6390

30.

0478

501.

8916

81.

5557

710

.183

180.

0580

58

R(t

)1.

0030

40.

7976

310

.439

390.

0578

561.

4328

11.

1088

114

.990

640.

0819

21

78

measurement error in the opinion polls is taken into account, the “permanentbenefits” model results in the following specification:

∆G(t) � G(t)� G(t� 1) = bu(t) + v(t)� v(t� 1); (26)

where u(t) and v(t) are white noise processes reflecting new information andmeasurement errors, respectively. It can be shown that the right-hand sideof equation (26) follows a first-order moving average process with a nega-tive coefficient. Thus, according to the “permanent benefits” model, politicalpopularity follows an ARIMA (0,1,1) process.

The second model of Holden and Peel, the “stock of goodwill” model,combines the assumption of rational expectations with the idea of loyalty to aparty. Voters evaluate political parties according to their accumulated “stockof goodwill”. The stock of goodwill changes due to depreciation and whennew information becomes available; again, new information follows a whitenoise process. If there is also a disturbance term in the relation between thestock of goodwill and political popularity, then this model gives the followingspecification:

G(t) = c0 + �G(t� 1) + �u(t) + v(t)� �v(t� 1); (27)

where u(t) and v(t) are again white noise processes reflecting new informationand the disturbance in the stock of goodwill-to-popularity relation, respec-tively; � is equal to one minus the depreciation rate of the stock of goodwill.Now the popularity of the party in government follows an ARIMA (1,0,1)(or ARMA (1,1)) process, again with a negative coefficient of the movingaverage parameter. Both models imply that popularity is only determinedby its previous value and an error term, but not by other current or laggedvariables. Analogous equations can be formulated for other parties than theone in office.

Both rational-expectations models have been estimated for Austria usingBox-Jenkins techniques. Tables 14 and 15 give the results for the ARIMA(0,1,1) process for the two subperiods, and Tables 16 and 17 give those forthe ARMA (1,1) process for all four parties. Estimations have also been triedfor the entire period 1976.1 to 1993.4, but can be rejected again: At leastfor one party, the moving average term becomes positive, which contradictsthe underlying theory, and for the ARMA process, the autoregressive termis close to one (or even greater than one) in all cases, indicating unit rootsagain. From the results of Tables 16 and 17, we conclude that the ARMA(1,1) process is also unacceptable for the two subperiods, because the movingaverage term is positive for two parties in the first subperiod and for one partyin the second; the autoregressive term is greater than one for one party inthe second subperiod. Moreover, the restriction that popularity must not be

79

Table 14. ARIMA (0,1,1) model of popularity. Time period: 1976.1–1986.4

Dependent Constant Moving average R2c SE Q Q�

variable parameter

∆SP(t) –0.1653571 –0.3348655 0.035209 1.284777 9.40 11.50

(–0.8532063) (–2.2651592)

∆VP(t) –0.0272009 –0.2313613 0.038925 1.287991 10.64 13.09

(–0.1395581) (–1.9737700)

∆FP(t) 0.0969651 –0.0144962 –0.023676 1.056831 4.45 5.54

(0.6037239) (–0.0715320)

∆R(t) 0.1248703 –0.3487361 0.077588 0.944029 13.30 15.99

(0.8761091) (–2.3521794)

Q is the Box-Pierce Q (12)-statistic, Q� is the Ljung-Box Q (12)-statistic.

Table 15. ARIMA (0,1,1) model of popularity. Time period: 1987.1–1993.4

Dependent Constant Moving average R2c SE Q Q�

variable parameter

∆SP(t) –0.0864080 –0.4470450 0.104919 1.331467 8.17 12.04

(–0.3433736) (–2.5398221)

∆VP(t) –0.5012713 –0.5840291 0.079398 1.776195 9.67 12.43

(–1.4757022) (–3.6747283)

∆FP(t) 0.2938552 –0.1401586 –0.030466 1.891302 5.09 6.58

(0.8221266) (–0.7178313)

∆R(t) 0.3402800 –0.2432095 0.019210 1.176591 8.56 12.71

(1.5272862) (–1.5623163)

greater than 100% is violated for the implied long-run values of two parties’popularities in each subperiod. This means that the “stock of goodwill” modelhas to be rejected for Austria. The ARIMA (0,1,1) process, on the other hand,gives estimates in accordance with the theory: The moving average termsare always negative (though not always significant), and the constants arenot significantly different from zero, as implied by the “permanent benefits”model. On the basis of conventional diagnostic statistics or of an examinationof the autocorrelations and partial autocorrelations, this model cannot berejected for either subperiod.

To examine the comparative advantages of the structural models of popu-larity of the previous section and those of the rational-expectations models,the explanatory economic variables have been included in the estimation ofthe ARMA (1,1) and the ARIMA (0,1,1) processes. The ARMA (1,1) and theARIMA (0,1,1) model with explanatory economic variables added can also be

80

Tabl

e16

.A

RM

A(1

,1)

mod

elof

popu

lari

ty.T

ime

peri

od:1

976.

1–19

86.4

Dep

ende

ntC

onst

ant

Mov

ing

aver

age

Aut

oreg

ress

ive

R2 c

SEQ

Q

�

vari

able

para

met

erpa

ram

eter

SP(t

)45

.460

9191

–0.2

3753

200.

9433

296

0.68

8760

1.29

3014

7.00

8.73

(7.0

3795

31)

(–1.

2331

453)

(8.4

9838

09)

VP(

t)42

.996

100

0.09

1594

40.

3690

761

0.15

7410

1.14

2158

9.58

11.8

5

(157

.354

82)

(0.3

0821

97)

(1.3

8997

28)

FP(t

)5.

9900

018

0.26

5270

50.

6440

096

0.48

6102

1.00

3977

3.68

4.64

(13.

6224

30)

(1.2

3756

32)

(4.8

6187

84)

R(t

)9.

8310

190

–0.3

2144

750.

9821

856

0.85

7548

0.95

3468

13.6

416

.45

(0.3

4652

17)

(–1.

8270

190)

(14.

5414

78)

Tabl

e17

.AR

MA

(1,1

)m

odel

ofpo

pula

rity

.Tim

epe

riod

:198

7.1–

1993

.4

Dep

ende

ntC

onst

ant

Mov

ing

aver

age

Aut

oreg

ress

ive

R2 c

SEQ

Q

�

vari

able

para

met

erpa

ram

eter

SP(t

)41

.768

639

–0.2

8385

000.

7126

611

0.21

9248

1.26

1111

8.47

12.4

5

(45.

4190

59)

(–0.

6352

692)

(1.8

0424

75)

VP(

t)24

.061

787

–0.3

7365

020.

9468

845

0.80

2423

1.81

9689

7.12

9.43

(1.3

0879

49)

(–1.

7228

241)

(9.8

4723

03)

FP(t

)16

.901

024

0.10

1599

70.

7226

162

0.65

6365

1.73

6331

3.76

5.47

(13.

4086

67)

(0.4

5501

63)

(6.2

7927

49)

R(t

)6.

5232

043

–0.5

8856

891.

1998

523

0.78

1347

1.07

2538

10.8

916

.76

(4.6

5268

22)

(–3.

0935

238)

(10.

1125

30)

81

interpreted as traditional popularity function models including depreciationof popularity and measurement errors, respectively; the rational-expectationsmodels are then nested within these general models and can easily be tested bychecking whether the economic variables have significant influences (whichthey should not according to the rational-expectations hypothesis) withinthese encompassing models. Estimating such ARIMAX-models also servesto invalidate the objection of unfairness against comparisons between timeseries models containing measurement errors and depreciation of popularityon the one hand and structural models lacking these aspects on the other oneby integrating both within one equation. For the ARMA (1,1) process witheconomic variables included, we get the result that the economic variableshave nearly always significant coefficients in those equations where they do soin the OLS regressions without a moving-average term; in addition, in somecases the moving-average term is positive. This means again a rejection of the“stock of goodwill” model. For the ARIMA (0,1,1) process with economicvariables added, fewer economic variables have significant coefficients thanin the OLS regressions, but some do, especially for the second subperiod. Thiscasts some doubt also on the “permanent benefits” model, because accordingto the theory of rational expectations no systematic influence should be exert-ed by any (lagged) economic variable beyond that contained in the laggedendogenous variable.

We have also examined the predictive performance of the two rational-expectations models in the same way as we have done for the structuralmodels of the simultaneous popularity functions. Tables 18 and 19 show theforecast statistics obtained from the ARIMA (0,1,1) processes for the two sub-periods. They can be compared directly to those obtained from the structuralmodels given in Tables 10 to 13. It turns out that the within-sample fore-casting performance is virtually always better for both structural models thanfor the rational-expectations model of the ARIMA (0,1,1)-type, regardlessof the forecast statistic applied. Analogous simulations have been done withthe ARMA (1,1) process estimates; here the forecast performance is mostlystill worse than with the ARIMA (0,1,1) process. From this, we concludethat both rational-expectations models are outperformed by the structuralpopularity functions, at least as far as their within-sample predictive perfor-mance is concerned. This verdict need not hold for out-of-sample forecasts,on the other hand. For instance, when forecasts for the second subperiodare based on ARMA (1,1) or ARIMA (0,1,1) results from the first subperi-od, they are in several cases better (though still of poor quality) than thoseobtained from the structural models for the first subperiod. This means thatfor purely predictive purposes, time series models of political popularitymight be superior to those of the traditional popularity function, at least when

82

structural breaks are present. Tentative estimations of ARMA and ARIMAprocesses of higher orders than those implied by the rational-expectationsmodels show that they may produce better forecasts. Higher-order autore-gressive and moving-average terms in several cases become significant andimprove goodness-of-fit measures. However, this does not say anything aboutrational expectations models, as these processes have to be considered aspurely statistical time series models without any politico-economic theoreti-cal background. For those models where such a background can be provided,rational-expectations models show inferior performance than those based ontraditional popularity functions.

Another type of test for the rational-expectations hypothesis concerningpopularity functions has been proposed by Kirchgassner (1985), based on asimilar procedure for other macroeconomic variables due to Mishkin (1983).According to the rational-expectations hypothesis, voters should not hold thegovernment responsible for the expected parts of the economic variables; atleast, expected changes in economic variables should not affect the popularityof the party in office (or of any other party). One has, therefore, to distinguishbetween expected and unexpected economic developments. This can be doneby explaining economic variables by regressions on past values of economicvariables and regarding the systematic part of such a regression as the expect-ed and the residual as the unexpected part of the economic variable. Both partscan then be included separately in the regression equations of the popularityfunction. We have implemented this procedure for both systems of popularityfunctions shown in Tables 6 to 9. As a first step, the independent economicvariables of each regression have been decomposed into expected and unex-pected parts according to VAR estimations of these variables regressed on allthose economic variables lagged up to the fourth quarter which appear in therespective specification of the popularity function. Next, both the expectedand the unexpected part of each economic variable were taken as regressorsin the popularity functions; again systems of popularity functions using spec-ification (5) with restrictions (6) to (8) were estimated. Tables 20 and 21 givethe results for our preferred specifications (with nominal income growth forthe first and real growth and inflation for the second subperiod); the resultsfor the other specifications (and for alternatives not reported here) are quitesimilar. In most cases, the expected part of the respective economic variableexerts a more significant influence on the popularity of the political partiesthan the unexpected part, although the absolute value of the influence ofthe unexpected part may be larger. Moreover, in most cases the directionof the influence of the expected and the unexpected part of the respectivevariable is the same. These results can be seen as another argument against

83

Tabl

e18

.Fo

reca

stst

atis

tics,

with

in-s

ampl

efo

reca

sts.

Sys

tem

ofTa

ble

14

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)1.

2552

380.

9712

182.

0097

800.

0130

001.

7524

541.

3886

592.

8422

210.

0183

67

VP(

t)1.

2583

780.

9315

132.

1602

450.

0146

211.

3402

481.

0791

452.

4883

940.

0155

61

FP(t

)1.

0325

330.

7040

1912

.182

100.

0874

352.

3356

551.

7655

4437

.770

650.

1838

05

R(t

)0.

9223

250.

6179

3928

.652

820.

1192

611.

6366

651.

3413

7710

9.07

470.

2038

01

Tabl

e19

.For

ecas

tsta

tistic

s,w

ithin

-sam

ple

fore

cast

s.S

yste

mof

Tabl

e15

Var

iabl

eSt

atic

sim

ulat

ion

Dyn

amic

sim

ulat

ion

RM

SEM

AE

MA

PET

heil

RM

SEM

AE

MA

PET

heil

SP(t

)1.

2830

340.

9430

152.

2393

140.

0152

831.

3798

941.

0781

932.

5823

600.

0164

38

VP(

t)1.

7115

841.

4164

684.

2267

010.

0253

782.

1590

311.

7059

855.

1918

570.

0313

66

FP(t

)1.

8225

041.

4956

249.

7806

930.

0554

743.

5423

193.

0553

7618

.398

740.

1192

44

R(t

)1.

1337

920.

9114

4511

.470

880.

0656

172.

8814

032.

4680

8130

.497

780.

1454

78

84

the rational-expectations hypothesis, showing that also anticipated economicdevelopments change voters’ evaluations of political parties in Austria.

It may be criticized that we have only gathered indirect evidence againstthe rational-expectations hypothesis for political popularity here. More recentstudies on the importance of expectations in the popularity function for oth-er countries apply a more direct approach, using survey data on measuredexpectations. This provides a promising approach for building new micro-foundations of voters’ behavior. Although most of these recent empirical stud-ies confirm backward-looking behavior of the voters, thereby also rejectingthe rational-expectations hypothesis (see Nannestad and Paldam, forthcom-ing, for Denmark), some contrary results are available, too (see Price andSanders, 1995, for the UK). In the light of these studies, the issue of forward-versus backward-looking expectations is still an open question, and our neg-ative results on the implications of the rational-expectations hypothesis forAustria should be considered as preliminary evidence only.

7. Concluding remarks

In this paper, we have presented econometric evidence on the influence ofmacroeconomic variables on the popularity of political parties in Austria. Therate of unemployment and the growth rate of disposable income, more recent-ly also the rate of inflation have been identified as economic determinants ofvoters’ evaluations of political parties. There is clear evidence for a struc-tural break in the popularity functions related to the change from a one-partyand “small coalition” government to a “grand coalition” government. For theformer period, the predictions of the responsibility hypothesis for the popu-larity functions are confirmed, whereas for the latter period, results are moreambiguous. It has been shown that the structural popularity function modeloutperforms rational-expectations models of political popularity; implica-tions of the rational-expectations hypothesis have been falsified. Althoughsome doubts remain about the explanation of political popularity during the“grand coalition” period, the superiority of traditional popularity functionsover rational-expectations models seems to be well established by our resultsfor Austria.

Further research should try to bring together the largely unrelated publicchoice and sociological attempts at explaining voters’ behavior in Austria inorder to arrive at a more comprehensive view of political-economic processesin this country. Sociological studies, which focus on voters’ social posi-tions, party identifications, leadership images and issue positions as well ason economic perceptions, usually use individual-level survey data, in con-trast to popularity function studies using aggregated data. Recently, however,

85

Tabl

e20

.S

yste

mof

popu

lari

tyfu

ncti

ons,

SU

R.

Exp

ecte

d(e )

and

unex

pect

ed(u

)ex

plan

ator

yva

riab

les.

Tim

epe

riod

:19

77.1

–198

6.4

Dep

ende

ntE

xpla

nato

ryva

riab

les

R2

SE

vari

able

Con

stan

tP j

(t–1

)U

Re (

t–1)

UR

u(t

–1)

GN

e (t–

1)G

Nu(t

–1)

SP(t

)27

.364

3–0

.720

587

–0.0

4685

50.

2377

440.

0710

220.

7846

371.

0469

0

(6.6

1205

)(–

4.30

975)

(–0.

0464

17)

(2.0

4499

)(0

.554

674)

VP(

t)24

.276

00.

1119

42–1

.589

58–0

.076

542

0.02

6414

0.30

4007

1.05

262

(6.4

8641

)(0

.798

264)

(–1.

5635

1)(–

0.68

1680

)(0

.205

002)

0.44

0354

FP(t

)5.

1204

3(5

.364

48)

–0.3

8902

01.

0537

9–0

.084

317

–0.1

0891

70.

5532

160.

9702

32

(4.2

3289

)(–

2.72

865)

(1.1

2631

)(–

0.82

0357

)(–

0.92

2487

)

R(t

)–0

.796

101

0.99

7665

0.58

2643

–0.0

7688

5–0

.011

480

(–0.

9399

44)

(5.6

0703

)(0

.725

694)

(–0.

8610

14)

(–0.

1133

01)

86

Tabl

e21

.S

yste

mof

popu

lari

tyfu

ncti

ons,

SU

R.E

xpec

ted

(e )an

dun

expe

cted

(u)

expl

anat

ory

vari

able

s.T

ime

peri

od:1

987.

1–19

93.4

Dep

ende

ntE

xpla

nato

ryva

riab

les

vari

able

Con

stan

tP j

(t–1

)U

Re (t

–1)

UR

u(t

–1)

IRe (t

–1)

IRu(t

–1)

GR

e (t–1

)G

Ru(t

–1)

R2

SE

SP(t

)14

.064

51.

1390

93.

6476

20.

1160

530.

5948

420.

4956

030.

3668

520.

5393

800.

9512

33

(2.7

7483

)(1

.931

45)

(1.8

9282

)(0

.427

214)

(0.9

1251

3)(2

.738

89)

(2.1

3641

)

VP(

t)39

.959

8–3

.073

552.

1330

0–1

.451

38–0

.319

181

–0.3

2455

0–0

.581

685

0.90

8801

1.21

579

(5.6

5113

)(–

3.94

693)

(0.8

6606

5)(–

3.32

322)

(–0.

3766

86)

(–1.

4102

0)(–

2.65

166)

0.46

6765

FP(t

)2.

3367

2(5

.172

31)

0.63

9775

–5.6

9816

0.98

1498

0.08

8999

0.02

3231

0.50

8700

0.78

9692

1.33

445

(0.4

3025

3)(0

.782

896)

(–2.

1075

0)(2

.158

45)

(0.0

9746

9)(0

.091

938)

(2.0

7261

)

R(t

)–3

.037

531.

2946

8–0

.082

466

0.35

3830

–0.3

6466

0–0

.194

284

–0.2

9386

7

(–0.

7774

88)

(2.1

2397

)(–

0.04

2020

)(1

.268

01)

(–0.

5480

56)

(–1.

0561

5)(–

1.70

261)

87

there have been attempts in the international literature to combine individualcharacteristics, voters’ assessments of their own and of the macroeconomicsituation, and objective economic factors in explaining political popularity.Pure or pooled cross-section analyses are usually applied for this purpose,and actual measurements of voters’ expectations from survey data are used.Data requirements preclude such an analysis for Austria at the moment, butit should be ranked highly in the agenda for further research.

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