Political Parties and the Variable Duration of Business Cycles

Abstract

A number of recent studies explore how political factors influence real economic activity. With electoral models, economic performance peaks near the election, as in the retrospective model of Nordhaus [30]. Electoral cycles also emerge (at least in fiscal policy) in the rational-expectations model of Rogoff and Sibert [34] and Rogoff [33]. With partisan models, economic activity differs systematically by party, as in both the ideological model of Hibbs [26] and the rationalexpectations model of Alesina [1]. Economic activity surges after election of Democratic presidents and recedes after election of Republican presidents. Although recent tests of the electoral approach are supportive [21; 22; 24; 25], the partisan approach enjoys the more compelling support [2; 3; 9; 10; 23; 26; 27]. The electoral and partisan views are not mutually exclusive, as the possibility of conflict between ideology and the desire to be reelected as the election approaches is well recognized. Indeed, our evidence here is consistent with a combined electoralpartisan model.' The macroeconomic literature at large, however, has been slow to absorb accumulating evidence that the party affiliation of the incumbent president, together with the timing of the electoral period, are linked to real economic activity. In this study, we gauge the significance of these links for the United States and relate them to two important strands of the macroeconomic literature. One strand, the time-series literature, investigates the statistical properties of measures of real economic activity, finding that these measures often exhibit nonconstant means and heteroscedastic variances. In autoregressive, integrated, moving-average (ARIMA) models, the conditional mean is a function of past innovations, while the conditional variance is constant. Applications of ARIMA and related models, beginning with Nelson and Plosser [29], suggest that real economic activity for the United States exhibits near random-walk behavior. In autoregressive, conditional heteroscedastic (ARCH) models, the conditional variance is a function of past (squared) innovations, while the conditional mean is constant. Applications of ARCH and related models, beginning with Engle [17], suggest that real activity exhibits heteroscedastic variances.