Business Cycles – Electoral Cycles. The Influence Of The Economy On Vote Intention In Local Elections In Romania

A basic assumption of the political business cycles theory is that voters will evaluate the results of the economic policies of the parties in power, in order to decide who to vote for. A possible point of interest is to test the relationship between economic variables at the county level – unemployment, average wage – and the vote in the local elections, in each county. The theoretical framework in this case contains two fundamental perspectives: the first is around the theory of political business cycles developed by William Nordhaus; the second is closely linked with voting behavior theory. We have formulated two hypotheses, continuing the work of Dorin Jula in "Economic impact of political cycles - the relevance of European experiences for Romania" (Jula, 2001), for the next three electoral cycles: 2004, 2008 and 2012. The first hypothesis is that there is a negative correlation between the vote for the party (parties) in power and the unemployment rate; and a positive correlation between the vote for the party (parties) in opposition and unemployment rate, respectively. The second hypothesis is that the voters use an ideological approach, meaning that those faced with unemployment and from the poorest counties will tend to vote for the leftist parties. The first hypothesis was not confirmed for all electoral cycles. However, in the election where the economy was the dominant theme (local elections in 2012), there is a strong correlation in the logic proposed by it. The second hypothesis is verified; there is a correlation between the unemployment rate in counties and the vote for the leftist party, with PSD getting more votes in counties with higher unemployment rate. However, when introducing other possible explaining variables in the model, most notable a variable to account for regional vote – the impact of the unemployment variable decreases and a significant part of the variance in the model is attributable to the “region” variable.