The Effect of US Unemployment Rate on US Congressional Elections

• Main Authors: Yun-Hsuan Kuo, 郭芸瑄
• Other Authors: David Kleykamp
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/25593974117249093389

碩士 === 淡江大學 === 美洲研究所碩士班 === 101 === The 2010 United States House of Representatives elections, Republicans gained control of the chamber, picking up a net total of 63 seats and erasing the gains Democrats made in 2006 and 2008. We also saw the rise of the Tea Party movement ushering in many conservative newcomers to the House. And this was due to the high unemployment rate and bad economic conditions, but why the unemployment rate so important and can make a major change in election? The empirical model we propose to use here is actually a unique combination of two different types of models – a fixed effect panel regression and a binary logit model. In the first stage a fixed effects panel regression model is estimated using the binary variable (1 = incumbent party returned to power and 0 = incumbent party is not returned to power).Hence, the single fixed effects variable described above is then introduced into a binary logit model involving the same set of explanatory variables as the linear probability model in the first stage. By doing so, a set of very specialized effects for the panel in question can be aligned with the logit model and a better analysis of the probability of reelection can be obtained. A conservative estimate shows that a rise in the unemployment rate of 2% and 4% across ALL congressional districts lowers the probability of the incumbent party being returned to office by about 3.6% and 8.4%, respectively. This effect is greatly magnified when a lagged dependent variable is added to the logit model. In such as case, a 2% increase in unemployment across all congressional districts lowers the average probability of return the incumbent party by 7.2%, while a 4% increase in unemployment reduces the probability by an impressive 18.5%.