A Consumer Surplus Estimate of Peace & Love festival in Borlänge : A Travel Cost Approach

This thesis uses zonal travel cost method (ZTCM) to estimate consumer surplus of Peace & Love festival in Borlänge, Sweden. The study defines counties as zones of origin of the visitors. Visiting rates from each zone are estimated based on survey data. The study is novel due to the fact that mostly TCM has been applied in the environmental and recreational sector, not for short term events, like P&L festival. The analysis shows that travel cost has a significantly negative effect on visiting rate as expected. Even though income has previously shown to be significant in similar studies, it turns out to be insignificant in this study. A point estimate for the total consumer surplus of P&L festival is 35.6 million Swedish kronor. However, this point estimate is associated with high uncertainty since a 95 % confidence interval for it is (17.9, 53.2). It is also important to note that the estimated value only represents one part of the total economic value, the other values of the festival's totaleconomic value have not been estimated in this thesis.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.