Analyzing the Effects of Amenities, Quality of Life Attributes and Tourism on Regional Economic Performance using Regression Quantiles

Conventional wisdom argues that tourist expenditures and recreation activities generate demands for traded goods and services, and create jobs and income for local residents in counties endowed with rich natural amenities. However, more recent studies have suggested that regions with high levels of amenities can experience lower wages and higher unemployment because amenities are capitalized into wages and rents in a manner that can hinder economic growth. Attempting to estimate the impact of tourism and retirement activities on the local economy, a few studies have performed multiple least squares regression analysis to discount activities generated by both local residents and nonresidents who travel for purposes other than tourism. However, the least square regression provides nothing more than an estimate of the average of the response (dependent) variable conditioned on the covariates (independent variables). In almost all regression settings with the exception of the rather naive constant- error-variance setup, the upper and lower quantiles (percentiles) often depend on the covariates quite differently from the mean or the median response. Investigating quantiles other than the mean or median using quantile regression analysis, we have found interesting dependency effects that cannot be discovered otherwise. The results of this analysis provide crucial information to policy makers while discussing public policy effectiveness in natural resource management and community development. If policy is to rely on the structural shift that is taking place in rural America, we need a better understanding on how amenities, quality of life attributes, and tourism affect regional economic performance.