It is widely understood that climate affects the spatial distribution of homelessness—warm places have on average higher rates of unsheltered homelessness than cold places. A less recognized fact is that variation in rates of unsheltered homelessness is higher in warm places as well. We document this fact using quantile regression techniques and show that it has important implications for estimating the determinants of homelessness across communities. In particular, housing prices, poverty rates and religiosity are much more strongly associated with rates of unsheltered homelessness in warm places than in cold places. As an alternative to splitting the sample, we find that logarithmic transformations of rates of unsheltered homelessness can be reliably used in a pooled sample. Associations between total homelessness and important covariates also vary across warm and cold places, in this case in terms of both rates and logarithms. Ultimately, future research should carefully account for climate when estimating the determinants of homelessness.
ASJC Scopus subject areas
- Economics and Econometrics