Combining disparate surveys across time to study satisfaction with life: the effects of study context, sampling method, and transport attributes

Abstract

Satisfaction with life is a self-reported measure of the quality of life that has become a critical societal indicator extensively used for the evaluation and comparison of a wide range of trends and policies. This study fuses five cross-sectional travel surveys conducted from 1992 to 2018 across various geographical locations in California. Using the fused sample, we develop generalized ordered logit models to examine the effects of demographic characteristics, travel-related attributes, general and transport-related attitudinal variables, and context-control variables on individuals’ self-reported measures of life satisfaction. We find that longer commute times, mobility limitations, and a tendency to see travel as a waste of time are negatively associated with life satisfaction. To enable the use of disparate cross-sectional survey data, we incorporate context-control variables into the models. We find that life satisfaction appears to be increasing as GDP per capita increases. Among employed people, the macro-scale unemployment rate positively influences their life satisfaction. Interestingly, all else equal, we find that online opinion panel respondents have lower life satisfaction relative to respondents from other sampling methods (mainly address-based sampling), a finding that should be considered in future research using these sampling methods. Overall, this study provides a unique look at life satisfaction within a transport context, while providing an example of fusing small-scale survey datasets to study longitudinal, domain-specific, influences on variables like subjective well-being.