Modelling the social and psychological impacts of transport disadvantage
- First Online:
- Cite this article as:
- Currie, G. & Delbosc, A. Transportation (2010) 37: 953. doi:10.1007/s11116-010-9280-2
- 838 Downloads
This paper presents the results of a research project aiming to develop a robust empirical model to measure links between transport disadvantage (TD), social exclusion (SE) and well-being (WB). Its principal aim is to improve on current research methods in this field. Existing approaches derive associations between TD and its impacts through simple comparative methods, through qualitative methods and using limited and prescriptive definitions of SE. The new method draws from an interview questionnaire measuring TD through self-reported difficulties with transport. A principal components analysis of responses identifies four statistically significant sub-scales (transit disadvantage, transport disadvantage, vulnerable/impaired and rely on others). SE is represented in five dimensions including income, unemployment, political engagement, participation in activities and social support networks. Well-being adopts standard psychological measures—‘Satisfaction With Life Scale’ (SWLS), ‘Positive Affect’ (PA) and ‘Negative Affect’ (NA). Structural equation modelling (SEM) was used to model links between TD, SE and WB. A hypothesised model proposed negative associations between SE and WB and between TD and WB and a positive association between TD and SE. Modelling results showed that scales used to measure TD, SE and WB were all statistically related to their underlying concepts. Modelling of the hypothesised links between constructs was generally favourable with a good statistical fit. However the relationship between TD and WB was not significant. An exploratory analysis supported the hypothesis that this was caused by high reported travel difficulties for both highly mobile and less mobile people. A revised theoretical model explored the theory that feelings of isolation due to time poverty might be mediating the TD-WB link. SEM analysis of the revised model confirmed a good model fit with statistically significant measures between TD, time poverty and WB. Time poverty was not found to be associated with social exclusion. The final model suggested that TD is positively associated with SE with a measured strength of .27. SE is strongly negatively associated with WB (−.87). TD is positively associated with time poverty (.19) while time poverty is negatively associated with well-being (−.14). Areas for future research are identified.