Geography, Climate and Life Satisfaction

Individual life satisfaction (LS), used as a proxy measure of human wellbeing, has been a growing topic of research in the discipline of economics over the last 30 years. The underpinning rationale is the need to determine the extent to which various contextual factors contribute to LS, thereby informing policymaking decisions designed to enhance LS in the population. Empirical studies have predominantly focused on economic factors like income and employment and socio-demographic variables, e.g., age, gender, education and health. This paper presents a review of the LS research literature, focusing on the less widely researched influences of geography, environmental factors, and climate. The review identifies knowledge gaps in these specific areas and develops a research agenda to address these gaps. The review finds evidence of significant variations in individual LS, and in influencing factors, between different locations within the same country. While these variations’ nature and significance appear largely dependent on the scale of geographic aggregation used in the data, clear patterns for these spatial variations are yet to be identified and understood. Building on recent studies undertaken in Australia, further research using techniques such as geographically weighted regression at different scales of observation could provide insights into both where and why spatial clusters occur. The paper also examines the evidence regarding the contribution of environmental and climatic factors to individual LS. Variables associated with environmental amenities (such as urban greenspace or the presence of a world heritage area) generally have statistically significant positive coefficients in LS regression models; conversely, variables representing environmental dis-amenities like air or noise pollution have been found to be negative contributors. The evidence on the impact of climatic variables is more patchy, and their association with individual LS appears to be more ambiguous. This may be linked to context-specific conditions creating a high level of correlation between the different climate factors and other environmental factors: e.g., a positive correlation between precipitation levels and LS may be driven by the fact that high rainfall is generally associated with lush landscapes and scenic beauty. The paper argues that the investigation of the contribution of the climate and environment on individual LS requires the building of complex regression models integrating multiple variables. The research agenda proposed in the final section of this paper articulates two research objectives based on the above research gaps. It formulates research questions and lays out a research strategy to answer these questions.