Domains and indicators of life satisfaction: case studies in Costa Rica and Northern Australia

Chacón Calvo, Adriana (2016) Domains and indicators of life satisfaction: case studies in Costa Rica and Northern Australia. Masters (Research) thesis, James Cook University.

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Abstract

Measuring the progress of nations by only focusing on economic growth is inadequate. New measures such as life satisfaction have been put forward as an option to use alongside gross domestic product (GDP). The notions of life satisfaction or subjective wellbeing have been around for many years as central elements of quality of life, but until recently they were not generally accepted as serious, replicable indicators. During the last two decades, however, there has been an increasing body of evidence showing that life satisfaction can be measured in surveys, and that these are reliable and valid measures.

There is a large and growing body of research that seeks to learn more about the contribution different factors make to overall 'life satisfaction' (Ambrey & Fleming, 2011). The enumeration and demarcation of factors contributing to life satisfaction is often arbitrary. Some researchers use a small number of relatively aggregated indicators (Gross Domestic Product is a well-known example of an aggregate indicator, in that it is a single number that captures information about a very large variety of factors); others use a very large number of indicators (Rojas, 2006a). There remains little certainty and no agreed rules for the operationalization of a life-satisfaction construct (Cummins, 1998; Hsieh, 2015; Rojas, 2006b); but much effort has sought to determine which indicators (i.e., what numbers or what type of data), from which domains are better for predicting life satisfaction.

The aim of this thesis is to test the life satisfaction approach in two case studies separately, my main objective being to identify ways of assessing and monitoring the contribution of the domains and types of indicators to people's life satisfaction in each case. I also specifically focused on the environmental domain, and the indicators that are being used. To achieve this aim I focused on three core questions:

RESEARCH QUESTION 1: Do some domains appear to contribute more to life satisfaction in developed countries than in developing countries?

RESEARCH QUESTION 2: Which indicators (objective and/or subjective) best represent which domains when measuring the contribution of different domains to life satisfaction in different socio-economic contexts?

RESEARCH QUESTION 3: Do environmental factors, other than those 'normally' considered (such as those relating to climate and pollution) contribute to life satisfaction?

The case study sites used include Costa Rica and the Northern Territory and outback Queensland in Australia (referred to as Northern Australia). In Costa Rica, I collected primary data from a sample of residents. I designed my own questionnaire to collect data about overall life satisfaction and about contributors to life satisfaction. Following previous literature I included questions about five life domains relating to: society, economy, the environment, health and safety. I then asked a series of questions designed to gather both 'subjective' and 'objective' information about each of the five life domains. I also collected some background information on income and occupational status plus other sociodemographic factors known to influence life satisfaction (including age, gender and education). Where-ever possible, I endeavoured to collect 'matching' subjective and objective indicators for variables (e.g. satisfaction with, and actual time spent with family).

For the case study in Northern Australia I used sub-set of secondary data from a cross-sectional survey of land managers (gathered as part of a research project funded by the Australian Government's National Environmental Research Project (NERP)). The data provided from this project included subjective information regarding the perceptions of land managers about their overall life satisfaction and additional objective and subjective indicators across the social and economic domains, and a subjective indicator from the environmental domain. Recognising that the environment may also be important to land managers for non-productive purposes, I thus also compiled additional information relating to aquatic biodiversity data from other resources, in addition to other biophysical information about vegetation type, soil type and places of interest (e.g. national heritage places, wetlands of national or international significance).

I found evidence to suggest that the economic domain is probably the most important domain for Costa Rican residents – at least some variables from this domain were statistically significant for the entire sample and for each sub-sample that I tested. Regarding the type of indicators from each domain, both subjective and objective indicators had a statistically significant relationship with measures of overall life satisfaction; but the type of indicators that were relevant for each domain were different. It was a subjective (rather than objective) indicator of satisfaction with housing (mostly associated with the economic domain) that had a positive association with life satisfaction for Costa Rican residents. But for the health domain, it was the objective (rather than the subjective) indicator – specifically, time spent exercising – that had a positive association with life satisfaction. Only within one sub-sample (employed persons living in an urban area adjacent to beaches and/or protected areas), did an environmental indicator – in this case, frequency of interaction with the environment – have a positive association with life satisfaction.

My analysis of land managers in Northern Australia also demonstrated that life satisfaction depends on multiple domains and that, using both subjective and objective indicators adds value to the analysis. In this case, the social domain had the strongest statistical association with life satisfaction: the single most important indicator of land managers' life satisfaction was having good relationships with family and friends. In contrast to the Costa Rican case, I did not find a statistically significant relationship between the economic domain indicators and life satisfaction.

Different people in different places value different things, according to my study. GDP alone is not a good indicator of life satisfaction; other indicators should be considered. My research demonstrates that there is a need to monitor multiple domains (including, at minimum, those from the social, economic, environmental and probably also health and safety domains), using both objective and subjective indicators. My research also demonstrates that one can expect different indicators to 'matter' at different stages of development of a country. If governments lack the resources to monitor a large variety of indicators, it may be possible to, at the very least, include a single question about overall life satisfaction within their regular censuses, thus readily monitoring more than mere GDP, in a cost-effective way.

Item ID: 49873
Item Type: Thesis (Masters (Research))
Keywords: Costa Rica, environmental factors, financial incentives, intrinsic motivators, life satisfaction, Northern Australia, on-farm conservation, social relationships, socio-economic contexts
Additional Information:

Publications arising from this thesis are available from the Related URLs field. The publications are:

Chapter 4: Chacón, Adriana, Stoeckl, Natalie, Jarvis, Diane, and Pressey, Bob (2016) Using insights about key factors impacting 'quality of life' to inform effective on-farm conservation programs: a case study in Northern Australia. Australasian Journal of Environmental Management, 23 (4). pp. 338-355.

Related URLs:
Date Deposited: 15 Aug 2017 04:56
FoR Codes: 14 ECONOMICS > 1402 Applied Economics > 140219 Welfare Economics @ 45%
14 ECONOMICS > 1402 Applied Economics > 140205 Environment and Resource Economics @ 35%
14 ECONOMICS > 1402 Applied Economics > 140202 Economic Development and Growth @ 20%
SEO Codes: 91 ECONOMIC FRAMEWORK > 9102 Microeconomics > 910209 Preference, Behaviour and Welfare @ 45%
91 ECONOMIC FRAMEWORK > 9199 Other Economic Framework > 919902 Ecological Economics @ 35%
91 ECONOMIC FRAMEWORK > 9102 Microeconomics > 910201 Consumption @ 20%
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