Governments around the world have started to investigate alternative methods for measuring “success” besides just GNP. One of these methods is Gross National Happiness, which measures quality of life or social progress in more holistic terms than GDP (http://en.wikipedia.org/wiki/Gross_national_happiness)
Team HappinessMatters embarked with the idea of applying this global concept at the Local Government Association (LGA) level. In other words, is the area where you live happier than the other areas in Australia?
Our first task was to establish “what is happiness”. Instead of just making assumptions what quantitative elements make up happiness, or generic surveys asking “are you happy”, we used the research from the paper “Factors Predicting the Subjective Well-Being of Nations” (Diener et al, 1995). From this paper we isolated the variables with the highest significance and located data to support a possible model.
The Happiness Index is rooted in statistics, which means it doesn’t just display data, but displays the index as a result of modelling multiple predictors. The usable output is a map depicting the happiness index per LGA.
Notable interests in the data analysis include:
– Finding the income growth mean per LGA (using the formula to calculate GDP)
– Comparing income levels spatially between neighbouring LGAs. In other words, compare the income level of every LGA to average income levels of the surrounding LGAs.
– Analysis of income inequality based on the Gini coefficient of income levels within the LGA
The predictors we used and their associated data sets:
Complaint Statistics (Rights measure): http://humanrights.gov.au/about/publications/annual_reports/2009_2010/complaint-statistics.html
National Regional Profile (Money, Growth, Neighbouring LGA’s Income)
Income Distribution from Basic Community Profiles, from the 2006 Census Data (Income Gini)
Local Government Areas ASGS Non ABS Structures Ed 2011
Consumer Price Index
Tools used: QGIS, R, Microsoft Excel, Google Fusion Tables, PostGIS, and Python
Intermediate data and most of the scripts used to prepare the data are available on Bitbucket. During the data-gathering phase, we considered many other sets of data that we didn’t end up using in our analysis.